quantification of urban freight transport effects i · 2013-11-06 · 2.3.1 task 5.1 urban freight...

TREN/04/FP6TR/S07.31723/506384

BESTUFS II

Best Urban Freight Solutions II Co-ordination Action Priority 1.6.2 Sustainable Surface Transport

Quantification of Urban Freight Transport Effects I

Due Date of deliverable: September 2006 Actual submission date: 10 October 2006

Start date of the project: Sept. 2004 Duration: 48 months

Main Authors: Jarl Schoemaker (NEA), Julian Allen (UoW), Marcel Huschebeck (PTV), Janos Monigl (Transman)

Revision [final] Project co-funded by the European Commission within the Sixth Framework Programme

(2002-2006) Dissemination level

PU Public X PP Restricted to other programme participants (including the Commission Services) RE Restricted to a group specified by the consortium (including the Commission Services) CO Confidential, only for members of the consortium (including the Commission Services)

D 5.1 Quantification of effects I

CONTENTS page

1 INTRODUCTION.................................................................................1

2 APPROACH........................................................................................2

2.1 Introduction .....................................................................................................2 2.2 Background / problem definition ....................................................................2 2.3 Working structure............................................................................................3 2.3.1 Task 5.1 Urban freight context: urban freight transport in

the economy and society .................................................................................3 2.3.2 Task 5.2 Contribution of single themes to EC objectives ...............................3 2.4 Methodology ...................................................................................................4 2.5 Definition, system relations and effects in city logistics.................................6 2.6 Indicators.........................................................................................................8

3 FREIGHT VOLUMES AND COMMODITIES IN URBAN AREAS ....10

3.1 Transport demand / volumes .........................................................................10 3.2 Logistics ........................................................................................................12 3.2.1 Goods receivers .............................................................................................13 3.2.2 Logistics costs ...............................................................................................13 3.2.3 Share of urban transport costs compared to total supply chain .....................15 3.3 Population .....................................................................................................16 3.3.1 Population density and share of population in urbanized areas ....................16 3.4 Household size ..............................................................................................18

4 URBAN FREIGHT TRANSPORT FLEET.........................................20

4.1 Freight vehicles .............................................................................................20 4.1.1 Number of vehicles according to GVW and age...........................................20 4.1.2 Proportion of goods vehicles in total traffic..................................................21 4.1.3 Ownership of vehicles...................................................................................23 4.1.4 Vehicles operating in cities ...........................................................................25 4.2 Urban freight traffic flows.............................................................................26 4.2.1 Number of vehicles entering cities................................................................26 4.2.2 Distribution of freight vehicles movements over day ...................................28 4.3 Service visits and waste collection................................................................31 4.3.1 Service visits .................................................................................................31 4.3.2 Waste collection ............................................................................................32 4.4 Performance ..................................................................................................33 4.4.1 Freight vehicle kilometers .............................................................................33 4.4.2 Use of load capacity ......................................................................................35

5 URBAN DELIVERIES.......................................................................38


5.1 General delivery characteristics (operators) ................................................. 38 5.1.1 Combined shipments .................................................................................... 39 5.1.2 Delivery days and times ............................................................................... 39 5.1.3 Regularity of trips......................................................................................... 42 5.1.4 Origin of delivery trips ................................................................................. 42 5.1.5 Number of stops per tour / per day ............................................................... 43 5.1.6 Trip length .................................................................................................... 44 5.1.7 Distance between stops................................................................................. 45 5.1.8 Trip times ..................................................................................................... 46 5.1.9 Travel time to and within city centre............................................................ 46 5.2 General delivery characteristics (receivers).................................................. 48 5.2.1 Deliveries at premises................................................................................... 48 5.2.2 Dwelling time in urban area / loading and unloading times......................... 48 5.3 Home deliveries............................................................................................ 50 5.3.1 Home delivery offered by shops................................................................... 50 5.3.2 Number of km covered per inhabitant .......................................................... 50

6 ECONOMY........................................................................................52

6.1 Employment, % in transport and logistics.................................................... 52

7 ENVIRONMENT................................................................................54

7.1 Energy use .................................................................................................... 54 7.1.1 Typical fuel consumption by vehicle type.................................................... 54 7.1.2 Energy consumption in urban freight transport ............................................ 55 7.1.3 Number of alternatively propelled or fuelled distribution vehicles.............. 56 7.2 Exhaust emissions ........................................................................................ 57 7.2.1 Typical emission factors by vehicle type ..................................................... 58 7.2.2 Share of urban freight transport in exhaust emissions.................................. 59 7.3 Traffic, energy and emission ratios per inhabitant and per job .................... 62 7.4 Noise............................................................................................................. 62 7.4.1 Noise levels driving truck............................................................................. 62 7.4.2 Noise levels loading and unloading truck..................................................... 64

8 SAFETY ............................................................................................65

8.1 Fatalities, accidents and casualties in urban freight transport ...................... 65 8.2 Involvement of freight vehicles in accidents ................................................ 66 8.3 Road user type: cyclists, pedestrians, car drivers ......................................... 69

SOURCES AND REFERENCE LIST............................................................................71

Authorities, companies and institutes.......................................................................................... 71 Presentations at BESTUFS workshops and conferences............................................................. 71 Reports and websites ................................................................................................................... 72


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1 INTRODUCTION

This document D5.1 is the first Deliverable BESTUFS II project Work Package 5 “Quantification of Effects”. The objective of the Work Package is: (1) Quantifying urban freight transport in the context of all (urban) traffic, and (2) Quantifying the contributions of measures to EC policy objectives. This document relates to the first part of the objective. The second objective will be covered in the second deliverable, D5.2. In this document the contributions of measures to policy objectives will be developed into estimates of average impacts of typical best practice scenarios. The second deliverable will include an update of the data presented in D5.1. This document contains a compilation of data published previously, which data has been collected using the BESTUFS network. As this data stems from various sources and periods it is very difficult to achieve the maximum level of accuracy and completeness. Careful consideration has been applied with each piece of information integrated into this report, however. Continuous improvement and updating is foreseen during the remaining last two years of the project. The results of WP5 contribute to the policy recommendations in WP1 and to the guides that are developed in WP4. WP5 is linked with WP3 (Modelling of urban commercial transport) in the sense that WP3 aims at harmonizing data collection and at modelling, and WP5 aims at collection the available data.


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2 APPROACH

2.1 Introduction

This chapter describes the approach taken to quantify urban freight, its external effects and the contribution of single themes to EU objectives. In the section “Background / problem definition”, the relevance of the quantification and the reasons for the current lack of data are described. The section “Working structure” identifies the specific activities of the two identified tasks. The section “Methodology” describes the methods that are used for the data collection and analysis. The section “Definition and introduction of Urban Freight Transport” defines the scope and system boundaries of the task, identifies the actors, relations and processes and identifies the external effects that will be covered.

2.2 Background / problem definition

Quantitative information on urban freight transport is not widely available, especially not on European level. This means the size of the urban freight transport is unknown on many levels. A reasonable amount of data is available, but usually on very specific areas and often only in the local language. Another major problem is that urban freight is not considered as a first priority problem in most countries. On the other hand, national governments consider urban freight transport to be a local problem, which means cities are responsible for dealing with urban freight. Most cities, except some very large ones and some with specific concerns, don’t have the capacity to evaluate the highly complex urban freight situation. This means that the effects of measures are not widely understood and its contribution to the economy, environmental problems and society are not known. This report aims to provide a useful, inspiring document for anyone interested in obtaining a first urban freight transport data set and references to more specific information. At the time of finalizing this report, a number of data collection projects are being carried out in several countries. Not all results are currently available, but these will be integrated in Deliverable D5.2, which will be the result of Task 5.2.


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2.3 Working structure

2.3.1 Task 5.1 Urban freight context: urban freight transport in the economy and society

The objective of Task 5.1 is to quantify the urban freight and to compare it with other modes and sectors of transport. Economic, environmental and social/safety issues of urban freight transport, including external cost aspects will be taken into account. Within Task 5.1, the following steps are taken: • Scoping: definition of urban freight actors and relations (common terminology) • Identifying data sources (statistics and case studies) • Identifying long list of indicators • Selection of relevant indicators that can actually be used • Data collection and structuring (statistics and case studies) • Workshop for validating approach and results and to obtain additional data • Reporting

2.3.2 Task 5.2 Contribution of single themes to EC objectives

The objective of Task 5.2 is to determine the contributions of single best practice themes or city logistics solutions to EC objectives. The results will be considered at city level and possibly extrapolated to the EU-25. Within Task 5.2 the following steps are taken: • Identification of relevant policy objectives (White Paper and others) • Identification of Best Practice Themes and City Logistics Solutions • Selection of most relevant indicators from Task 5.1 • Obtaining data from Task 5.1 (statistics and case studies) • Workshop for validating approach and results and to obtain additional data • Analysis:

- Quantifying of effects of individual Best Practice Themes and City Logistics Solutions

- Assessment for whole EU 25 • Reporting


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2.4 Methodology

The set up of an urban freight context description is an iterative process, see Figure 2.1. Based on an initial analysis of the effects of urban freight transport on the EU objectives, indicator categories have been identified. Within each indicator category a number of indicators has been developed and an initial data set has been collected. The structured data set has been compared to the contribution to objectives, which led to refinement of the indicators categories and indicators. With iterative approach, the “picture” of the urban freight context improves when more information gets available. Although Task 5.1 is finished, this iterative process will be continued up to the end of the project and documented in D5.2 (e.g. by presentations on workshops and conferences in the following WP5 activities). Figure 2.1 Iterative approach

Data collection The statistical data and case studies are collected from several sources: • Statistical offices

- European statistics - National statistics

• Research projects - European - National - Municipalities - Associations - Individual companies

• Urban freight platforms • Case study reports • Traffic studies • Discussions with representatives during workshops Analysis The following analyses have been carried out:

Contribution to objectives

Indicator category

Indicators

Indicator values by examples


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• Availability of urban freight transport figures from European and national statistics • Comparability of values • Structuring case studies And within Task 5.2: • Quantifying the effects of individual best practice themes • Assessment for EU-25 Workshops / expert meetings Two workshops will be organized addressing the quantification of contributions of urban freight to EC policy objectives. The workshops will address qualitative and quantitative issues of urban freight and EC policy objectives. The aim is to have discussions with policy makers and industry experts. The workshops will be used to validate the approach and methodology and to review the results. The first workshop took place in Barcelona on July 13th 2006. The workshop was used to validate the approach of the report and to obtain more data. The workshop was set up as a highly interactive working meeting. Each presentation was followed by in-depth discussions. A small number of experts was invited specifically for the workshop. Experts from the following companies and authorities participated in the workshop: • Regione Emilia-Romagna • RAPP Trans LTD • MVA Ltd • Gérardin Conseil • Transman Consulting for Transport System Management Ltd. • Peter Brett Associates • Transport for London • Freight Transport Association (UK) • PTV Planung Transport Verkehr AG • NEA Transport research and training The main issues addressed in the workshop were: • Are the right indicators addressed - do the indicators cover the urban freight transport

system: - Are we thorough, are there crucial indicator missing? - What are your experiences on indicators used in policy making?

• What is the relevance of the indicators to support policy statements and actions? • Are there additional data sources to improve our estimations? • How to streamline the report optimally to the users’ benefit (text or tables, concise or

precise etc.)? After the workshop the participants were asked to fill out a “final statement” document. In this document the participants could state their preferences regarding the report characteristics,


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indicators, data sources and overall comments. Most of the participants have filled out and returned the document. The workshop has led to the following conclusions: • An easily accessible document on urban freight figures on EU level targeted at urban policy

makers currently does not exist, but would be welcomed by many municipalities as data is an important topic for urban freight planning.

• The report should be concise, but references to a larger data set, either available as an Annex or on a website would be valuable.

• Many indicators are possible. For the current document a selection has been made. This structure was agreed upon by the participants, but additional suggestions were made. – The indicators will be reviewed in the iterative process of 5.2 and complemented or adapted if considered beneficial.

• New data sets are currently collected in the UK and in Switzerland. - This data will be integrated in D5.2 when available.

2.5 Definition, system relations and effects in city logistics

In Europe about 80% of the population is living in urban areas and the economy and industrial production is also concentrated on urban areas. This leads to a high potential for urban freight transport. Due to the high density of settlement within urban areas and the limited space and infrastructural resources, and existing environmental restraints urban freight transport has to cope with many difficulties. By definition logistics is that part of the supply chain process that plans, implements, and controls the efficient, effective flow and storage of goods, services, and related information from the point of origin to the point of consumption in order to meet customers’ requirements”1,2 With this, transport is a part of logistics, with respect to logistical processes of acquisition and distribution of goods. City logistics incorporates a row of activities – resulting in complicated relationships – between different actors, from production, commerce and supply of different clients and inhabitants, which appears in form of inner urban goods transport, or distribution of interurban freights, fulfilling a substantial contribution to economy, city life and operation (see Figure 2.2). The frame for city logistics is given by local and regional economy, the transport infrastructure, the surrounding environment, legal and regulatory conditions.

1 CLM Definition for Logistic Council of Logistics Management, http://www.clm.1.org, OAk Brook 2001 2 Inner urban freight transport and city logistics, EU-funded Urban Transport Research Project Results 2003 www.eu-portal.net


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The main actors in city logistics are the clients, shippers and receivers with transportation needs to and from their locations to other places. The transportation companies forwarding the goods on the transport network often cause delays for other road users. The congestions caused by trucks, vans and hindrances by loading goods vehicles, are often substantially negatively contributing to air pollution, noise and vibrations in sensitive living areas. The accidents of trucks and vans have often serious consequences on human life and damage to goods. The task of transport system management is to ensure the conditions of urban goods transport and supply in an optimal way, which means the minimising the inner costs and the external costs of transport, together the social costs to the community. From this aspect the cities are better off, where urban goods transport is organised, similarly to public transport, which canalizes the flows of passengers, in contrast to the individual car traffic. This needs that also urban goods transport is organised and partly restricted by space and time and transport means to reduce the negative effects of goods transport. A logistic distribution centre can be able to improve the conditions, helping the good flows between the different clients also by different information technology and coordination methods. The municipal government should be the main initiator of this kind of centre and should, with adequate and targeted regulations and incentives, provide positive signs and circumstances for better city logistics. In the creation and operation of the centres by incentives of the public (e.g. provision of land for a centre) the private investors and capital can play an important role and the whole process should have rather a commercial character. The logistics and distribution centre can overtake also storage functions from expensive inner city areas to reduce costs for the clients. The negative impacts on transport, economy and living cause in some cases serious problems in the life of a city. The main task of the experts of city logistics is after analysing the problems to set policy goals and measures, which should improve the conditions and the different impacts of the urban good transport and city logistics. Among other measures the creation of a distribution centre, the change and improvement of the vehicle fleet, canalising the transport routes, dedication of time-windows and loading bays for transport can be efficient tools in improvement of the conditions. Figure 2.2 shows the urban freight system’s main actors (municipality, system operator, transporters, clients, etc.). The activities of the actors cause at the same time different effects on others and the actors have to bear also different other effects. To investigate the goods transport volumes and their spatial and modal distribution data collections, planning models and evaluation methods are needed which allow in advance the estimation the effects of different measures and the efficiency of the system interventions.


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The modelling of urban freight is mainly related to questions of operational planning (e.g. tour planning) or of strategic planning in connection with city development and improving measures (e.g. demands, flows and consequence impacts, evaluations). To provide sufficient data for comparison across Europe about the processes and deciding factors in city logistics is the main task of WP 5.1. The structuring and presentation of the collected data tries to follow the main logic of Figure 2.2, showing the complexity of city logistics and their different influencing factors. It is difficult to present data in a holistic way because, systematic data collections and statistics are rare and different from country to country and from city to city. Therefore one can only await a kind of “data mosaics” from different places, small, medium size and large cities, which can be used in planning and organisation of goods transport and city logistics for comparisons.

Figure 2.2 Urban freight system approach

Source: Transman

2.6 Indicators

This report contains a quantitative view of effect of urban freight transport in six categories. For each impact category, a number of indicator categories are defined. Each category contains a number of indicators.


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Table 2-1 Urban freight indicators by impact category

Impact category Indicator category Indicator Transport demand − Volumes transported into urban areas Logistics − Goods receivers

− Logistics costs − Share of urban transport costs compared to total

supply chain − Salaries in urban freight transport

Freight volumes and commodities in urban areas

Population − Population density and share of population in urbanized areas

− Household size Freight vehicles − Number of vehicles according to GVW and age

− Proportion of goods vehicles in total traffic − Ownership of vehicles − Vehicles operating in cities

Urban traffic flow − Number of vehicles entering cities − Distribution of freight vehicles movements over

day Service visits and waste collections

− Service visits − Waste collection

Urban freight transport fleet

Performance − Freight vehicles kilometers − Use of load capacity

General delivery characteristics (operators)

− Combined shipments − Delivery days and times − Regularity of trips − Origin of delivery trips − Number of stops per tour, per day − Trip length − Distance between stops − Trip times − Travel time to and within city centre

General delivery characteristics (receivers)

− Deliveries at premises − Dwelling time in urban area / loading and

unloading times

Urban Deliveries

Home delivery − Home delivery services offered by shops − Number of km covered by inhabitant

Contribution to economy

Employment, % in transport and logistics

− Number of jobs in transport − Number of transport related companies

Energy use − Typical fuel consumption by vehicle type − Energy consumption in urban freight transport − Consumption of non-renewable fuel resources

Exhaust emissions − Typical emission factors by vehicle type − Share of urban freight in exhaust emissions

Environment

Noise − Noise levels driving truck − Noise levels loading/unloading truck

Accidents and casualties in urban freight transport

− Number of accidents − Number of fatalities − Involvement of freight vehicles in accidents − Weekly distribution of accidents involving HGVs

Safety

Road user type − Cyclists − Pedestrians − Car drivers


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3 FREIGHT VOLUMES AND COMMODITIES IN URBAN AREAS

For optimizing the road system it is informative to know the total demand of (freight) transport using the road system. In order to take effective measures to prevent traffic problems a municipality needs information about the traffic volumes and traffic flows.

3.1 Transport demand / volumes

Total volumes The transport demand is the expression of the transport needs determined by the amount of goods transported into the urban areas. Also for some cities it is clear that flows through a port may be counted and yet this is not really urban freight in some senses. The total amount of freight is related to the city size. Table 3-1 shows volumes of goods transported into four cities and their population sizes. It is not possible however to determine the volume per inhabitant as the volume might include consumer goods as well as industrial products and building materials. This is only possible if the volumes and inhabitants refer to the same study area.

Table 3-1 Freight transport (1.000 tonnes) and number of inhabitants in four European cities

Indicator London3 Dublin Amsterdam Rotterdam Volumes 122.277 72.834 29.834 55.701 Inhabitants 7,9 mln. 1 mln. 736.045 599.544

Source: DfT 2004, Road freight transport survey 2004; central statistics office (CSO); www.rws.avv.nl/goederenvervoer2003/index

Volumes in, out and inside cities It is important to realize that a significant amount of urban freight transport actually area goods that are transported within the city, especially in larger urban areas. Table 3-2 shows the amount of road freight in Dublin and London. In both cities, the majority of freight is transported within the city.

Table 3-2 Amount of road freight (in 1,000 tons) with origin/destination Dublin (2004) and London

Source: Road Freight Transport Survey 2004; Central Statistics Office (CSO), Table 16, DFT (2004)

In some cities, very specific data is collected on the actual origins and destinations of goods vehicle trips. Figure 3.1 shows these figures for London where a large amount of goods trips have their origin and destination within the city. 70% of the goods entering London originate from the East or South East (109,200 tons) and 30% from elsewhere in the UK. The same balance shows for goods exiting London. Lifted by British HGVs with an origin or destination in

3 Only goods transported by vehicles over 3.5 tonnes gross weight are included.

Deliveries Dublin Share London Share Out of the city 19,082 26.2% 50,000 25% Into the city 21,105 29.0% 66,000 33% Within the city 32,647 44.8% 84,000 42% Total 72,834 100.0% 200,000 100%


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London 123 million tons are transported. This constitutes of 51 million lifted within London, 41 million entering London and 31 million leaving London.

Figure 3.1 Origins and destinations of freight transport in London

Source: TfL,(based of DfT 2004 data)

Figure 3.2 shows an increase in freight delivered into London, a very small increase in freight delivered from (or out of) London and a decline in deliveries within London.

Figure 3.2 Development of London road freight

Source: TfL


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Table 3-3 shows tons transported in four Norwegian cities. The column “internal” means trips with both start and end within the city (the municipal). The column “county” is to or from the surrounding region. The “rest” are to or from outside the county – including import and export.

Table 3-3 Tons transported in Norwegian cities (extract from 16 cities)

1993-1999 Vehicles < 3.5 tonnes Tonnes per citizen per year

Vehicles > 3.5 tonnes Tonnes per citizen per year

Cities Internal County Rest Total Internal County Rest Total Frederiksstad 2.3 0.9 0.2 3.3 23.0 20.3 19.9 63.2 Oslo 2.1 0.8 0.2 3.1 24.2 13.1 20.8 58.1 Bergen 2.1 0.4 0 2.5 22.2 7.8 5.3 35.4 Tromso 2.0 0.1 0 2.1 27.9 4.6 3.7 36.3 Total 1.9 0.8 0.2 2.9 26.9 15.3 16.4 58.6

Source: Toi-report 737/2004

3.2 Logistics

It is already stated in section 2.5 that logistics is that part of the supply chain process that plans, implements and controls the efficient, effective flow and storage of goods, services and related information from the point of origin to the point of consumption in order to meet customers’ requirements. A part of this is city logistics. City logistics incorporates a row of activities between different actors, from production, commerce and supply of different clients and inhabitants which appears in form of inner urban goods transport, or distribution of interurban freights. The next four subsections contain information of goods receivers, logistics costs, share of urban transport costs compared to total supply chain and salaries in transport.


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3.2.1 Goods receivers

Goods receivers are the demand side of the transport market. Their characteristics (size, type of goods delivered and frequency of delivery) determine the final demand for transport in the urban area. The goods receivers can be categorized in many forms. Usually the categorization is determined by the specific study purpose. The differences make comparisons complicated. This paragraph presents a few categorizations. Table 3-4 shows the categorization used for a study in Bologna. Table 3-5 shows which types of goods receivers are located in commercial areas in three cities in the Netherlands. In these cities a common data collection methodology has been used.

Table 3-4 Type of goods receivers in the city of Bologna (2004)

Goods receiver Bologna Hotel, restaurant and catering 35% Retail business food 14% Retail business “non food” 39% Clothes shop 12% Total 100%

Source: Regione Emilia Romagna (2004)

Table 3-5 Type of goods receivers in three areas in the Netherlands (share of number of businesses)

Goods receiver Amsterdam Rotterdam Utrecht Services and institutions 48% 47% 66% Catering and entertainment industry 12% 12% 9% Retail business 10% 10% 8% Clothes shop 5% 16% 8% Equipment and home furnishing 4% 4% 4% Convenience grocer 4% 4% 2% Supermarket and warehouses 0% 1% 0% Other 16% 6% 4%

Source: Dataverzameling Stedelijke Distributie

3.2.2 Logistics costs

Figure 3.3 shows that all important aspects of supply chain costs have decreased consistently over the past 20 years. The share of logistic costs has decreased from 12.1% to 6.1% of sales. The transportation share has decreased from 5.9% to 2.6%. However, in recent years cost reductions have been stagnating.

Figure 3.3 Development of logistic cost


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Development of logistic cost (% of sales)

0%

2%

4%

6%

8%

10%

12%

14%

1987 1993 1998 2003

AdministrationInventoryWarehousingTransportationTransport packaging

Source: Differentiation for performance excellence in logistics 2004 (European Logistics Association, ATKearney, 2004)

Figure 3.4 shows the logistics costs by country.

Figure 3.4 Logistics costs by country

Source: Institute of Logistics and DELOITTE and TOUCHE CONSULTING GROUP, 1998


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3.2.3 Share of urban transport costs compared to total supply chain

Although urban freight transport constitutes only a very small proportion in the total freight transport length, it invokes a high proportion of the transport costs. According to the Council of Logistics Management, this “last mile” in the transport chain accounts for 28 percent of total transport costs. A study on the influence on delivery time windows in the Netherlands has determined the average costs for delivering a mesh container for shops in general and shops in core shopping areas, but both the whole retail sector and the food retail sector (Table 3-6). The costs in the core shopping areas are higher because of access restrictions (delivery windows and vehicle restrictions). The share of distribution and transport costs in the turnover of the sector are shown in Table 3-7. The costs in the food retail sector are lower, because in the Netherlands the food retail sector is characterized by large supermarket chains capable of organising highly efficient logistics.

Table 3-6 Average delivery costs per mesh container (euro)

Retail sector All shops Core shopping area All retail € 6.20 € 8.50 Food € 4.80 € 7.50

Source: De invloed van venstertijden en voertuigbeperkingen op de distributiekosten in de Nederlandse detailhandel, TNO Inro, 2003

Table 3-7 Distribution and transport costs in the Netherlands (billion euro per year)

Indicator Total retail Share Food retail Share Turnover € 100.00 € 26.00 Total distribution costs € 6.60 6.6% € 1.56 6.0% Transport costs € 2.98 3.0% € 0.70 2.7% Costs of retail outlets € 1.15 € 0.36 Introduced costs (access restrictions etc. – 28%) € 0.43 € 0.10

Source: De invloed van venstertijden en voertuigbeperkingen op de distributiekosten in de Nederlandse detailhandel, TNO Inro, 2003

Exceptions to the rule can be observed, however. For instance, in urban areas in Italy, the 1ocal consignment costs are low, with a non-significant share on final selling prices (Ministry of Transport and Infrastructure, 2006). The reason lies in the fact that on average in urban areas about 80% of consignments are delivered by small individual “own transport” operators (padroncini) operating under sub-contract of MTO (Multi Transport Operator) and forwarders. The “padroncini” strongly compete against each other, lowering the urban logistic costs on final prices. It is not easy to assess the share of such costs on the overall supply chain. The national association of logistics enterprises (Asso Logistica) estimates the impacts of logistics distribution costs on final prices (CONFETRA, 2002).


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3.3 Population

Freight transport is strongly linked to the population characteristics: the population density and demographic characteristics influence the urban freight transport demand.

3.3.1 Population density and share of population in urbanized areas

The EU has a growing population (Table 3-8) and a high share of people living in urban areas: 80% of the population lives in urbanized areas see (Table 3-9). This means most consumer goods will have to go to urban areas.

Table 3-8 Development of EU population

Countries 1970 1980 1990 2000 2004 2005 EU25 406.9 426.1 438.6 451.8 457.2 459.5 EU15 340.0 354.6 363.7 378.0 383.1 385.4

Source: EUROSTAT, national sources

Table 3-9 Comparison EU-25 with the world in 2004

Indicator EU-25 USA Japan China Russia Population (million) 457.0 293.5 127.8 1292.3 142.8 Population growth (%) 0.8 0.9 0.1 0.6 -0.4 Urban population (%) 80 81 66 40 73 Area (million m2) 3.97 9.36 0.38 9.56 17.08 Population density (persons/km2) 115 31 336 135 8

Source: EUROSTAT, World Bank

In some countries more specific information is available on urban areas. For instance, in The Netherlands, over 40% of the inhabitants live in highly to very strongly urbanized areas, see Table 3-10. About 40% lives in slightly to medium urbanized areas and only 20% lives in non-urbanized areas.

Table 3-10 Living areas of inhabitants in the Netherlands

Area type The Netherlands Very strongly urbanized 19% Highly urbanized 23% Medium urbanized 18% Slightly urbanized 20% Non- urbanized 21% Total 100%

Source: CBS, national statistical bureau the Netherlands


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In most countries, the share of inhabitants in urban areas is increasing. Figure 3.5 shows that in the Netherlands from 39.9 percent in 1997, it had risen to 41.5 percent by the beginning of 2002. The percentage of the population living in rural areas decreased from 42.8 to 41.1 percent between 1997 and 2002.

Figure 3.5 Share of population in urban and rural environment

Source: CBS, national statistics bureau the Netherlands

The share of urban population is usually expressed by the number of people living in areas with predefined population densities. Table 3-11 shows population densities of Dublin and four cities in Germany. Table 3-12 shows the size of the municipality and the density per km2 for Italian cities. It must be noticed that the density per km2 is not (always) direct related to the number of inhabitants. For example: the size of Berlin is ten times the size of Augsburg but the density is only two times as high.

Table 3-11 Examples of characteristics of Dublin and three German cities

Category Indicator Dimension Year Dublin Aachen Augsburg Berlin Inhabitants Number 2002 952.692 Inhabitants Number 2003 256.605 259.217 3.388.477

Population

Population density Inhabitants per km2 2003 4.215 1.600 1.766 3.799

Source: 2002 Census of population, volume 1, population classified by area CSO, Die Initiative neue soziale Marktwirtschaft

Table 3-12 Population density in urban areas in Italy (general census, 2001)

Urbanisation level Inhabitants Resident population Share Density per km2 >250.000 ab. 9,103,091 16.0% 2,609.9

Highly urbanized 100.001 - 250.000 4,125,516 7.2% 695.5 50.001 - 100.000 6,390,014 11.2% 445.6

Urbanized 20.001 - 50.000 10,076,393 17.7% 376.0 Medium urbanized 10.001 - 20.000 8,669,117 15.2% 246.6

3.001 - 10.000 12,680,752 22.2% 131.0 1.001 - 3.000 4,849,390 8.5% 60.6

Non urban area Up to 1.000 ab. 1,101,471 1.9% 28.4

Total 56,995,744 100.0%

Source: ISTAT (2003)


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Urban is not very urban in Norway: a village with more than 200 inhabitants and not more than 50 metres between houses are “urban” following Norwegian statistical definition, see Table 3-13.

Table 3-13 Urban population in Norway in 2006

Indicator Population”Urban” areas 3,607,813Rural areas 1,016,736Undefined 15,670Total population 21,842Share of population in urban areas 78%Average household size (2001) 2.28

Source: Norway statistical bureau

3.4 Household size

The household size has a strong influence on the shopping behavior of people. A several person household has different shopping patterns than a single person household. The differences in demand volume will lead to specific transport needs in the city/region. Table 3.9 shows the average number of persons per private household. It has slightly decreased in the past years and is currently steady at 2.4. Figure 3.6 shows the development. Table 3.9 Average number of persons per private household4

1961 1971 1981 1991 2000 2001 2002 2003 EU (25 countries) 3.6 3.3 3.0 2.8 2.5 2.4 2.4 2.4 EU (15 countries) 2.6 2.4 2.4 2.4 2.4 The Netherlands 2.8 2.4 2.4 2.3 2.9 2.3 Italy 3.0 2.8 2.6 2.6 2.6 2.6 Spain 3.6 3.3 3.0 3.0 3.0 2.9 Finland 2.3 2.2 2.2 2.2 2.2 2.2 Great Britain 2.7 2.5 2.3 2.4 2.3 2.3 Hungary 2.8 2.6 2.6 2.6 2.6

Source: Eurostat, ISTAT (2005), European system of Social indicators (EUSI), social Indicators Department, ZUMA, Mannheim

4 Number of persons living in private households divided by the number of private households. Collective households such as boarding houses, halls of residence and hospitals and the persons living in them are excluded. Estimated values because the information is not available for each country in each year.


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Figure 3.6 Development of household size

Source: European Environmental Agency


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4 URBAN FREIGHT TRANSPORT FLEET

4.1 Freight vehicles

The composition of the transport fleet provides information about the number, age, sort (LGV/HGV) of vehicles present in the transport fleet within a country. To examine a policy measures an effective information about the transport fleet is needed.

4.1.1 Number of vehicles according to GVW and age

The number of registered freight vehicles in Europe is shown in Table 4-1. Between 1990 and 2003 the number of light goods vehicles has increased by 15%. The number of heavy goods vehicles by 6.6%. This reflects the increased use of LGVs (and especially vans) in urban freight transport. The table does not specify the share of vehicles active in urban freight, however. Table 4-1 Vehicle registrations per year in EU-15 (* 1,000) Vehicle type 1990 2002 2003 1990-2003 Light goods vehicles (<3.5t) 1,474.1 1,741.3 1,695.7 +15.0% Heavy goods vehicles (>3.5t) 290.0 316.6 309.3 +6.6%

Source: EUROSTAT

The year of manufacturing provides information about the environmental class of the vehicle fleet in a country. Older trucks are more harmful for the environment than recently produced trucks. The road freight transport survey in Dublin showed that 49% of the transport fleet in Dublin is not older than four years, see Table 4-2. The city of Dublin counts relatively many vehicles built before 1994, but the number of vehicle kilometers made by these old vehicles is relatively small. Table 4-2 Performance of vehicle fleet in Dublin by age category Year of manufacturing

Number of vehicles

share Tonne kilometres (million)

share Tonnes Carried (thousand)

share Vehicle kilometres (million)

share

1994 or before 20,338 24.0% 1,266 7.3% 32,519 11.5% 222 9.5% 1995-1996 11,707 13.8% 2,034 11.8% 35,715 12.6% 282 12.0% 1997-1998 11,351 13.4% 2,546 14.7% 43,627 15.4% 345 14.7% 1999-2000 15,274 18.0% 4,322 25.0% 71,035 25.1% 552 23.6% 2001-2002 13,395 15.8% 3,462 20.0% 51,626 18.2% 469 20.0% 2003-2004 12,682 15.0% 3,659 21.2% 48,812 17.2% 472 20.2%

Ref. Road Freight Transport Survey, 2004, CSO

For the Netherlands comparable shares can be noticed. 40% of the Dutch LGV and HGV transport fleet was older than six years in the year 2003, see Table 4-3.


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Table 4-3 Age of vehicle fleet in the Netherlands (2003) Year of manufacturing LGVs (< 3.5 t ) Share HGVs (> 3.5 t) Share 1991 or before 164,875 15.9% 112,096 13.4% 1992-1996 257,699 24.8% 210,454 25.2% 1997-2002 615,999 59.3% 513,294 61.4%

Source: TLN, Transport in cijfers, editie 2003

The environmental class of the vehicle fleet is directly related to the year of manufacturing. Vehicles produced before 1997 do not conform to the minimal European EURO-norm 2. Relates to almost 54% of the total vehicle fleet in Lombardia. Table 4-4 Age of vehicle fleet in Lombardia (2006) Year of manufacturing Environmental class Number Share

1980-1987 Pre Euro 0 70,000 17,07% 1988-1993 Euro 0 95,000 23,17% 1994-1996 Euro 1 55,000 13,41% 1997-2001 Euro 2 130,000 31,71% 2002-2003 Euro 3 60,000 14,63% Total 410,000 100% Source : Ministry of Transport and Infrastructure, Italy

Table 4-5 provides information about the typical fuel types used by the vehicle fleet in Italy. The number only considers domestic vehicles officially registered in the national public register. Information by vehicle type (LDV and HGV) and fuel type has been estimated on the basis of 1999 data. An estimate of the age of vehicle fleet of diesel freight vehicle over 2.5 ton has been provided by the Pirelli company (Pirelli Ambiente Eco Technology S.p.a, 2006) with reference to the Lombardia region (410,000 circulating trucks). At national level it is likely to be expected no particular differences from the Lombardia region sample. A relatively great amount of petrol LGV vehicles can be noticed. Table 4-5 Freight vehicle fleet Italy (2004) Vehicle type Fuel type Vehicles Share

Diese l 2,423,637 63,03%LGV Petrol 431,662 11,23%

Diesel 977,344 25,42%HGV Petrol 12,730 0,33%

Source : Ministry of Transport and Infrastructure, Italy

In Norway, the proportion of goods traffic of total traffic (vehicle-km) is 12% on national basis. Figures for cities are not available. By the end of 2004 Norway had 284 000 vans (total weight less than 3.5 tonnes) and 82 000 trucks (total weight from 3500 tonnes and more).

4.1.2 Proportion of goods vehicles in total traffic

The proportion of goods vehicles in all traffic gives an indication of the impacts of (urban) freight transport on the city’s road network. Information about the amount of traffic using the


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road system is collected via continuous or periodical traffic counts, either by measuring equipment in the roads or by visual counting.

Figure 4.1 shows the share of freight traffic as a percentage of all motor vehicles in London. The HGV share ranges between 2 and 8% and the LGV share ranges between 8 and 18%. The shares grow between 8 and 10AM and slowly decrease until 8PM. The shares are lower on the non-TRLN (Transport for London Road Network) roads.

Figure 4.1 Proportion of goods vehicles in total traffic

In Italy the proportion of urban traffic is approximately 18% in terms of total traffic (in vehicle kilometer), see Table 4-6.

Table 4-6 Proportion of goods vehicles in total traffic (million vehicle kilometers) in Italy (1999)

Vehicle type Total traffic Urban share Urban trafficLGV 38,384 25% 9,596HGV 37,594 12% 4,511Total freight 75,978 18% 14,107

Source: FFSS Amici della Terra (2002)

Table 4-7 shows the share of urban freight in trips, vehicles kilometers and vehicles kilometers expressed in passenger car units. Freight traffic has a significant share in the total urban traffic expressed in all variables.


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Table 4-7 Share of Urban Freight Traffic in the total traffic in France Indicator France Vehicle trips 9 to 15% Vehicle kms 13 to 20% Vehicle kms (car unit) 15 to 25%

Source: LET, UGM surveys Bordeaux, Marseille, Dijon

Traffic counts in Budapest on the percentage of the stay in the city area compared to the annual mileage were made showing that about 57% of the fleet operating in cities have an annual mileage of less than 50.000 km a year. Table 4-8 Urban distribution transport characteristics Budapest: yearly running performance per vehicle (km/year) Running performance Within the city Transit runs < 30.000 29,1% 18,1% < 50.000 27,8% 24,8% < 80.000 20,7% 26,6% < 120.000 13,3% 15,5% < 200.000 5,4% 10,2% > 200.000 3,8% 4,8%

Source: Complete road traffic survey of Budapest and the agglomeration, 1999

4.1.3 Ownership of vehicles

The ownership of the vehicles as indicator provides information about the way the vehicles will be used regarding multi-drop trips and combined shipments. The ownership is vehicles is published in (national) statistics, but information about the ownership of vehicles actually active in urban freight has to be determined in urban freight surveys. For Great Britain since 1995, freight moved by public vehicles has increased by 3 per cent and by own account operators by 15 percent, with public haulage accounting for about three quarters of freight moved in recent years. Between 2004 and 2005, freight moved by own account operators increased by four percent whilst freight moved by public vehicles decreased by 1 per cent slightly increasing the share of all goods lifted by own account haulage to 28 percent, see Figure 4.2. Figure 4.2 Goods lifted by mode of working in Great-Britain: 1980-2005


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Source: Department for transport, transport statistics Great Britain, 2005

The complete road traffic survey of Budapest shows the amount of privately and self owned vehicles for Budapest, see Table 4.7. In Budapest 61% of the trips is carried out by private operators. Table 4.7 Urban distribution transport characteristics Budapest: owner type Owner type Within the city Transit runs Self 13,7% 11,5% Sole proprietor 21,0% 23,9% Private comp. 57,2% 60,6% State owned company 5,5% 1,8% Other 2,6% 2,2%


In “Confederation of Logistics and Freight Transport” (CONFETRA, 2002) is quoted an analysis of road freight operators and number of vehicles in own account and on account of third parties (the analysis is related to the mid-1990s). The share of own account transport operators and “padroncini” (owner of transport mean) is about 83% in terms of vehicle fleet and 95% in terms of number of operators. Table 4.8 Ownerships of freight vehicles (mid 1990s)

Ownership Vehicles OperatorsOwn account 368,000 198,000Self 175,000 126,000Third parties Hauliers 106,000 12,200Couriers 3,000 500MTO 2,000 25Forwarder 1,000 275Total 655,000 337,000

Source: CONFETRA (2002)


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4.1.4 Vehicles operating in cities

The vehicle type used for urban goods distribution differs per business type (recipient). Differences per country can be seen as well. These differences are also influenced by local differences caused by access regulation schemes. Table 4-9 shows the vehicle types used in urban distribution in three cities in the Netherlands which were collected in specific areas using a common data collection methodology. Table 4-9 Type of vehicle used for distribution in the Netherlands Vehicle type Utrecht Amsterdam Rotterdam Passenger car 33% 38% 28% Light goods vehicle 33% 18% 40% Heavy goods vehicle 21% 41% 27% Truck and trailer 13% 4% 5%

Source: Dataverzameling Stedelijke Distributie, TU Delft

The types of vehicles used for urban distribution vary by recipient’s sector. Table 4-10 shows the use of vehicle types per sector of recipients. Retailers in the Netherlands use mostly trucks for the urban distribution. In contrast, in the industry and service sectors vans are the most commonly used vehicles for deliveries. Table 4-10 Vehicle types used for urban distribution by recipient’s sector in NL Sector Car Van Truck Total Retail, food etc. 17% 37% 46% 100% Retail fashion 3% 18% 79% 100% Retail durables 8% 25% 67% 100% Industry 1% 67% 32% 100% Service 17% 41% 38% 100% Bars, restaurants 18% 46% 46% 100% Total 11% 37% 36% 100%

Source: DTO, 1997 in: Dunnewold 1999

A case study in Winchester shows that the food retail premises are predominantly frequented by rigid trucks, see Table 4-11. Articulated trucks and vans have an equal share. In retail other than food or clothing, vans are predominant. Restaurants are mainly served by articulated trucks, but hotels mainly by rigid trucks. Banks on the other hand are only served by vans.


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Table 4-11 Mean number of weekly core goods deliveries by vehicle type Business type Deliveries Articulated Rigid Van Car Food retail 16.4 21.0% 55.8% 23.2% - Clothing retail 4.8 32.0% 42.0% 26.0% - Other retail 8.6 7.5% 38.4% 49.5% 4.5% Restaurant 3.0 57.1% 14.3% 14.3% 14.3% Public house 5.0 - 70.0% 30.0% - Hotel 24.5 - 100.0% - - Banks 5.3 - - 100.0% - Other Services 9.7 5.3% 21.2% 65.7% 7.8% Warehousing 36.8 21.8% 44.9% 33.3% - Manufacturing 24.1 27.2% 34.3% 38.5% - Personal Services 2.3 - 25.0% 60.0% 15.0%

Source: Effects of Freight Movements in Winchester

4.2 Urban freight traffic flows

The urban freight transport flows are described by two indicators: the number of vehicles entering the city and the freight movements over day. This data is collected by manual or electronic counts.

4.2.1 Number of vehicles entering cities

Figure 4.3 shows that, on a daily basis 441,000 MGVs and HGVs are entering Greater London. Table 4-12 shows that most goods vehicles crossing the borders are LGVs. Figure 4.3 Boundary crossings by MGVs and HGVs in London

Table 4-12 Daily goods vehicle crossings of the GLA boundary by vehicle types Vehicle type Crossings Share HGVs 57,330 13% MGVs 83,790 19% LGVs 299,880 68% Total 441,000 100%

Source: TfL


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Table 4-13 shows that on yearly basis 123 million tons of goods are lifted by British HGVs with an origin or destination in London. This constitutes of 51 million lifted within London, 41 million entering London and 31 million leaving London. The goods lifted within London are mainly transported by rigid trucks. The goods entering the city are mainly transported by articulated trucks and goods leaving the city are equally often transport by rigid and articulated trucks. Table 4-13 Goods lifted by British HGVs on journeys within origin and/or destination in London by vehicle type in 2002 (percentage and millions) Vehicle type

Gross vehicle weight Within London

Entering London

Leaving London

All journeys with O and/or D in London

Rigid over 3,5 to 7,5 t 16% 5% 5% 9% Rigid over 7,5 to 17t 16% 4% 5% 9% Rigid over 17 to 25t 11% 4% 4% 7% Rigid over 25t 43% 16% 31% 31%

Rigid

ALL RIGID 85% 29% 45% 57% Articulated over 3,5 to 33t

4% 9% 7% 6%

Articulated over 33t 11% 62% 48% 37%

Articulated

ALL ARTICULATED

15% 71% 55% 43%

ALL VEHICLES 51 (100%) 41 (100%) 31 (100%) 123 (100%)

Source: DfT, 2004

In London, more light goods vehicles are used for urban freight instead of medium and heavy goods vehicles. Changing stockholding and goods delivery patterns required more frequent, smaller deliveries which resulted in growing use of LGVs. The amount of LGV traffic crossing the Greater London boundary cordon doubled between 1980 and 2001. LGV traffic crossing the Inner and Central London cordons have been increasing at a slower rate, but appear to have stabilized. Figure 4.4 shows changes in goods vehicles crossing the Greater London Boundary cordon.


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Figure 4.4 Goods vehicles crossing Greater London Boundary Cordon

Assuming average working day (hours 7-20), the number of freight vehicles entering in the urban areas of Bologna (2004), Milan (2002) and Genova (2004) are as shown in Table 4-14. It is important to realize that this table only refers to the urban centre. Table 4-14 Number of vehicles entering the urban areas

Indicator Bologna Milan Genova Dimension (km2) 3.15 9.6 6.6Freight vehicles 1,935 3,800 1,300Freight vehicles per km2 614.2 395.8 195.7

Source: Bologna, Regione Emilia-Romagna (2004), Comune di Milano (2002), Vito Maria Contursi, Comune di Genova, (2004)

4.2.2 Distribution of freight vehicles movements over day

The distribution of freight vehicle movements shows peak hours in distribution and whether or not passenger transport peak hours are avoided. Although freight vehicles have a relatively low share in traffic volumes, they are a source of congestion in a city. Access restriction schemes often force distribution operators to carry out deliveries during morning peak hours, however. This is a classic trade-off between freight vehicles aggravating congestion during morning peak hours or hindering consumers in shopping areas after the morning peak. On Figure 4.5, the black curve describes a typical distribution of the deliveries and pick-ups in the town Bordeaux (1995), the blue one, the whole private cars (household travel survey 1998). The peak hours are between nine and eleven a.m., when the shops are opening. At nine, a conflict appears between the two types of road users. The first peak appears between 7 and 8AM, corresponding to the opening of the factories and warehouses.


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Figure 4.5 Daily distribution of the deliveries and pick-ups in the town Bordeaux (survey 1995)

0%

1%

2%

3%

4%

5%

6%

7%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 240%

2%

4%

6%

8%

10%

12%

Source : LET, urban freight surveys in Bordeaux 1995, weighted data

Also in London, the morning hour peak in HGV activities coincides with the general traffic peak. Figure 4.6 shows goods vehicles as an index (7.00AM = 100). There is also a strong peak of HGVs in the mid-morning. Figure 4.6 Goods vehicles by time of the day in London

Source: DfT

Figure 4.7 shows the use of light goods vehicles during the day in London. The usage peaks between 8.00-09.00 and 16.00-17.00. This peak coincides with peaks in passenger transport demand. This indicates goods transport (or at least if carried out by light vehicles), is not avoiding traffic. Figure 4.7 Proportion of LGVs in use by time of day (weekdays)


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Source: GLA

In Italy, all the surveys carried out in the main urban areas show that the great share of consignments is delivered in the morning, with a peak between 10 and 11. In the afternoon the activity decreases, with a slight increase between 16-17 hours. Table 4-15 shows the delivery time in the city of Bologna. Table 4-15 Delivery times in the city of Bologna

Delivery times

06_07 07_08 08_09 09_10 10_11 11_12 12_13 13_14 14_15 15_16 16_17 17_18 18_19 19_20 20_06

hours

35%

0

Source : Elaboration of R.Rosini, Regione Emilia Romagna. Ministry of Transport and Infrastructure, 2006)


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4.3 Service visits and waste collection

4.3.1 Service visits

Service visits occupy space in the urban areas. Table 4-16 shows the number of service visits and the mean dwell time per visits in a specific area in Winchester. Most visits are mail deliveries, cleaning, waste and other collections and window cleaning. The time needed ranges from 7 minutes from mail deliveries to 80 minutes for lift maintenance. Table 4-16 Number of service visits and mean dwell time (minutes) by type of service visit Type of service Visits Dwell time Type of service Visits Dwell time Computer Equipment 10 52 Catering 40 41 Photocopier 8 38 Utilities 30 15 Security 9 44 Other services 10 55 Lifts 5 80 Ancillary 150 12 Window cleaning 120 30 Mail deliveries 450 7 Pest control 3 44 Mail collections 145 8 Floral care 40 44 Waste collections 145 11 Laundry 30 23 Other collections 155 15 Cleaning 210 74


Table 4-17 shows which share of premises within a specific area are using a specific service, how many the service visits are planned and how many are carried out ad hoc, and how many trips this involves per year. The majority of servicing activities are planned. Only computer servicing trips are more often carried out ad-hoc, although the yearly frequency is very low. Table 4-17 Vehicle trips for servicing activities made to premises surveyed Type of service Premises Planned ad hoc Average number of planned trips Computer equipment 56% 32% 64% 2 per year Photocopier 30% 81% 19% 44 per year Security/fire alarms 92% 76% 24% 6 per year Air conditioning 42% 64% 26% 4 per year Vending machines 26% 85% 15% 44 per year Window cleaning 72% 100% 0% 79 per year Florist/ plant care 8% 100% 0% 240 per year Towel /linen supplies 8% 100% 0% 156 per year Pest control 25% 91% 9% 11 per year

Source: A framework for considering policies to encourage sustainable urban freight traffic and goods/service flows

Regarding the vehicle types used for service visits, more than half of all visits is carried out by vans, 15% is carried out by car and 15% on foot. Table 4-18 Mean number of weekly service visits by mode Transport mode Weekly service visits Share Transport mode Weekly service visits Share


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Articulated lorry 100 7,6% Motorbike 0 0,0% Rigid lorry 100 7,6% Bicycle 30 2,3% Van 698 52,7% Foot 199 15,0% Car 197 14,9%


In Winchester, the majority of the service vehicles arrive in the AM period and 1/3rd arrives at any time of the day, see table 4-19. Table 4-19 Reported arrival times of service vehicles Service vehicle arrival period am pm am/pm Number 377 134 232 Share 51% 18% 31%


4.3.2 Waste collection

Waste collection is an increasingly important part of urban goods movements. Table 4-20 shows the results of a survey in Great-Britain as to how many waste collection services are used by each premise. The majority of premises use one waste collection, although 22% is using two services and one was even using three. Table 4-20 Number of waste collection services used by premises surveyed Number of waste collection services used Number of premises Share 1 38 66% 2 13 22% 3 1 5% Not known 6 10% Total 58 100%


A quarter of the premises has one waste collection vehicle visit per week, Table 4-21. Almost half of the premises receive more than 6 visits per week. Table 4-21 Number of vehicle trips per week to the premises to collect waste Number of waste collection vehicle visits to the premises Number of premises Share 1 trip per week or less 12 26% 2-5 trips per week 13 28% 6-10 trips per week 20 43% More than 10 trips per week 2 4% Total 47 100%


The waste service of collection, i.e. frequency, number of movements, etc, varies with the specific characteristics of urban context. No national reports are available for a comprehensive survey at national level. Some local information is available, for instance in Rome (Autorità per I servizi publici locali, 1999) the daily amount of waste collection is about 4,000 tonnes. The


33

total daily movements are 430, corresponding to an average yearly amount of 120,000 movements through dedicated trucks. The service is organized in three rounds: − From 6.00 to 12.00 − From 13.45 to 19.45 − From 23.30 to 04.30

4.4 Performance

The transport performance can be expressed by the number of vehicle kilometers and by the loading factor (indicating the efficiency).

4.4.1 Freight vehicle kilometers

The number of freight vehicle kilometers in urban areas is a very important indicator to determine the impact on the urban road network and to determine the environmental impacts. Overall figures are available for Germany. The KID survey reports that about 203 billion vehicle km of trucks <3.5 tonnes are dedicated to commercial transport in Germany. In London, the heavy goods vehicles travel about 1.0 billion vehicle km a year, whereas light goods vehicles travel 1.3 billion, see Table 4-22. The data in the second column is an estimate of the vehicle km performed in London by HGVs and privately-owned LGVs (i.e. not including company owned LGVs) making trips to, from, or within London. This data was collected by surveying vehicle operators about all the trips they made in Britain (including London trips) for several days. An alternative calculation of vehicle km performed by all HGVs and LGVs on major roads (minor roads not included) in London produced the estimates in the third column for 2002 (the data used to make this estimation is obtained from traffic counts in London):

Table 4-22 Total annual vehicle km (million) in London

Vehicle type HGVs and privately-owned LGVs All HGVs and LGVs Heavy goods vehicles 988 887 Light goods vehicles 1,289 2,226

When considering trips with both the origin and the destination in London, light goods vehicles make 2.8 times more daily trips than heavy goods vehicles. The heavy goods vehicles have a 2.3 times longer trip distance. This amounts to 2.2 and 2.7 million daily vehicle km.

Table 4-23 Daily vehicle km for goods with both origin and destination in London

Vehicle type Daily vkm (million) Daily trips Average trip (km) HGV 2.2 80.000 28 LGV (only privately owned) 2.7 225.000 12 Passenger cars 132.0 11.000.000 12


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In some cities very specific information is available on the vehicle types used. In London for instance, 90% of the mileage is made by rigid trucks and 10% by articulated trucks (on trips with both origin and destination in London), see Table 4-24. Most of rigid trucks mileage is by the smallest category.

Table 4-24 Vehicle km performed by British HGVs on journeys with both origin & destination in London in 2002 (DfT, 2004a)

Vehicle type Gross vehicle weight Vehicle kilometres (million) Percentage Rigid over 3,5 to 7,5 t 305 54% Rigid over 7,5 to 14t 31 5% Rigid over 14 to 17t 70 12% Rigid over 17 to 25t 53 9% Rigid over 25t 51 9%

Rigid

ALL RIGID 510 90% Articulated over 3,5 to 30t 13 2% Articulated over 30 to 33t 8 1% Articulated over 33t 33 6%

Articulated

ALL ARTICULATED 54 10% ALL VEHICLES 564 100%

Source: Freight transport in London: a summary of current data and sources

An overall picture on urban trips for French cities is given below, see Table 4-25.

Table 4-25 Urban distance (vehicle km) covered each day by motorized vehicles5

Segment Bordeaux Marseille Dijon Shopping trips 1,400,000 10% 1,750,000 13% 237,000 7% Deliveries and urban management flows 620,000 4% 790,000 6% 200,600 6% Transit (heavy vehicles through traffic) 550,000 4% 180,000 1% 68,000 2% Other (individual car ) 11,500,000 82% 10,500,000 79% 3,000,000 86% Total 14,070,000 100% 13,220,000 100% 3,505,600 100%

Sources: LET, Aria Technologie, Systems consult, 2000, according to urban freight surveys results in Bordeaux, Marseille, Dijon, 1995-97.

Table 4-26 Components of the Urban Goods Movement in France

Indicator Share in vehicle km Share in car unit vehicle km Deliveries and pick-up 24% 40% Purchasing trips by car 69% 50% Urban management6 7% 10%

Source: Presented in “State of the art of data collection for urban freight transport in France”. Presentation Jean-Louis Routhier at BESTUFS modeling round table

In Italy 25% of the urban transport is done by LGV’s with an urban traffic volume of 9,596 million vehicle kilometers.

Table 4-27 Freight vehicle-kilometer in urban areas (1999)

5 Except two-wheeled vehicles 6 Building sites, network maintenance, waste collection, removals


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In Norway, 95% of the goods (tonnes) transported within (internal) the cities are transported by vehicles with maximum load more than 3.5 tonnes7.

4.4.2 Use of load capacity

The use of load capacity is an indicator for the efficiency of transport operations. This is usually expressed in the share of the maximum weight that can be transported in the vehicle. However, in urban freight, loads are more often voluminous than heavy. Therefore, in case studies often attempts are made to determine the use of load capacity in volume in addition to the use of loading capacity in weight (tons). Another used indicator is the share of loaded and empty trips, instead of the use of loading capacity of individual trips. Because national statistics typically publish use of loading capacity for all transport operations which include heavy goods transport, this report focuses on the results of specific measurements and case studies in cities. In general, it is recognized that the loading factor in urban freight transport is rather high, especially when expressed volume capacity, see table 4-28.. Generally the loading factor expressed in volume is higher than the load factor in weight. This indicates why it is hard to increase the weight load capacity.

7 Larsen and Andersen, TØI rapport 737/2004

Vehicles Vehicle km Traffic volume (million vkm) Urban share

Urban traffic volume (million vkm)

LGV 2,466,232 15,564 38,384.4 25% 9,596.1 Petrol 372843 11,454 4,270.5 25% 1,067.6 Diesel 2,093,390 16,296 34,113.9 25% 8,528.5 HGV 855,166 43,961 37,594.0 12% 4,511.3 Petrol 10,995 5,000 55.0 20% 11.0 Diesel 844,170 44,468 37,538.6 12% 4,504.6 Total 3,321,398 75,978.4 14,107.4


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Table 4-28 Mean loading factor per vehicle per trip by departure in weight and volume 1999

Vehicle type Groningen Amsterdam Tilburg Weight Volume Weight Volume Weight Van <1500 kg 30% 80% 50% 62% 61% Van 1500-3500 kg 85% 86% 93% 81% 68% Truck 3500-7500 kg 61% 80% 78% 75% 57% Truck > 7500kg 73% 90% 62% 79% 73% Truck and trailer >7500kg 95% - 70% 85% - Articulated truck >7500 kg - - 65% 65% 70% Mean - - 69% 76% 66%

Source: effectstudies

From the “City Goods” project in Copenhagen the following figures on the use of capacity of trucks entering the inner city zone from 1 Nov. 2002 to 31 January 2004 (see Table 4-29).

Table 4-29 Loading factor per vehicle in the inner city area of Copenhagen

Trucks by GVW Average use of capacity Trucks by GVW Average use of capacity 2500 - 2800 kg 66% 3501 - 6000 kg 65% 2801 - 3000 kg 85% 6001 - 12000 kg 73% 3001 - 3200 kg 67% 12001 – 18000 kg 65% 3201 - 3500 kg 70% Mean 70%

Source: Hendrik Jensen, BESTUFS workshop Maribor

In London the use of load capacity varies between 40 and 60% on vehicles active, see Table 4-30. The lowest rates are with vehicles traveling within London, contrary to vehicles either entering or leaving the city. There is no significant difference between rigid and articulated trucks.

Table 4-30 Lading factor for British HGVs on journeys with origins and/or destinations in London by vehicle type and mode of working in 2002 (DfT, 2004a)

Vehicle type Gross vehicle weight Within London Entering London Leaving London Rigid over 3,5 to 7,5t 35% 41% 52% Rigid over 7,5 to 17t 35% 52% 42% Rigid over 17 to 25t 37% 57% 48% Rigid over 25t 53% 67% 61%

Rigid

ALL RIGID 43% 59% 57% Articulated over 3,5 to 33t 33% 48% 41% Articulated over 33t 45% 64% 57%

Articulated

ALL ARTICULATED 41% 62% 54% ALL VEHICLES 42% 61% 55%


In Budapest, most goods vehicles are travelling partly loaded, except from trucks over 6 ton, which are fully loaded in 50% of the cases, see Table 4-31. Figures for commercial transport t with vehicles <3.5 tonnes in Germany suggest a share of 72,7% loaded vehicles (empty 27,3%), see Table 4-32.

Table 4-31 Distribution of the transports within Budapest by carrying capacity and loading


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Loading < 1,5t Share < 3,5t Share < 6t Share > 6t Share Total Loaded (full) 65 13,5% 164 15,6% 17 25,0% 233 50,8% 479 Partly loaded 324 67,5% 676 64,5% 43 63,2% 163 35,5% 1206 Empty 91 19,0% 208 19,8% 8 11,8% 63 13,7% 370 Total 480 23,4% 1048 51,0% 68 3,3% 459 22,3% 2055


Table 4-32 Share of loaded trips of commercial vehicles <3.5t in Germany

Vehicle trips Share Loaded 72,7% Empty 27,3%

Source: "Kontinuierliche Befragung des Wirtschaftsverkehrs in unterschiedlichen Siedlungsräumen". Kraftfahrzeugverkehr in Deutschland (KID), 2002. www.verkehrsbefragung.de


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5 URBAN DELIVERIES

The impact category “urban deliveries” presents a number of indicators on operational urban freight transport deliveries, both from the operator’s view and from the receiver’s view. Also the home delivery characteristics are shown in this chapter. The following indicators have been identified: − General delivery characteristics (operators):

o Combined shipments o Delivery days and times (distribution of goods deliveries by day of the week

and time of the day) o Regularity of trips (frequency of the trips – daily, monthly, etc.) o Origin of delivery trips (from shippers, from warehouses, etc.) o Number of stops per tour and per day o Trip length (number of km per round trip / hours per trip) o Distance between stops o Travel time to and within city centre

− General delivery characteristics (receivers) o Deliveries per receiver type o Dwelling time in urban area / loading and unloading times

− Home deliveries o Home delivery offered by shops o Number of km covered per inhabitant

5.1 General delivery characteristics (operators)

In order to evaluate urban freight transport in a city, it is necessary to have an understanding of the general delivery characteristics of these transport operations. This paragraph introduces a number of indicators regarding the operational aspects of urban freight, in contrast to the aggregated transport volumes and traffic indicators presented in the previous chapters.


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5.1.1 Combined shipments

A major issue in urban freight transport is the share of combined shipments. Information about combined shipments is typically gathered in urban freight case studies. In the Netherlands, most shipments are combined, although this varies between cities, see Table 5-1.

Table 5-1 Combined shipments per week in four cities in the Netherlands in 1999

Combined shipments Amsterdam Tilburg Den Bosch Groningen No combined shipments 20% 13% 5% 6% In de city centre 27% 13% 13% 38% In the centre and elsewhere in the city 10% 42% 33% In the centre and outside the city 2% 45% In the city and outside the city 18%

26%

Other* 24% - 23%

56%

Total 100% 100% 100% 100%

Note (*) In effectstudie Amsterdam is stated that the other category consists of combined shipments for outside the centre of Amsterdam and/or no combined shipments. Source: effectstudies

A study in three cities in France has investigated a number of operational aspects of urban freight transport. This study showed (Table 5-2) that 25% of the vehicles accomplish tours and they carry out 75% of the pick-ups or deliveries. However, 75% of the vehicles accomplish direct trips, which account for 25% of the deliveries or pick-ups (see). In total 60% of operations constitute deliveries and 40% pick-ups. Hauliers carry out 40% of the deliveries and pick-ups, own account (forwarders) 37%, own account (consignee) 19%.

Table 5-2 Combined shipments and direct trips and type of transport operator in France

Indicator Types Share Round trip 25% Vehicles Direct trip 75% Round trip 75% Operations Direct trip 25% Hauliers 44% Own account (consignee) 19%

Operator type

Own account (providers) 37%

Source: LET, urban freight surveys in Bordeaux, Marseille, Dijon, 1995-97

A study on key indicators of the Italian urban delivery system showed that 87% of the freight vehicles circulating in urban areas are in own account and only 13% on account of third parties. This implies that on average the logistics in own account delivers between 1 to 4 consignment/day per trip against the average of 40-60 of third party logistics, which can benefit of better load consolidation and traffic and loading optimization techniques the loading factor of vehicle is on average low, i.e. below 25%.

5.1.2 Delivery days and times

Delivery days


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The delivery days indicate whether there is a preference for certain days, either from the side of the operators or of the receivers. This data is highly specific for each country and city. It depends on the local situation, but in general for larger cities a more or less even distribution is expected, see Table 5-3. In specific sectors, such as high value luxury products, specialized operators distribute to all retail outlets, resulting in less than daily deliveries, which might cause uneven delivery patterns. In some countries a truck ban is in force on Saturdays, and some in cities shops open on Sundays.

Table 5-3 Daily distribution of goods in The Netherlands (specific areas)

Day Utrecht Amsterdam Rotterdam Monday 16% 15% 16% Tuesday 19% 16% 23% Wednesday 20% 21% 14% Thursday 20% 18% 19% Friday 20% 22% 17% Saturday 6% 7% 2% Sunday 0% 1% 8%

Source: Dataverzameling Stedelijke Distributie, TU Delft

A study on the delivery pattern in Bologna shows the delivery days depending on the kind of goods to be delivered, see Table 5-7. In general, it shows a concentration of deliveries in the mid-week days.

Table 5.6 Daily distribution of goods in the City of Bologna (2004)

Day Food Non Food HoReCa Clothes Average Monday 10% 24% 10% 10% 13% Tuesday 21% 20% 21% 30% 25% Wednesday 27% 21% 28% 20% 24% Thursday 6% 19% 16% 15% 15% Friday 36% 15% 20% 22% 21% Saturday 0% 1% 5% 3% 2%


Delivery times Delivery times depend on opening hours and delivery time windows. As there is no harmonization in urban freight data collection and presentation of the results, it is hard to compare data on different cities. In the Netherlands, data has been collected three cities using the same method. These studies showed that 80% of the goods deliveries take place in the morning hours, see Table 5-4 and a limited number of deliveries takes place in the afternoon. Evening and night distribution only takes place in Amsterdam.

Table 5-4 Time of goods delivery in The Netherlands

Time frame Utrecht Amsterdam Rotterdam 7:00 to 12:00 77% 82% 80% 12:00 to 18:00 22% 7% 19% 18:00 to 23:00 1% 0% 0% 23:00 to 7:00 0% 11% 0%


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Source: Dataverzameling Stedelijke Distributie

A case study in a Winchester in Great-Britain, in which deliveries were monitored on street level, show that the delivery times are even area specific: in some areas most deliveries take place between 6AM and 9AM, whereas in other areas this takes place more between 9AM and 16AM, see table 5-5.

Table 5-5 Number of core deliveries per delivery time by area in Winchester

Delivery times Bar End Winnall High St. Other Central 4:00-6:00 0 0 10 12 6:00-9:00 50 30 100 70 9:00-16:00 80 140 65 270 16:00-18:00 35 10 12 2 18:00-21:00 0 4 2 0 21:00-4:00 4 0 0 8


Figure 5.1 shows the delivery frequencies in Dublin in a bar chart.

Figure 5.1 Frequency of deliveries arriving each hour during the day

Ref. Sustainable Freight Distribution in a Historic Urban Centre, November 2004, (TCD)


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5.1.3 Regularity of trips

The regularity of trips depends on the receivers characteristics (demand, storage space, etc.) and therefore depends on the area. A complete road traffic survey the Budapest agglomeration shows that in Budapest 35% of the trips is at least daily and 71% at least weekly and 23% is occasional, see Table 5-6. A case study in Milan shows that about 60% of trips is carried out on a daily basis, see Table 5-7.

Table 5-6 Distribution of the transports within Budapest by regularity

Regularity Trips Share Daily more 307 14.9% Daily one 401 19.5% Weekly more 487 23.7% Weekly one 245 11.9% Monthly one 75 3.6% Occasionally 468 22.8% Non regular 75 3.5% Total 2055 100.0%


Table 5-7 Regularity of trips in the city of Milan (2002)

Frequency Freight vehicles Share Daily 2278 59.4% Weekly 628 16.4% Monthly 164 4.3% Irregular 580 15.1% Non specified 183 4.8% Total 3833 100.0%

Source: Comune di Milano (2002)

5.1.4 Origin of delivery trips

Delivery trips are organized from depots and distribution centers, and they can be organized on a permanent or non-permanent basis. Table 5-8 gives some figures from a road traffic survey in Budapest. This same survey indicated that in 71% of the trips within the city originate from depots out of the city, see Table 5-9. Most transit runs (95%) originate from depots out of the city.


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Table 5-8 Origin of delivery trips in Budapest

Loading Trips Share Depot in/out 209 10,2% Permanent collection 109 5,3% Permanent distribution 188 9,1% Permanent direct 337 16,4% Non-permanent collection 276 13,4% Non-permanent distribution 325 15,8% Non-permanent direct 611 29,7% Total 2055 100,0%


Table 5-9 Urban distribution transport characteristics Budapest: location of distribution depot

Distribution by depot Within the city Transit runs Within the city 28,7% 5,5% Out of the city 71,3% 94,5%


A study in the Emilia Romagna region in Italy shows that the number of deliveries in an average day in the city of Bologna is 23,456. Most of the trips are carried out from the Bologna district, especially the trips made by shopkeepers.

Table 5-10 Carriers versus delivery district

Carriers Bologna District Bologna Province Emilia Romagna Region Total Transport on own account 51% 34% 15% 100% Transport on behalf of third parties 57% 42% 1% 100% Agent 48% 48% 5% 100% Shopkeeper 87% 13% 0% 100%


5.1.5 Number of stops per tour / per day

The distribution trips can be presented by vehicle type, for instance by trucks smaller or larger than 3.5t loading capacity and other vehicles. Table 5-11 shows that in Germany the number of trips per vehicle is 5.0 to 5.5. The larger trucks have three times longer operating times and four times more vehicle kilometers.


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Table 5-11 Characteristics of urban distribution trips in Germany in 2002

Category Indicator Weekdays Weekends and public holidays

<3.5t >3.5t Other <3.5t >3.5t Other Daily trips per vehicle 5.6 5.5 4.3 5.0 4.9 3.3 Daily trips per vehicle (commercial traffic)

5.0 5.5 3.6 4.0 4.9 2.4

Share of loaded trips (%) 72.7 78.4 36.0 78.2 90.6 56.8

Traffic volumes

Daily operating times (minutes) 115.8 374.1 104.5 92.4 420.6 93.8 Vehicle km per day 87.5 321.4 59.9 77 396.4 65.8 Vehicle km per day (commercial traffic)

77.0 320.0 43.5 50.4 390.6 33.6 Road performance

Tonkm per vehicle (commercial traffic)

43.9 3,108.3 196.5 29.9 4,391.2 50.9

Distance (km) 14.7 55 11.2 12.8 76.3 11.6 Duration (minutes) 28.1 83.8 26.4 27.5 185.9 29.4

Trip characteristics

Transported goods per trip (kg) 483.8 8,978.4 3,486.9 410.8 9,248.5 2,397.5

Source: "Kontinuierliche Befragung des Wirtschaftsverkehrs in unterschiedlichen Siedlungsräumen". Kraftfahrzeugverkehr in Deutschland (KID), 2002. www.verkehrsbefragung.de

5.1.6 Trip length

The trip length is the total trip length from leaving the warehouse, delivering (and/or collecting) all goods and returning to the base. The availability of this data varies strongly between cities. Table 5-12 shows that most goods lifted within London (36 million tons, 71%) are transported over transport distances of up to 25kms. The goods entering and leaving London have longer travel distances.

Table 5-12 Good lifted by British HGVs with origin and/or destination in Greater London in 2002 (million tonnes and percentages) (DfT, 2004a)8

Trip length (km) Within London Entering London Leaving London All with O and/or D in London Up to 25 36 (71%) 4 (10%) 4 (14%) 45 (37%) Over 25 to 50 9 (18%) 8 (19%) 8 (25%) 24 (20%) Over 50 to 100 4 (7%) 10 (24%) 5 (18%) 19 (15%) Over 100 to 200 2 (4%) 11 (26%) 8 (24%) 20 (16%) Over 200 to 300 0 (1%) 5 (11%) 3 (10%) 8 (7%) Over 300 0 (0%) 4 (10%) 3 (9%) 7 (6%) All lengths 51 (100%) 41 (100%) 31 (100%) 123 (100%)


In France, the trip lengths have been collected by surveys among drivers. Table 5-13 shows that hauliers generally have a longer trip length than companies own account transport.

Table 5-13 Average trip length by owner type in France (km)

Owner type LGV<3_5t Rigid lorries Articulated

8 Totals may not add up due to rounding


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Hauliers 108 km 81 km 63 km Own account 40 km 90 km 45 km Total Average 56 km 86 km 52 km

Source: LET, driver survey (2255 vehicles), sample data not straightened.

A case study in Bologna (Italy) shows significantly lower trip lengths than in France, see Table

5-14.

Table 5-14 Trip length (in km) in the city of Bologna (2004)

Carriers Trip length Transport on own account 46 Transport on behalf of third parties 52 Agent 48 Shopkeeper 20

Source: …

Average trip length for Oslo: − Vehicle < 3.5 tonnes maximum load = 14 km − Vehicle > 3.5 tonnes maximum load = 11 km

5.1.7 Distance between stops

The distance between two stops depends on several factors. One of the most important is the transport organiser: a trip organised by a haulier can usually combine shipments to receivers in smallers areas. Parcel deliveries are a clear example. Table 5-15 shows figures on the town of Marseille.

Table 5-15 Average distance between stops by owner type and vehicle type in Marseille (km)

Owner type LGV<3.5t Rigid lorries Articulated TOTAL Hauliers (for hire) 3 km 5 km 15 km 6 km Own account (consignee) 11 km 16 km 19 km 13 km Own account (forwarder) 7 km 8 km 14 km 8 km Total 7 km 8 km 16 km 8 km

Source : LET, Model Freturb applied on the town of Marseille.


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5.1.8 Trip times

The trip time is dependent on several factors, such as number of shipments, travel distance, etc. In Budapest, the average trip time is mainly between 0.5 and 1.5 hours, see Table 5-16.

Table 5-16 Average trip times in Budapest (hours)

Trip time Count Share Trip time Count Share

< 0.5 7 0,3% 2.5 84 4,1%

0.5 643 31,3% 3.0 78 3,8%

1.0 614 29,9% 5.0 140 6,8%

1.5 312 15,2% 8.0 22 1,1%

2.0 131 6,4% 24.0 24 1,2%

Source: Complete road traffic survey of Budapest and the agglomeration

5.1.9 Travel time to and within city centre

The city size has a strong influence on the travel time, as can be seen in Table 5-17: in a large city like Amsterdam, it takes longer to reach the first destination of the trip from the city border than in medium sized cities like Den Bosch en Tilburg. Table 5-18 shows that the trips in Amsterdam are usually shorter than in Den Bosch or Tilburg.

Table 5-17 Average travel time from city border to first destination

Average travel time Amsterdam Den Bosch Tilburg 0-15 minutes 39% 51% 61% 15-30 minutes 55% 35% 34% 30-45 minutes 7% 14% 2% 45-60 minutes - - 0% Over an hour - 0% 2% total 100% 100% 100%

Source: effectmetingen Groningen, Den Bosch, Tilburg en Amsterdam

Table 5-18 Average travel time in city centre

Average travel time Amsterdam Den Bosch Tilburg 0 to 30 minutes 32% 14% 18% 30 to 60 minutes 21% 5% 25% 1 to 1,5 hour 9% 16% 8% 1,5 to 2 hours 14% 27% 20% Over 2 hours 25% 38% 28% Total 100% 100% 100%

Source: effectmetingen Groningen, Den Bosch, Tilburg en Amsterdam

Table 5-19 shows some characteristics of delivery trips in three cities in the Netherlands: time needed to drive into town, average stay and average stop time. As these figures are influenced


47

by many factors (city size, congestion, product types, etc.), more information about the these trips is needed.

Table 5-19 Average minutes and modus for drive up, stay and stop while delivering

Indicator Amsterdam Utrecht Rotterdam Average drive up duration 19 21 19 Modus drive up duration 15 15 15 Average stay 200 163 170 Modus stay 180 60 120 Average stop duration 21 34 27 Modus stop duration 15 10 15

Source: Verklaring Stedelijke Distributie (NL-01)

A case study in Livorno (Italy) has shown that the average dwelling time of a delivery vehicle is dependent on the product type. This was also shown in Dublin. The average dwell time for deliveries was 14 minutes. This varies across different business types, for example deliveries to clothing retail stores were found to have an average dwell time of approximately 26 minutes, department store deliveries had an average dwell time of approximately 16 minutes and offices/financial institutions had an average dwell time of just 7 minutes (Ref. Sustainable Freight Distribution in a Historic Urban Centre, November 2004, (TCD), by Margaret O’Mahony, Hugh Finlay, Clare Finnegan)

Table 5.18 Average dwelling time in the city of Livorno (2003)

Type of goods Delivery time at premises (minutes) Newspaper, food < 10 Non food 10-20 Clothes, domestic appliances, etc > 20

Source: Ondaverde (2003)

Unloading times in Norway: − Average 17 minutes, mainly from to cities Trondheim and Tønsberg. (studies from 200 to

2003)9 − Earlier studies (1994) show similarly results Average in 4 cities between 14 and 19 minutes

for trucks more than 11 tonnes total maximum weight. The study from 1994 also states that average time unloading times for vans are much longer, (40 minutes) 10

9 Sintef 2003, Rødseth and Nicolaisen 10 TØI 1994/no 260 Hagen, Killi, Grue


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5.2 General delivery characteristics (receivers)

5.2.1 Deliveries at premises

An important indicator in urban freight transport is the number of deliveries and pick-ups per week per receiver type. In general this is indicated by the total number for all receivers per type or the number per establishment of a type. Case studies show that there is a wide range in figures, which makes comparisons difficult. Also the categorization of receiver types differs strongly between cities. Table 5-20 shows an example of deliveries and pick-ups in France.

Table 5-20 Distribution of the establishments, employment and deliveries / pick-ups by activity

Activity Establishments Employment Deliveries and pick-ups per week

Share Deliveries Pick-ups

Small retail 24% 14% 197,527 25% 47% 53% Wholesailers 5% 5% 174,509 22% 73% 27% Crafts 29% 14% 131,485 17% 55% 45% Industry 9% 17% 127,505 16% 35% 65% Warehouses 1% 2% 80,920 10% 91% 9% Tertiary offices 31% 45% 55,488 7% 83% 17% Supermarkets 0% 3% 19,306 2% 70% 30% Agriculture 1% 0% 2,822 0% 38% 62% Total 100%

(110 000) 100%

(779 563) 789,563 100%

(789 563) 61% 39%

Source: LET, towns of Bordeaux, Marseille and Dijon surveys data (4,500 establishments, 1995-97), straightened with the Freturb model

5.2.2 Dwelling time in urban area / loading and unloading times

The dwelling time in the urban area is a measure for the space occupancy of urban freight and (combined with the delivery quantities) for the efficiency of deliveries. Table 5.19 and Table 5.20 show the wide range of delivery times per type of premises encountered in Great-Britain.

Table 5.19 Average time taken for deliveries for a range of premises

Type of premises Delivery time Type of premises Delivery time Florist 15 (Food delivery) 5 Off-licence 120-180 Convenience grocer 2-15 Chemist 30 Fast food restaurant 30-120 Book shop 5-60 Clothes shop 15-30 Stationers 30 Clothing/food shop 30-45 Pub (Drink delivery) 15-30 Furniture shop 5-120 (Food delivery) 5 Gift shop 5-10 Pub (Drink delivery) 30-120

Source: A framework for considering policies to encourage sustainable urban freight traffic and goods/service flows (GB-07)

Table 5.20 Mean dwell time (minutes) by business type in Winchester (across all vehicle classes)


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Business Type Mean dwell time Business Type Mean dwell time Food retail 19 Banks 7 Clothing retail 23 Other services 14 Other retail 20 Warehousing 18 Restaurants 42 Manufacturing 11 Pubs 14 Personal services 19 Hotels 37

Source: Effects of Freight Movements in Winchester (GB-03)

The dwell time also differs by vehicle and delivery area, see table 5.21: The larger the vehicle, the longer the dwell time is.

Table 5.21 Mean dwell time (minutes) by delivery vehicle by area

Type of vehicle Centre Winnall High St. Bar End Articulated Lorry 32 23 41 50 Rigid Lorry 21 14 20 20 Van 9 6 12 8 Car 9 6 7 6

Source: Effects of Freight Movements in Winchester (GB-03)

The majority of the distribution trips are rather short. In Tilburg, more than half of the trips (56%) stays in the city centre less than 15 minutes. 19% stays more than one hour (see Table

5-21).

Table 5-21 Total times spent by trucks in city centre (minutes)

Length of stay Trucks Share Length of stay Trucks Share 1-5 143 33% 31-45 25 6% 6-10 58 13% 46-60 20 5% 11-15 45 10% 61-90 23 5% 16-20 22 5% 91-120 18 4% 21-25 17 4% 121-600 51 12% 26-30 14 3% Total 436 100%

Source: Effectmeting Tilburg (BRO, 2000)


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5.3 Home deliveries

5.3.1 Home delivery offered by shops

Home deliveries constitute an alternative way of getting products to consumers, for example by e-commerce. In formation about these services is gathered in some surveys. For instance, in Great-Britain a small scale survey indicated that a majority of shops offer a home delivery service, or may do this soon, see Table 5-22. In Ireland, Tesco and Superquinn supermarkets facilitate home deliveries. As far as Tesco concerns: regular customers spent an average of €130,- once every 3 weeks. For the situation of Superquinn there is no data available (Ref. ‘Internet shopping service creates over 80 new jobs at Tesco Ireland in first year of operation’, press release (www.tesco.ie))

Table 5-22 Does the shop offer a home delivery service in which goods are delivered to the customers from the shop?

Home delivery service offered? Number of premises Yes (official service) 24 Yes (unofficial service)* 1 No, but will post goods to customer if requested 5 No, but may do soon 16 No 2 N.B. * - unofficial in the sense that this shop is part of multiple retailers who have not implemented this as a standard service, but the shop staff are personally prepared to offer this service

Source: A framework for considering policies to encourage sustainable urban freight traffic and goods/service flows (GB-07)

5.3.2 Number of km covered per inhabitant

In some countries studies have been carried out regarding the freight movements by consumers. In France a survey found that urban goods movement seems very low regarding the individual car flows. The main part of urban goods traffic is realised by the individuals when they are purchasing with their car. But it is necessary to do several remarks which must be kept in perspective: − the flows that are counted are specifically dedicated to the transport of goods (including

empty return but no individual nor professional trips), − the traffic due to the exchanges between the town and the outside was underestimated,

notably, concerning the traffic between the outlying platforms and the town. − the real impact of the commercial vehicles in the whole traffic is not well appraised by the

single indicator of vehicle km. It does not take into account the place taken on the street by the vehicles running and stationary (congestion measurement), the fuel consumption and the environmental impact.


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Table 5-23 Share of the number of km per inhabitant each day in France11

Segment Bordeaux Marseille Dijon Shopping (by car) 1,87 km 1,72 km 1,05 km Urban freight Deliveries and urban management flows 0,83 km 0,78 km 0,89 km

Other Other (individual car ) 13,8 km 10,3 km 13,4 km

Sources: LET, Aria Technologie, Systems consult, 2000, according to urban freight surveys results in Bordeaux, Marseille, Dijon, 1995-97.

11 Except two-wheeled vehicles


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6 ECONOMY

6.1 Employment, % in transport and logistics

Table 6-1 shows the number of freight transport companies and freight transport employees per country.

Table 6-1 Unternehmen und Beschäftigte der Transportwirtschaft in den EU-15-Ländern 2000

Country Freight transport companies

Freight transport employees (*1.000)

Country Freight transport companies

Freight transport employees (*1.000)

Belgien 7.298 62,0 Italien 112.173 308,6

Dänemark 7.994 44,0 Luxemburg 478 5,5 Deutschland 32.885 390,0 Niederlande 10.290 122,1 Finnland 11.843 38,0 Österreich 5.019 47,7 Frankreich 44.311 305,1 Portugal 5.906 45,7 Griechenland N.A. N.A. Schweden 15.447 63,5 Großbritannien 36.819 329,0 Spanien 130.141 301,6 Irland 2.919 12,8

Source: http://wko.at/bsv/Internet/Unternehmen_Beschäftigte_EU15.htm

In London, about employment in freight transport and distribution activities amount to 2.4% of the total employment in the area, see Table 6-2.

Table 6-2 Employment in freight transport and distribution activities in Greater London12

Sector Employment in Greater London, 2002

As a % of total employment in Greater London

Freight transport by road (SIC 60.24) 14,065 0.4% Storage and warehousing (SIC 63.12) 8,681 0.2% Activities of other transport agencies (inc. freight forwarding and goods handling operations) (SIC63.40)

19,037 0.5%

National post activities (SIC 64.11) 39,895 1.0% Courier activities other than national post activities (SIC 64.12)

13,622 0.3%

Total 95,300 2.4%

Source: ONS, 2004

12 SIC codes shown in the table are UK (1992) SIC codes


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There is also other employment in other sectors that include freight transport and distribution activities, see Table 6-3.

Table 6-3 Employment in other sector that include freight transport and distribution activities in Greater London13

Sector Employment in Greater London, 2002

As a % of total employment in Greater London

Other industries that include logistics and distribution activities

Wholesaling (SIC 51) 170,514 4.3% Retailing (SIC 52) 377,966 9.6% Manufacturing (SIC 15-37) 236,166 6.0% Other transport sectors that include logistics and distribution activities

Transport via railways (SIC 60.10) 12,736 0.3% Sea and coastal water transport (SIC 61.10) 2,033 0.1% Inland water transport (SIC 61.20) 273 0.0% Scheduled air transport (SIC 62.10) 39,569 1.0% Non-scheduled air transport (SIC 62.20) 987 0.0% Cargo handling(SIC 63.11) 1,218 0.0%

Source: ONS, 2004

13Sic codes shown in the table are UK (1992) SIC codes


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7 ENVIRONMENT

7.1 Energy use

Energy use is an important indicator to determine the effects on the environment. This can be measured by the fuel consumption by each vehicle type in general and for urban freight and the opportunities for alternative fuel types.

7.1.1 Typical fuel consumption by vehicle type

Fuel consumption varies considerably depending in the type of traffic, roads, driving behaviour, etc. The data in Table 7-1 contains general data about the fuel consumption for Euro 4 and Euro 5 diesel trucks in different types of transport for empty and fully loaded trips.

Table 7-1 Typical fuel consumption in liters per 100 km

Type Payload in tons Total weight in tons

Litres/100km empty

Litres/100km full load

Trucks, distribution traffic 8.5 14 20-25 25-30

Trucks, regular traffic 14 24 25-30 30-40

Tractor and semi trailer, long-haul traffic 26 40 21-26 29-35

Truck with trailer, long-haul traffic 40 60 27-32 43-53

Source: Volvo (2006)

For Great Britain the average distance per gallon is increased over the last ten years by 11% for rigid vehicles and by 14,5% for articulated vehicles.

Table 7-2 Average fuel consumption by age and type of vehicle (miles per gallon)

HGV 1993 1996 1999 2002 2003 2004 Rigid vehicles 7.5 8.2 8.3 8.1 7.8 8.3 Articulated vehicles 6.9 7.3 7.7 7.6 7.5 7.9

Source: TSO (2005)


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7.1.2 Energy consumption in urban freight transport

Urban freight is more polluting than long distance freight transport. This is caused by the number of short trips and amount of stops for distribution within the city. Figure 7.1 shows that the fuel consumption increases strongly if the vehicle has to stop very often: with five stops on a distance of 10km the fuel consumption increases by 140%.

Figure 7.1 Infrastructure impact on fuel consumption

Source: Volvo truck corporation environmental affairs, June 2005 Urban distribution in Amsterdam costs, in relation to the other three cities, relatively the most fuel per 100 km. Table 7-3 presents the average fuel consumption per vehicle type for four Dutch cities.

Table 7-3 Average fuel consumption per vehicle type (liters per 100km)

Type of Vehicle Amsterdam Den Bosch Groningen Tilburg Van <1500kg 10 9 9.1 9.7 Van 1500-3500kg 21 15 8.9 15.8 Truck 3500-7500kg 27 20 20.4 24.5 Truck >7500kg 27 23 22.7 27.5 Truck and trailer >7500kg 29 N/A 26.3 N/A Articulated truck >7500kg 32 29 N/A 28.4

Source: several effectstudies

Table 7-4 Average fuel consumption per vehicle type (litres per 100km)

0

5

10

15

20

25

30

0 1 3 5 10Number of stops per 10 km

Fuel consumption index, %

100

136163

210240

Tractor / semi trailer combination, 40 tons


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Type of Vehicle Amsterdam Den Bosch Groningen Tilburg Van <1500kg 10 9 9.1 9.7 Van 1500-3500kg 21 15 8.9 15.8 Truck 3500-7500kg 27 20 20.4 24.5 Truck >7500kg 27 23 22.7 27.5 Truck and trailer >7500kg 29 - 26.3 29.0* Articulated truck >7500kg 32 29 - 28.4

Note: (*) only one respondent Source: effectstudies In France, freight transport uses 20% of all energy used for motorized traffic, see Table 7-5.

Table 7-5 Energy and emission figures in France

Energy and emission Indicator Energy consumption urban transport Kgep/inhabitant/day 1 to 1.4 Greenhouse effects Kg/inhabitant/day 3.1 to 4.4 Energy consumption Of motorised traffic 20% Share of goods transport in Marseille Deliveries/pickups and urban management

Trips for purchasing 16% 13%

Source: LET-Aria Technologie, Bilan Marseille, 2000 (Presentation Routhier)

7.1.3 Number of alternatively propelled or fuelled distribution vehicles

In order to reduce environmental impacts of emissions from diesel powered distribution vehicles, vehicles running on alternative fuels have been developed. These alternatives include hybrids (diesel and electric), CNG (compressed natural gas) and fuel cells. Figure 7-2 shows the NOx emissions from alternative technologies compared to diesel engines. Accurate statistics on the total number of alternatively fuelled distribution vehicles are available, however. In general the figures are low, but growing. At the moment, for instance in the city of Bologna about 1% of urban freight vehicles entering in urban areas are alternatively powered, in this case methane.


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Figure 7.2 Compared NOx emissions diesel and alternative technologies

Source: IVECO presentation by M. Lage BESTUFS Conference Amsterdam June 2005

7.2 Exhaust emissions

One of the major problems in cities caused by urban freight transport is the pollution from exhaust emissions. The result of the combustion of diesel fuel in an internal combustion engine of a vehicle is the production of gaseous emissions which include among others, Carbon Monoxide (CO), Carbon Dioxide (CO2), Nitrogen Oxides (NOx) and Particulate Matter (PM). Carbon Monoxide is a toxic gas which results from an incomplete combustion of diesel. High concentrations can be lethal, small concentrations can result in cardiovascular disorders and the corrosion of the respiratory tract. Carbon Dioxide is one of the gaseous pollutants that is a major contributor to the greenhouse effect. Nitrogen Oxides can be divided into two principal compounds: Nitrogen Monoxide (NO) and Nitrogen Dioxide (NO2). The NOx emissions of vehicles are emitted at a rate of about 95% as NO. The atmospheric reaction with oxidants like Ozone (O3) during the dispersion and transportation process produces significant NO2 concentrations. NO2 is of major interest in terms of health effects; the corrosion of the respiratory tract, while the NO shows no significant effects on human beings in the concentration levels normally observed in road transportation. However, the exhaust of NOx emissions will result in the souring which affects historic buildings in cities. Particulate Matter (PM) or particulates originate mainly from diesel and consist of a solid core of elementary carbon onto which a wide variety of organic compounds and oxides such as sulphates adhere. The human upper respiratory tract is able to stop particles above 10 µm with 100% efficiency. The efficiency however decreases with smaller particle sizes and is close to zero for particles of about 1 µm and they can therefore easily enter the human lung system. These sub micron particles exhibit a gaseous behaviour during the dispersion process in the vicinity of streets, but also tend to agglomerate into larger particles at the same time.

0

1

2

3

4

5

6

2002 2003 2004 2005 2006 2007 2008 2009 2010

Diesel i

Hybrid

EEV limits

Fuel cell

Euro 3

Euro 4

Euro 5

CNG IVECO stoich.


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7.2.1 Typical emission factors by vehicle type

In order to reduce heavy goods vehicle emissions, the European Union designed emission standards for all vehicles called the EURO norms. These standards indicate the legal limit of CO, NOx and PM emissions. The diesel standards for emissions from urban freight vehicles are listed in Table 7-6. In addition to these norms some countries have additional requirements. For instance, Sweden has made a mixture of 5% clean bio fuels in all the fuels obligatory. The Euro-6 norm, which can be expected by 2010, will include a 5,75 percentage of bio-fuels in all fuels.

Table 7-6 EU emission standards for freight trucks (g/kWh)

Law Introduction NOx PM HC CO R49.00 1982 18 - 3.50 14 Euro 0 1990 14.4 - 2.40 11.2 Euro 1 1993 8.0 0.36 1.10 4.5 Euro 2 1996 7.0 0.15 1.10 4.0 Euro 3 2001 5.0 0.10 0.66 2.1 Euro 4 2006 3.5 0.02 0.46 1.5 Euro 5 2009 2.0 0.02 0.46 1.5

Source: Volvo truck Corporation (2006) (Refnr. 20640/05-008)

The energy use of a truck is heavy influenced by the type of activity: in city traffic the energy use and emissions are significantly higher than on a transport corridor. The influence of the number of stops on a trip was already presented earlier. Table 7-7 shows the fuel consumption of a 18 truck in urban distribution with and without exhaust filter and a 40 ton truck a long haul trips.

Table 7-7 Fuel consumption and emission per 100 km

Indicator 18 ton truck urban distribution without filter

18 ton truck urban distribution with exhaust

filter

40 ton truck long haul without filter

Fuel consumption 22 liter 22 liter 31 liter CO2 57kg 57kg 81kg HC 9g 2g 25g CO 48g 4g 71g NO2 370g 370g 530g PM 4g 1g 6g

Source: Volvo FH How the environment is affected


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In some cities specific emission factors are available for different vehicle types. For instance, the average exhaust emissions of freight vehicles in Bologna (Italy) by type of pollutant are the following are shown in Table 7-8.

Table 7-8 Emissions factors by vehicle type and pollutant Bologna (g/tkm)

Vehicle type CO2 CH4 NO2 LDV 714 0.066 0.041 HGV 104 0.010 0.005 Total 134 0.013 0.007 Source: FFSS Amici della Terra (2002)

In Great-Britain a strong decline in the emission rate of Carbon Monoxide and particulates was seen in the last decade, see table Table 7-9.

Table 7-9 Index emission of HGVs (pre 1993 = 100)

HGV Year of manufactu-

ring

Carbon monoxide

Hydro carbons

Oxide of nitrogen

Particulates Carbon dioxide

Pre 1993 25 118 344 277 361 1993-1996 14 43 437 143 361 1997-2001 11 34 373 100 361

Rigid vehicles

from 2002 8 23 258 72 361 Pre 1993 29 100 969 403 591 1993-1996 40 107 1159 375 523 1997-2001 31 88 799 259 483

Articulated vehicles

from 2002 21 60 554 187 483

Source: TSO (2005)

7.2.2 Share of urban freight transport in exhaust emissions

The share of urban freight transport in exhaust emissions can be interpreted in two ways: − Emissions from freight vehicles compared to the total traffic − Emissions from freight vehicles in urban areas compared to all their activities (all roads) Emissions from freight vehicles compared to the total traffic In general the share of emissions from freight vehicles compared to the total traffic is about 20 to 30%,obviously dependent on the local situation. Table 7-10 shows the that total urban goods transport accounts for 14% of the vehicle kilometres, 19% of the energy use and 21% of the CO2 emissions. In addition, one should mention heavy goods vehicles travelling through the area, but which are not active in urban distribution. This accounts for 4% of the vehicle kilometres, 12% of the energy use and 10% of the CO2 emissions.


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Table 7-10 Share of the number of km per inhabitant each day (motorized*)

Urban traffic in Bordeaux Vehicle km (vkm) % vkm TOE14 % TOE CO2 (ton) % CO2

Total UGM 2,020,000 14% 222 19% 762 21% Through traffic (HGV) 550,000 4% 138 12% 364 10% Through traffic (cars) 1,100,000 7% 62 5% 165 5% Other (private car) 10,400,000 74% 735 64% 2 334 64% TOTAL BORDEAUX 14,070,000 100% 1 156 100% 3 626 100%

Source : LET, Aria Technologies, System consults, 2000. *excepted two-wheeled vehicles

Table 7-11 shows the part of each segment of traffic according to the vehicle type involved in the Urban Goods Movement in Bordeaux (excluding through traffic). According to the consumption, the part of the heavy vehicles (more than 3.5 t) is 44%, about the same as the part of the private car traffic for shopping (45%).

Table 7-11 Share of vehicles in the Urban Goods Movement, Bordeaux 1995

Activity vkm/day % vkm TOE/day CO2 (ton/day) % TOE

Shopping 1,407,000 69% 99 315 45% Total deliveries/pickups+ urban management 620,000 31% 123 447 55% Total UGM 2,020,000 100% 222 762 100%

Source : LET, Aria Technologies, Balance of Urban Goods Movement in Bordeaux, 2000, survey 1995

Table 7-12 shows the vehicle kilometres, energy use and CO2 emissions of cars and commercial vehicles in urban goods movements in Bordeaux in 1995. It clearly shows that heavy vehicles have a low share in kilometres, but a high share in energy use and emissions (bearing in mind that these figures represent the situation in 1995, which is before the introduction of the EURO-2 vehicles).

Table 7-12 Share of various commercial vehicles in the Urban Goods Movement, Bordeaux 1995

Vehicle type vkm/day % vkm TOE/day CO2 (ton/day) % TOE

Car 80,325 4% 6 18 3% LGV (<3.5 t) 202,280 10% 18 56 8% HGV (3.5-7.5 t) 27,067 1% 3 9 1% HGV (7.5-16 t) 104,182 5% 21 80 9% HGV (16-32 t) 123,353 6% 39 149 18% HGV (>32 t) 85,636 4% 36 135 16%

Source: LET, Aria Technologies, Balance of Urban Goods Movement in Bordeaux, 2000, survey 1995

Table 7-13 shows the share of urban freight transport in the total motorized traffic in three cities in France in 2000.

Table 7-13 Share of urban freight transport (deliveries, urban management, shopping trips) in the total motorized traffic

14 TOE: tons oil equivalent. The CO2 emissions comprise only the conversion in CO2 of the oil combustion but not the GES equivalent CO2 of other pollutants (CH4, NO2)


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Indicator Bordeaux Dijon Marseille Vehicle-km 13% 13% 19% Fuel consumption 15% 20% 24% CO 7% 9% 13% NO 15% 24% 35% HC 9% 12% 16% PM 2% 42% 32% SO2 18% 25% 44% CO2 15% 20% 26%

Source : LET, Aria Technologies Balance of Urban Goods Movement in Bordeaux, Marseilles, Dijon, 2000

Emissions from freight vehicles in urban areas compared to all their activities (all roads) In absolute values in Italy, the CO2 share of urban freight transport on total road transport emissions accounts for about 27%, with a peak of 35% considering LGV vehicles.

Table 7-14 CO2 emissions: share of freight urban transport in Italy

Vehicle type Total emissions Urban emission Urban shareLGV 9,483 3,356 35.4%HGV 26,869 6,280 23.4%Total 36,352 9,636 26.5%


Table 7-15 shows the share of urban goods transport in pollutant emissions in France.

Table 7-15 Share of goods transport in pollutant emissions in France

Indicator Share CO 13% NOx 37% HC 16% SO2 33% Particulates 48% CO2 27%

Source: LET-Aria Technologie, Bilan Marseille, 2000 (Presentation Routhier)


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7.3 Traffic, energy and emission ratios per inhabitant and per job

In order to understand the transport generation, it is informative to consider the urban freight transport related energy consumption and emissions. The average urban traffic fuel consumption per inhabitant and per job in Bordeaux would as shown in Table 7-16.

Table 7-16 Fuel consumption per inhabitant and job each day

Bordeaux Fuel consumption

(GOE/inhabitant)

Fuel consumption

(GOE/job)

Shopping (by car) 132 297 Goods Deliveries + management 163 368 Other (private car) 812 1,827 Total 1,107 2,492

Source : LET, Aria Technologies Balance of Urban Goods Movement in Bordeaux, 2000

The conversion in CO2 (*3.18) gives the emission ratios in Table 7-17.

Table 7-17 CO2 emissions per inhabitant and job per day

Bordeaux CO2 / inhabitant (g) CO2 / job (g)

Shopping (by car) 420 944

Deliveries + management 518 1,170 Other (private car) 2,582 5,810 Total 3,520 7,925

Source : LET, Aria Technologies Balance of Urban Goods Movement in Bordeaux, 2000

7.4 Noise

Urban freight deliveries produce a significant amount of noise in a city, not only from the engine and tyre noise, but also the sound of to (un)loading of goods can be very obtrusive.

7.4.1 Noise levels driving truck

Depending on different traffic conditions and type of roads, on average the emissions of LGV and HGV are 2-10 times higher than passenger cars (a weight of 2 indicates +3 dB on average levels. The measurement has lead to the definition of average conversion factors of noise emissions by vehicle types, assuming noise emissions by car equal to 1, as described in the following table.


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Table 7-18 Different weight of noise emissions by vehicle type

Type of vehicle Weight Passenger Car 1 Motorcycles, moped 1.7-3 LGV and HGV 1.5-10 Bus 10-40

Source: ARPAT (1998)

Table 7-8 shows that the part of the road network exposed to a noise of more than 65 dB(A) is 12% without UGM and 25% with the whole traffic (ADT - Private vehicles + UGM + FTT). During the UGM peak hours, the ratio varies from 13% to 32 % (MPH).

Table 7-19 Distribution of the distances subject to different noise levels in Bordeaux according to the traffic

< 55 dB(A) 55-65 dB(A) >65 dB(A)

ADT15 MPH16 EPH17 ADT MPH EPH ADT MPH EPH

All Traffic 35% 5% 6% 39% 62% 60% 25% 32% 34% Private Vehicles 34% 28% 12% 53% 59% 72% 12% 13% 16% UGM 48% 38% 45% 39% 42% 45% 14% 21% 11% FTT18 47% 47% 43% 39% 38% 45% 14% 15% 12%

Source: LET, Aria Technologies, System's Consult, urban freight surveys in Bordeaux, Marseilles, Dijon, 1995-97

The city of Florence has developed a systematic measurement of noise levels arising from transport activities (ARPAT, 1998). The following table shows the average noise emissions per vehicle type. As can be seen from this table, LGV vehicles have a more or less similar value.

Table 7-20 Noise levels by vehicle type in the city of Florence

Type of vehicle dB(A) Motorcycles, moped 61.1 Passenger car 58.8 LGV 60.7

Source: ARPAT (1998)

The noise emission limit values in Germany have been decreased again in the last years. The reduction of the values for passenger cars, truck and busses in the EG is mentioned in Table 7-21. A passing truck produces about 90 dB(A) at 7.5m distance19. Table 7-21 Noise emission limit values in Germany Type of vehicle 1972 1982 1988/90 1995/96

15 ADT: Average day traffic 16 MPH: Morning peak hour 17 EPH: evening peak hour 18 FTT: Freight through traffic (heavy goods vehicles transit) 19 http://elib.uni-stuttgart.de/opus/volltexte/2001/911/pdf/Diplomarbeit_Peter_Schick.pdf


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Passenger car 82 dB(A) 80 dB(A) 77 dB(A) 74 dB(A) Bus 89 dB(A) 82 dB(A) 80 dB(A) 78 dB(A) Heavy goods vehicle 91 dB(A) 88 dB(A) 84 dB(A) 80 dB(A)

Source: …

7.4.2 Noise levels loading and unloading truck

Data on noise levels of loading and unloading trucks are hard to get by. Within the Dutch PIEK (or PEAK) program a whole range of quite distribution equipment has been developed. Table 7-22 shows the noise levels of Table 7-22 Noise factors in urban distribution (dB(A) at 7.5m distance) Noise generator Noise level Drivers behaviour (radio, jelling etc.) variable Slamming with doors ca. 74 Driving and maneuvering the truck 67 – 83 Controlling the tailboard 65 – 92 Using pallet truck within the vehicle 74 – 85 Transport cooling 70 – 78 Using pallet truck on the pavement 72 – 81 Attach load locks Attach/use pallet trucks 77 – 82 Using pallet trucks ca. 75 Mobile shop trolley 53 – 77

Source: www.piek.org


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8 SAFETY

Safety is a very important topic in urban freight transport. Goods vehicles are seen as hazardous entities in the urban environment. The level of detail of the main European safety statistics (CARE database) is insufficient to provide a fully accurate view, however. From these statistics it’s is impossible to determine how many accidents and fatalities involving urban freight transport take place. This chapter however presents an overview of available data in statistics, complemented with specific national and local data.

8.1 Fatalities, accidents and casualties in urban freight transport

Figure 8.1 presents the European statistics on fatalities, accidents and injured in the EU.

Figure 8.1 Evolution of fatalities, accidents and injured EU

Source: CARE (EU road accidents database) or national publications, European Commission / Directorate General Energy and Transport


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Figure 8.2 shows the number of fatalities inside urban areas by person class (drive, passenger and pedestrian). As expected, a large number of the fatalities are pedestrians.

Figure 8.2 Fatalities by person class inside urban areas

Source: CARE database

Figure 8.3 shows the fatalities inside urban areas by transport mode. This shows that goods vehicle drivers have a very low share.

Figure 8.3 Fatalities by transport mode inside urban areas


The most relevant statistics in urban freight - fatalities, accidents and injured involving (i.e. not just driving this vehicle) goods vehicles – is not published in European statistics. The next paragraph presents some studies and national statistics.

8.2 Involvement of freight vehicles in accidents

The share of fatalities involving goods vehicles differs by country. Figure 8.4 shows the values registered by IRTAD for 2003 for 6 EU countries. The share of light urban freight vehicles (less than 3.5t) ranges from 3.5% in Finland to 17% in the Netherlands, while most countries have a


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share between the 5 and 10%. The share of heavy urban freight vehicles (more than 3.5t) ranges from 2.5% in Finland to 15% in Austria, while here most of the countries have a share between the 10 and 15%.

Figure 8.4 Fatalities from accidents involving goods vehicles in 2003

Source: IRTAD

A specific study on accidents with personal injuries involving goods vehicles in Germany using the IRTAD database shows a decline in the involvement of heavy goods vehicles, see Figure 8.5.

Figure 8.5 Accidents with personal injuries involving goods vehicles (Germany)

Source: IRTAD


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In the Netherlands, the involvement the involvement of freight transport in accidents, fatalities and injuries is published, see Table 8-1. This shows the involvement ranges between 18 and 30%. This value relates to all traffic, not just in urban areas.

Table 8-1 Involvement of freight transport in accidents, fatalities and injuries in the Netherlands

Severity Indicator 2001 2002 2003 Total road transport 187.204 171.689 159.278 Total freight transport 45.201 39.970 36.838

Accidents

Share freight transport 24,1% 23,3% 23,1% Total road transport 993 987 1.028 Total freight transport 287 243 303

Fatalities

Share freight transport 28,9% 24,6% 29,5% Total road transport 11.029 11.018 10.596 Total freight transport 2.071 1.947 1.922

Injured

Share freight transport 18,8% 17,7% 18,1%

Source: RWS-AVV, Goederenvervoermonitor 2005

In Rotterdam, the share of truck traffic in all accidents is relatively low: between 1 and 3% of all accidents.

Table 8-2 Accidents involving truck traffic in Rotterdam

Indicator 2000 2001 2002 All accidents 211 159 164 Accidents involving truck traffic 6 5 2 Share of truck traffic 2,8% 3,1% 1,2%


The figures on Utrecht (NL) show that the involvement of trucks in accidents is about 20 to 30%. Most of the accidents only constitute material damage. In a limited number of cases people got injured. Over several years, no fatal accidents involving truck traffic occurred.

Table 8-3 Involvement of truck traffic in accidents in historical centre of Utrecht

Year Fatal Injured Only material damage Share of total 1999 0% 2000 0% 16,7% 30,84% 29,71% 2001 0% 3,3% 23,21% 20,87% Total 1999-2001 0% 14,5% 28,28% 26,75%


Table 8-4 Participation of truck traffic in accidents outside historical centre of, Utrecht

Year Fatal Injured Only material damage Share of total 1999 0% 16,7% 19,8% 19,0% 2000 0% 10,3% 19,3% 17,9% 2001 0% 13,5% 18,7% 17,1% Total 1999-2001 0% 13,7% 19,4% 18,2%



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8.3 Road user type: cyclists, pedestrians, car drivers

Table 8-5 shows an analysis of accident opponents in heavy goods vehicle accidents.

Table 8-5 Persons killed in accidents per involved vehicle group in Austria, 2001

Accident opponent All Lorry <3,5t

Lorry >3,5t

Lorry <3,5t+Lorry >3,5t

Lorry,>3,5t + Road tractor

Agricultural tractor 15 0 1 1 1 Bus or Coach 14 2 0 2 0 Car or Taxi 570 24 35 58 50 Heavy goods vehicle

14 3 8 8 14

Lorry <3,5 tonnes 25 25 5 25 9 Moped 36 4 1 5 1 Motor cycle 108 3 8 11 10 Other 4 0 0 0 0 Pedal cycle 55 3 13 16 17 Sum: 841 64 71 126 102

Source: Case study Heavy goods vehicle accidents, based on CARE data

The number of pedal cycle accidents associated with goods vehicle collisions has more than halved between 1990 and 2004.

Figure 8.6 Pedal cycle casualties associated with goods vehicle collisions in London

Source: Road safety unit, TfL


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Pedestrian accidents: it is assumed that at a relative high share of the pedestrian accidents occur in urbanized areas. Figure 8.7 shows a rate of pedestrian accidents per 109 inhabitants of 1.0 for Sweden and 5.4 for Portugal.

Figure 8.7 Rate of pedestrian accidents per 10^9 inhabitants

Source: Evolution and Typology of Accidents and Severity (Centre d’Études Techniques de l’Équipement du Sud-Ouest (C.E.T.E.), based on CARE database

The total number of pedestrian fatalities gradually decreased over time, see Figure 8.8.

Figure 8.8 Total number of pedestrian fatalities



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SOURCES AND REFERENCE LIST

The following sources and references have been used:

Authorities, companies and institutes

• Autorità per I servizi publici locali (1999) • CBS, national statistics bureau of the Netherlands • Central Statistics Office, Ireland • Comune di Milano, Italy • CONFETRA • Department for Transport, UK • European Environmental Agency • European Commission, CARE (EU road accident database) • FFSS Amici delle Terra (2002) • European system of Social indicators (EUSI) • EUROSTAT • IRTAD • ISTAT • Ministry of Transport and Infrastructure, Italy • Ondaverde (2003) • ONS (2004) • PIEK, www.piek.org • PTV • Regione Emilia Romagna • SCO • Transport for London, UK • TfL, Road safety unit • TNO, national research institute of the Netherlands • TSO • Vito Maria Contursi, Comunedi Genova (2004) • World Bank

Presentations at BESTUFS workshops and conferences

• Jensen, H. Bestufs workshop, Maribor • Lage, M. IVECO (June 2005), presentation BESTUFS Conference Amsterdam • Routhier, J.L State of the art of data collection for urban freight transport in France,

presenation at BESTUFS modeling round table


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Reports and websites

• BRO (2000), Effectmeting Tilburg • Census of population (2002), volume 1, population classified by area CSO, Ireland • Centre d’ Études Techniques de l ‘Équipement du Sud-Ouest (CETE), Evolution and

Typology of Accidents and Severity, base don CARE database • Complete road traffic survey of Budapest and the agglomeration (1999) • CLM Definition for Logistic Council of Logistics Management, OAK Book (2001),

http://www.clm.1.org • Dataverzameling Stedelijke distributie, TU Delft, NL • Department for transport (2005), transport statistics Great-Britain • DETR (1998), Focus on Freight, publ. The Stationary office • Effects of Freight Movements in Winchester • European Logistics Association, ATKeearney (2004), Differentiation for performance

excellence in logistics 2004 • Inner urban freight transport and city logistics, EU funded Urban Transport Research

Project, Results (2003), www.eu-portla.net • Kraftfahrzeugverkehr in Deutschland (2002), Kontinuierliche Befragung des

Wirtschaftsverkehr in unterschiedlichen Siedlungsräumen, www.verkehrsbefragung.de • LET (2000), Aria Technologie, According to urban freight surveys results in Bordeaux,

Marseille, Dijon 1995-1997 , Systems consult • LET, driver survey (2255 vehicles) • LET, Model Freturb applied on the town of Marseille • LET (1995), Urban Freight Survey in Bordeaux • O’Mahony, M. Finlay, H. and Finnegan, C. (November 2004), Sustainable Freight

Distribution in a Historic Urban Centre • Report: A framework for considering policies to encourage sustainable urban freight traffic

and goods/service flows • RWS-AVV, the Netherlands : Goederenvervoer (2003) • RWS-AVV, the Netherlands: Goederenvervoermonitor (2005) • Schick, P. (2001),

http://elib.uni-stuttgart.de/opus/volltexte/2001/911/pdf/Diplomarbeit_Peter_Schick.pdf • Tesco, Internet shopping service creates over 80 new jobs at Tesco IReland in the first year

of operation, press release, www.tesco.ie • TLN (2003), Transport in cijfers • Volvo: FH How the environment is affected • Volvo: Truck corporation environmental affairs, (June 2005) • http://wko.at/bsv/internet/Unternehmen_Beschäftigte_EU15.htm

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