chapter 7 (final)

National Highways Authority of IndiaPreparation of Feasibility Study and DPR of Kerala Border to Thrissur Section of NH-47 (Km 182.00 to Km 270.00) Draft Feasibility Report

7.0 TRAFFIC SURVEYS, ANALYSIS AND FORECASTS

7.1 Introduction

In planning and design of a highway an appreciation of the traffic expected to use the highway is of importance to assess the capacity requirements, pavement design, identify present and likely future traffic problems and to device suitable remedial measures and evolve appropriate designs. It is important for toll roads to assess the revenue from toll collections on the Project. As part of this study, a systematic methodology has been followed to assess the characteristics of the traffic on the project road.

Kerala Border to Thrissur of NH-47 is an important link providing the most direct route from Tamilnadu, Karnataka , Andhra Pradesh and other parts of India to Kerala. In view of this, it is considered part of North-South corridor by NHAI for capacity augmentation to 4-lanes.

The data for the study has been collected from field surveys as well as from secondary sources. Vehicle and traffic growth rates have been correlated to the demographic and economic growth trends of the state to establish future growth rates. The link-wise travel demand has been projected as per the growth rate estimates and a traffic diversion procedure has been used to derive traffic on different links of the road network.

The details on traffic surveys and results from its analysis are presented in this chapter followed by the derivation of traffic growth rates and traffic diversion analysis to estimate travel demand on the project road.

7.1.1 Objectives

The primary objectives of this traffic study are to:

assess the characteristics of traffic movement on the project road collect historical data on traffic growth in the project road estimate the future growth rate . determine the travel pattern as well as type and weight of

commodities carried by goods vehicles. determine the turning movements of traffic at road intersections identify traffic bottlenecks and the need for service roads bypasses

to congested locations determine the spectrum of axle loads and the vehicle damage

factor of different types of commercial vehicles.

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7.1.2 Surveys

To fulfill these objectives, the following traffic surveys were carried out

Traffic volume count surveys at 4 locations for 7 days Origin and destination surveys for 24 hours at two locations Turning movement surveys for 12 hours at 10

Junctions/Intersections. Journey time and delay studies over the entire project road Pedestrian movement surveys at 3 locations Traffic Accident Data Collection Axle load surveys at 2 locations for 48 hours

The Locations of the surveys conducted shown in Figure 7.1.

7.2 Classified Traffic Volume Counts

7.2.1 Objectives

The intensity of traffic flow at any given section of the road forms the basis for determining its spatial and structural design requirements. The collection of traffic data thus assumes utmost significance in the development of any road project. In order to assess the Average Daily Traffic at any section of road, classified traffic counts are carried out for a continuous period of 7 days to average any variation in the short term.

7.2.2 Methodology

The project road was divided into four homogeneous sections based on preliminary reconnaissance studies in such a way that the traffic intensity within a particular section will be more or less uniform. Classified traffic counts were organised at one selected location in each section. The locations of 7-day continuous traffic count stations are Table 7.1. Data on the number of vehicles of different categories moving along the road in both directions are noted by enumerators specially trained for this purpose. The vehicles are broadly classified into motorised and non-motorised vehicles, which are further sub divided into specific categories of vehicles. The traffic counts are generally conducted in accordance with IRC SP19-2001. However, the groupings of vehicles are further split to reflect the present day traffic pattern on the project road.

Table 7.1 Classified Traffic Count Locations

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Homogeneous SectionCount

No.Location Survey dates

HS-1 Kerala/TN Border to Palakkad (Km 182/0 to Km 204)

SC/1Km

191/000

24th July – 30th July 2004

HS-2 Palakkad (Km 204) to Alathur (Km 226)

SC/2Km

210/600

15th July – 21st July 2004

HS-3 Alathur (Km 226) to Vadakancherry (Km 239)

SC/3Km

237/000

21st July – 27th July 2004

HS-4 Vadakancherry (Km 239) to Thrissur (Km 270)

SC/4Km

263/100

13th July – 19th July 2004

7.2.3 Results and Analysis

The summary of ADT, in terms of vehicles and PCU at all the four count locations is given in Table 7.2. The details pertaining to hourly and day wise distribution of average daily traffic at the seven count locations are given in Annexure VII.

It may be noted that there was a general strike of state private bus operators over entire kerala state from 26th to 30th July 2004, there was a fall in Mini Bus and Bus traffic during these days. It is therefore considered to justified to take the 5 days average from 21st to 25th for Mini bus and Bus traffic at Km 237/000, and 2 days average from 24th to 25th at km 191/000.

From Table 7.2 and Annexure VII, the following can be observed.

The Average Daily traffic on the project road ranges between 10315 vehicles in Section 1 to 14486 vehicles in section 4. The traffic intensity in terms of Passenger Car Units ranges between 17204 PCU’s in section 1 to 25365 PCU’s in section 4.

The Average Daily Traffic (ADT) is close to 17200 PCU’s in Section 1, 18726 PCU’s in section 2, 19604 in section 3, and about 25365 PCU’s in Section 4. The traffic increases from Palakkad

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Town and Vadakanchery due to traffic joining from Southern Parts of Tamlnadu.

The traffic indicates that significant traffic generators along the project road and the traffic, is predominantly high in the section 2, 3 and 4.

There are about 3800 trucks observed in Section 3 and 4 compared to about 2900 in Section 1 and 2, about 900 higher. Light Goods vehicle range from 564 to 924.

The passenger car traffic ranges from 3085 in Section 2 to 4216 observed in Section 4. The bus traffic is also maximum in Section 4 at 1477, followed by 1088 in Section 2

Two-wheeler traffic ranges from 2226 in Section 4 to 2057 in Section 1.

The slow moving traffic is slightly high on Section 1, otherwise it ranges from 29-142 vehicles. Out of the total slow moving traffic, cycle traffic is significant which is about 96% of the total slow moving traffic.

The Day wise variation of traffic mode wise at Km 191/000 is indicated in Figure 7.2. The Day wise Variation of Traffic in PCUs is indicated in Figure 7.3. Details of mode wise hourly distribution of different categories of vehicles at Km 191/000 are indicated in Figure 7.4. The Hourly Variation of Traffic is indicated Figures 7.5. Composition of traffic at Km 191/000 is indicated in Figure 7.6.Weekly variation of traffic in terms of Total vehicles & total PCU is given in Figure 7.7.

Similarly for other locations i.e at Km 210/600, Km 237/000 & Km 263/100 all the above details are given in Figure 7.8 to 7.13, Figures 7.14 to 7.19 & Figure 7.20 to 7.25 respectively.

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7 Day CVC Locations

Turning Movement Survey Locations

O-D Survey/Axle Load Survey Locations

Figure 7.1 Traffic Survey Locations

Pedestrian Count Locations


Table 7.2

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Figures 7.2 to 7.4

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Figures 7.5 to 7.7

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Figures 7.8 to 7.10

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Figures 7.11 to 7.13

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The traffic intensity at different count locations is presented in Figure 7.26.

7.2.4 Hourly Variation and Peak hour factor

Analysis has been carried out to understand hourly variation and peak hour traffic characteristics. The hourly distribution of traffic at various count locations is illustrated in Annexure VII.

The highest and lowest hourly volumes of traffic in different sections of the project road are indicated below in Table 7.3.

Table 7.3: Highest and Lowest traffic volume in different sections.

Highest hourly traffic volume Lowest hourly traffic volumeMotorisedVehicles

Non-motorisedVehicles

PassengerCar Units

MotorisedVehicles

Non-motorisedVehicles

PassengerCar Units

1 634 36 939 214 0 4952 639 14 997 284 0 5973 628 9 1068 236 0 6104 915 15 1502 243 0 609

The hourly distributions of motorised and non-motorised vehicles as well as passenger car units at different count stations are summarized in Figures 7.27, 7.28 and 7.29.

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Proj

ect

sect

ion


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The Peak Hour Factor (PHF) (defined as the ratio between the number of vehicles counted during the peak hour to the total vehicles counted in a day) calculated at various count locations is presented in Table 7.4.

Table 7.4 Peak Hour Factors Observed at Various Count Locations

S. No.

Survey Location RoadPeak Hour

Volume (PCU)PHF

(in %)Peak Hour

(Hrs.)1 Km 191/000 SC/1 939 5.46 17:00-18:002 Km 210/600 SC/2 997 5.33 17:00-18:003 Km 237/000 SC/3 1068 5.45 18:00-19:004 Km 263/100 SC/4 1502 5.92 18:00-19:00

From the table and illustrations in the Annexure, it can be observed that the peak hour traffic volume at different count stations range between 5.33% in section 2 to 5.92% in section 4, the mean peak hour factor being 5.54 % at different survey locations.

7.2.5 Average Annual Daily Traffic

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Monthly Toll Collection Revenues (which is the reliable source to assess the monthly variation during the season) at Peruvamoozhy Bridge(Kottayam Nedumbassry Road M.D.R) was collected for the last one year; this data was used for calculation of seasonal factors. The AADT was arrived at by multiplying these factors with ADT obtained from the traffic volume count. The variation of calculated seasonal factors is given below.

Seasonal Variation Factors at Peruvamoozhy Bridge

Month Seasonal Factors

Jan 1.14

Feb 1.07

Mar 0.98

Apr 0.94

May 0.92

Jun 1.03

July 1.07

Aug 1.19

Sep 1.20

Oct 1.28

Nov 1.23

Dec 0.54

As the classified traffic volume counts was conducted in the month of July, therefore for calculation of AADT, value of seasonal factor has been taken as 1.07.

AADT at different count stations (After applying seasonal factors) is given in Table 7.5.

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Insert Table 7.5

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7.3 Origin Destination Survey

7.3.1 Objective

The objective of the Origin-Destination (O-D) survey is to gather information regarding travel characteristics of different users on the project road. Results of the O -D surveys are used to describe the user characteristics, both of passengers and goods vehicles, such as distribution of local and through traffic as well as commodity type and weight of goods carried by trucks. This information is also useful in determining the need for bypasses for heavily built up urban sections along the project road.

7.3.2 Methodology

The Origin-Destination survey as well as Commodity movement survey was carried out adopting Roadside Interview method as detailed in IRC 102-1988. The survey was carried out for passenger and commercial vehicles for 24 hours at two locations. It was considered that two O-D survey locations one each in Section 1 and Section 4, i.e., would predominantly cover O-D movements on the Project Road that are of interest to the project. Details of origin-destination surveys conducted as part of this study are presented in Table 7.6.

Table 7.6 Origin-destination Survey Details

Homogeneous Section Count No. LocationHS-1 Kerala/TN Border to Palakkad (Km 182/2 to Km 204)

SC/1 Km 191/000

HS-4 Vadakancherry (Km 239) to Thrissur (Km 270)

SC/4 Km 263/100

Roadside Interview method, as detailed in IRC: 102-1988, was used for O-D survey. The survey was carried out for both passenger and goods vehicles for 24 hours (in both directions) on a working day. Trained enumerators under the supervision of Transport Planners collected the trip characteristics.

The O-D survey elicited passenger mode characteristics like origin and destination, occupancy, trip purpose and length of trip by mode type. For goods modes, the survey elicited characteristics like origin and destination, commodity type and length of trip.

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7.3.3 Zoning

The entire country was divided into 38 zones in such a way that the characteristics of interzonal and intrazonal trips could be clearly analysed and their influence assessed on the project road. The details of zones are given in Annexure VIII.

With respect to goods vehicles, the types of goods carried were broadly grouped into 8 categories, plus empty trucks.The commodity groupings are as follows:-

1. Food Grains/Agriculture Products2. Perishable Goods3. Petroleum/Chemical products4. Building Materials5. Engineering Goods6. Garments7. Parcel Lorries8. Others9. Empty

Sample Size

The sample size for each category of goods vehicles at both locations are summarised in Table 7.7. As can be seen in the table, the sample size for various types of goods vehicles ranges between 15% and 35%.

Table 7.7 Sample Size for Various Types of Goods Vehicles

S. No. Vehicle Type OD Station (Km 191/000) OD Station (Km 263/100)

Number of Vehicles

Interviewed

Sample Size(in %)

Number of Vehicles

Interviewed

Sample Size(in %)

1 LCV 86 15.19 160 17.32

2 2-Axle Truck 434 21.60 581 18.23

3 3-Axle Truck 125 21.48 144 18.07

4 Multi-Axle Vehicle 79

36.7434 21.12

Total 724 21.47 919 18.13

The sample size for each category of passenger vehicles is summarised in Table 7.8. As can be seen in the table, the sample size for passenger vehicles ranges between 15% and 20%.

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Table 7.8 Sample Size for Various Types of Passenger Vehicles

S. No. Vehicle Type OD Station (Km 191/000) OD Station (Km 263/100)

Number of Vehicles

Interviewed

Sample Size(in %)

Number of Vehicles

Interviewed

Sample Size(in %)

1Passenger Car and Jeeps

496 15.32 830 19.95

7.3.4 OD matrices

The OD matrix for goods and passenger vehicles observed at Km 191/000 and Km 263/100 OD Station’s are given in Annexure VIII.

Trip Distribution

The OD matrices derived from the OD data collected at both locations are combined into one matrix to obtain OD matrix for the project. The combined OD matrix is analysed to assess the travel pattern of both goods and passenger vehicles. The analysis of the travel pattern reveals the following:

Palakkad, Coimbattore, other parts of Tamilnadu, Karnataka, Andhra Pradesh, Maharashtra, West Bengal, and Delhi, contribute most of the goods trips originating/terminating from/to the east of Project Road. Similarly, Thrissur, Ernakulam, Kottayam, Quilon, Trivendrum Kozhikode and Western Karnataka (Mangalore) contribute most of the traffic originating or terminating from/to the West of Project Road.

Palakkad, Coimbattore, Pollaci other parts of Tamilnadu, Karnataka, Thrissur, Ernakulam, Gurivayur, Alathoor, and Vadakanchery are major generators of passenger traffic.

Of the total goods traffic observed on the project road, 12% generates (including both originating or terminating traffic) at Coimbattore city, 8% generates in Palakkad, 12% in Thrissur, 19% at Ernakulam, 4% in Kottayam and Quilon districts, 17% at other parts of Tamilnadu, 4% in Karnataka 3% in Andhra Pradesh, 2.5 % in other parts of Country and 18.5% other Parts of Kerala State.

Of the total Passenger traffic observed on the project road, about 19% generates (including both originating or terminating traffic) at Coimbattore City, 16% generates in Palakkad, 17% in Thrissur, 10% at rnakulam, 2.5% in Kottayam and Quilon districts, 5% at other parts of Tamilnadu, 0.5% in other parts of Country, 30% in other parts of Kerala.

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The summary of the contribution of the major generating regions of both goods and passenger traffic estimated based on the travel pattern estimated under the combined OD matrix is given in Table 7.9.

Table 7.9 Percentage Contribution of Trips by Various Regions

Goods Trips

Vehicle Type

Region-wise Contribution of Trips (in %)

Kerala Tamilnadu KarnatakaAndhra Pradesh Others

LCV 74.86 22.06 2.43 0.67 0.002-Axle Truck 61.94 29.50 4.52 1.28 2.773-Axle Truck 50.99 32.09 2.87 12.09 2.27

Multi-Axle Truck 51.86 25.76 12.92 3.43 6.03Total 61.98 28.43 4.26 3.00 2.34Passenger Trips

Vehicle Type

Region-wise Contribution of Trips (in %)

Kerala Tamilnadu KarnatakaAndhra Pradesh Others

Passenger Cars and Jeeps 73.71 25.81 0.61 0.14 0.00

From the above table it can be seen that the observed goods traffic is generally a mix of short and long distance traffic whereas most of the passenger traffic generates within the Kerala and Tamilnadu States.

7.3.5 Lead Distribution of Goods Vehicles

Figure 7.30 presents the lead distribution for goods vehicles at both the locations combined. As can be observed, around 12 % of vehicles have a lead of less than 50 km, 11% have a lead between 51-100 km, 21% vehicles have a lead between 101-200 km, 20% have a lead in the range of 201-400 km, 23% have a lead in the range of 401-800 km, 8.5% have a lead in the range of 801-1600 km, 4% have a lead in the range of 1601-3200 km and 0.5% vehicles have lead of more than 3200 km. The goods traffic therefore is a mix of short and long distance traffic with a higher percentage of long distance traffic.

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Characteristics of good vehicles are given in Table 7.10.

7.3.6 Lead Distribution of Passenger Cars

Figure 7.31 presents the lead distribution for passenger Cars at both the locations combined. As can be observed, around 30 % of vehicles have a lead of less than 50 km, 28% have a lead between 51-100 km, 21% vehicles have a lead between 101-200 km, 12% have a lead in the range of 201-400 km, 4% have a lead in the range of 401-800 km, 2% have a lead in the range of 801-1600 km, 2% have a lead in the range of 1601-3200 km and 1% vehicles have lead of more than 3200 km. The goods traffic therefore is a mix of short and long distance traffic with a higher percentage of short distance traffic

Characteristics of Passenger Cars are given in Table 7.11.

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Table 7.10

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Table 7.11

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7.4 Willingness To Pay (WTP) Surveys

7.4.1 For Car User

WTP Survey for Truck Operators has been conducted at 2 locations along with the O-D Survey. Response of car user for various toll levels has been recorded. The survey has been conducted on random sample basis, for duration of 24 hours on normal days. The findings of the survey are reported in Table 7.12

Table 7.12 WTP for Car Users

WTPAcceptence (%)

0.60 Rs/Km 6.20.45 Rs/Km 15.10.30 Rs/Km 31.40.15 Rs/Km 39.40.0 Rs/Km 7.9Total 100.0

7.4.2 For Truck operators

WTP Survey for Truck Operators has been conducted at 2 locations along with the O-D Survey. Response of Truck Operators for various toll levels has been recorded. The survey has been conducted on random sample basis, for duration of 24 hours on normal days. The findings of the survey are reported in Table 7.13

Table 7.13 WTP for Truck operators

WTPAcceptence (%)

2.0 Rs/Km 1.801.5 Rs/Km 9.401.0 Rs/Km 51.700.5 Rs/Km 31.400.0 Rs/Km 5.70Total 100.00

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7.5 Pedestrian Survey

7.5.1 Objective

Pedestrian surveys are intended to collect data on the intensity of pedestrian movement at specific locations of significant pedestrian activity with a view to identifying and providing the type and extent of pedestrian facilities needed at these locations.

7.5.2 Methodology

Three locations of high pedestrian activity were chosen for the study. All these locations are within the village and Towns. These are i) Km 203.600 – Koothupattaii) Km 203.900 – Chandra Nagar Bus Standiii) Km 204.600 – Manoothy

The details of pedestrians crossing the Project road at the mid block sections Survey was conducted between 8.00 am to 8:00 pm. The data has been tabulated and analysed in order to decide the highest intensity of pedestrian movement at these locations.

The details of pedestrian crossings at different study locations are indicated in Table 7.14 and conflict levels at different study locations are indicated in Table 7.15.

Table 7.14 Peak hour pedestrian movements across the Project road

Location Morning Peak Hour

Pedestrian Crossings

Evening Peak Hour

Pedestrian Crossings

Koothupatta Km 202.6 8:00-9:00 192 18:00-19:00 206Chandra Nagar Bus Stand Km 203.0

10:00-11:00 99 18:00-19:00 106

Mannoothy Bus Stand Km 266.3

8:00-9:00 655 18:00-19:00 913

Table 7.15 Cross Pedestrian Traffic and Conflict Level

IndicatorKoothupatta

Km 203

Chandra Nagar Bus Stand Km

204.5

Mannoothy Bus Stand Km 266.3

Peak Hour Pedestrian Traffic

(P)206 106 913

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Fast Veh. During Pedestrian Peak

(V)1739 2562 1757

Present Conflict Level (PV2/2x10^8)

3.11 3.48 14.09

7.5.3 Analysis of study data

The intensity of pedestrian activity is very high and is sufficient enough to warrant provision of a pedestrian underpass. Also the pedestrian crossings are spread within the Built up areas. Depending on location of local crossings, pedestrian sub way may be provided at suitable location to avoid indiscriminate crossings and providing safety.

7.6 Intersection Turning Movement Survey

7.6.1 Objective

The objective of the turning movement survey is to determine the directional movement of traffic in order to assess the measures required to improve the junction capacity and to regulate safe traffic movement.

7.6.2 Methodology

A total of 8 intersections were selected for conducting turning movement studies. Studies were carried out at the following intersections:

1. Km 202/800 - 3 arm - Pollachi Road meeting with NH 47 2. Km 203/800 - 3 arm - Palakkad Bypass Starting Point 3. Km 216/400 - 4 arm - Thiruvallamali Chitoor Road

Crosses with NH-474. Km 228/000 - 4 arm - Alathoor Bypass Starting Point.5 Km 230/300 4 arm - Alathur Mudiyagaon Road Crosses

with NH-476. Km 231/775 - 3 arm - Alathoor Bypass Ending Point 7 Km 239/000 3 arm - Vadakanchery Bypass Starting

Junction8. Km 240/200 - 4 arm - Vadakanchery Padoor Road Crosses

with NH-47 9. Km 240/900 - 3 arm - Vadakanchery Bypass Ending Point10. Km 268/200 - 3 arm - Thrissur Bypass Starting Point

The directional traffic counts were carried out for 12 hours between 8 am and 8 pm at 15 minutes intervals.

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7.6.3 Analysis of Turning Movement study

The peak hour flow diagrams are given in Annexure IX. As per IRC:93-1985 (“Guidelines on Design and Installation of Road Traffic Signals”) the traffic at intersections will require time separation, i.e., signal control, when the major road flow is more than 800 vehicles per hour (both directions) and the minor road flow is more than 250 vehicles per hour (one direction) for each of any 8 hours of an average day.

Similarly, as per IRC:92-1985, traffic will require space separation, i.e., grade separation, when the total peak hour flow at the intersection is more than 10,000 pcu/hr. Junctions that do not warrant the above two types of control will require priority control.

i. Km 202/800 – To PollachiIt is a three-arm junction, with two arms being NH 47 and the third one Road leading to Pollachi. The peak hour traffic at this junction is 1724 veh/hr on major road (all directions) and 304 veh/hr on minor road. The total pcu at this junction during peak hour is 2489 pcu/hr. The traffic on major arm and minor arm satisfies the IRC specifications for signalisation, assuming an average growth rate of 8.5% p.a. the junction will require grade separation by the year 2022.

ii. Km 203/800 – To Palakkad TownIt is a three-arm junction, with two arms being NH 47 and the third one Road leading to Palakkad Town. The peak hour traffic at this junction is 2240 veh/hr on major road (all directions) and 1190 veh/hr on minor road. The total pcu at this junction during peak hour is 3430 pcu/hr. The traffic on major arm and minor arm satisfies the IRC specifications for signalisation, assuming an average growth rate of 8.5% p.a. the junction will require grade separation by the year 2016.

iii. Km 216/400 – To Thiruvallamalai/ChittoorThis is a four-arm right-angled intersection. Roads leading to Thiruvallamalai and Chittoor branch off at this location. The peak hour traffic at this junction is 730 veh/hr on major road (NH 47) (all directions) and 502 veh/hr on minor road. The total pcu at this junction during peak hour is 1600 pcu/hr. The traffic on major and minor arms warrants signal control at this junction as per IRC specifications, assuming an average growth rate of 8.5% p.a. the junction will require grade separation by the year 2026.

iv. Km 228/000 – To Alathoor Town/Chittoor

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This is a four-arm right-angled intersection. Roads leading to Alathoor Town and Chittoor branch off at this location. The peak hour traffic at this junction is 864 veh/hr on major road (NH 47) (all directions) and 265 veh/hr on minor road. The total pcu at this junction during peak hour is 1129 pcu/hr. The traffic on major and minor arms warrants signal control at this junction as per IRC specifications, assuming an average growth rate of 8.5% p.a. the junction will require grade separation by the year 2027.

v. 230/300 - To Alathur Town/MudiyagaonThis is a four-arm right-angled intersection. Roads leading to Alathoor Town and Mudiyagaon branch off at this location. The peak hour traffic at this junction is 779 veh/hr on major road (NH 47) (all directions) and 312 veh/hr on minor road. The total pcu at this junction during peak hour is 1498 pcu/hr. The traffic on major and minor arms warrants signal control at this junction as per IRC specifications, assuming an average growth rate of 8.5% p.a. the junction will require grade separation by the year 2028

vi Km 231/775 – To Alathoor TownIt is a three-arm junction, with two arms being NH 47 and the third one Road leading to Alathoor Town. The peak hour traffic at this junction is 667 veh/hr on major road (all directions) and 126 veh/hr on minor road. The total pcu at this junction during peak hour is 1148 pcu/hr. The traffic on major arm and the traffic on minor arm does not qualify for signalisation and therefore, this junction does not presently warrant signalisation. Assuming an average growth rate of 8.5% p.a. the junction will require signal control by 2009.

vii Km 239/000 -To Vadakanchery town/PollachiIt is a three-arm junction, with two arms being NH 47 and the third one Road leading to Vadakanchery Town/Pollachi. The peak hour traffic at this junction is 794 veh/hr on major road (all directions) and 156 veh/hr on minor road. The total pcu at this junction during peak hour is 1410 pcu/hr. The traffic on major arm and the traffic on minor arm does not qualify for signalisation and therefore, this junction does not presently warrant signalisation. Assuming an average growth rate of 8.5% p.a. the junction will require signal control by 2009.

viii Km 240/200 – To Vadakanchery Town/PadoorThis is a four-arm right-angled intersection. Roads leading to Vadakanchery Town and Chittoor branch off at this location. The peak hour traffic at this junction is 739 veh/hr on major road (NH 47) (all directions) and 519 veh/hr on minor road. The total pcu at this junction during peak hour is 1693 pcu/hr. The traffic on major

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and minor arms warrants signal control at this junction as per IRC specifications, assuming an average growth rate of 8.5% p.a. the junction will require grade separation by the year 2025.

ix Km 240/900 – To Vadakanchery TownIt is a three-arm junction, with two arms being NH 47 and the third one Road leading to Vadakanchery Town. The peak hour traffic at this junction is 922 veh/hr on major road (all directions) and 368 veh/hr on minor road. The total pcu at this junction during peak hour is 1824 pcu/hr. The traffic on major and minor arms warrants signal control at this junction as per IRC specifications, assuming an average growth rate of 8.5% p.a. the junction will require grade separation by the year 2025.

x Km 268/200 – To Thrissur TownIt is a three-arm junction, with two arms being NH 47 and the third one Road leading to Thrissur Town. The peak hour traffic at this junction is 1362 veh/hr on major road (all directions) and 667 veh/hr on minor road. The total pcu at this junction during peak hour is 2478 pcu/hr. The traffic on major and minor arms warrants signal control at this junction as per IRC specifications, assuming an average growth rate of 8.5% p.a. the junction will require grade separation by the year 2021.

As per the existing traffic and projected volumes for next 5 years or so erection of traffic signals at the above Intersections/Junctions shall be justified, however it is felt that to provide uninterrupted and safe flow of the traffic on the National Highway “Grade Separators” may be constructed at the following locations along with the widening of the National Highway.

1. Km 202/800 Pollachi road Junction2. Km 203/800 Palakkad bypass Starting Point3. Km 240/900 Vadakanchery Bypass Ending Point4. Km 268/200 Thrissur Bypass Starting Point.

Provision of flyover is also result in following advantages.

Intersections are located in the congested urban area and pedestrian movements are considerable.

Traffic delays to the through traffic will be completely avoided resulting in time savings.

Better speed shall be maintained at the National Highway maintaining its level of service.

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Uninterrupted flow shall results in savings of operating costs of the vehicles.

Safety at the intersection shall be ensured by separating local and through traffic.

7.7 Speed and Delay studies

7.7.1 Objective

Speed and delay studies are intended to provide information on road sections subject to undue traffic congestion and delay or other factors resulting in reduced travel speeds so that suitable remedial measures can be initiated to improve the overall travel speeds on the roads.

7.7.2 Methodology

Travel speeds on different sections of the project road were determined by the moving car method. In this method, the test vehicle is run at the average speed of the traffic stream so that the numbers of vehicles overtaken by the test vehicle and the number of vehicles overtaking the test vehicle are approximately equal. The observer determines the travel time and delay in each kilometer of the project road by using stopwatches. A minimum of 2 runs is made in each section at different times of the day i.e. morning, afternoon and evening.

7.7.3 Study and Analysis

The details of average travel speeds along the project road morning, afternoon and evening are indicated in Figure 7.32.

The average speed in the morning along the project road is 41.26 kmph, which rises to about 43.08 kmph in the afternoon and then falls to 40.19 kmph in the evening.

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7.8 Traffic Accident Studies

Traffic accidents are random occurrences due to deficiencies in one or more of the elements of traffic, viz, vehicle, driver, road and environment. While deficiencies in driver behaviour, vehicle performance and environmental characteristics are beyond the purview of the highway engineer; significant reduction in the number of traffic accidents and their severity can be achieved by improvements to the road system. In order to assess the economic savings likely due to reduction in traffic accidents and their severity resulting from any highway improvement scheme, it is necessary to analyze past traffic accident data on the study section..

7.8.1 Methodology

Details of traffic accidents which occurred on the project road during the past 6 years were collected from the Police records and analyzed. The accident data collected year wise with number of accidents in respective year has been given Figure 7.33 and Table 7.16.

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Table 7.16 Accidents Data on NH-47 (Km 182/200 to 270/000)

Palakkad DistrictValayar Police

StationPudussery Police Station Palakkad Police

StationYear Fatal Non-Fatal Fatal Non-Fatal Fatal Non-Fatal1999 13 39 17 62 3 462000 12 47 22 58 6 392001 14 45 22 61 12 372002 20 54 13 35 14 392003 12 51 12 34 10 392004

(31.08.2004)14 38 2 33 4 23

Kuzhalamannam Police Station

Alathoor Police Station Vadakanchery Police Station

Total (Palakkad Distrct)

Year Fatal Non-Fatal Fatal Non-Fatal Fatal Non-Fatal Fatal Non-Fatal1999 7 19 12 37 9 53 61 2562000 8 31 17 63 9 52 74 2902001 6 33 13 52 5 55 72 2832002 10 40 15 76 11 70 83 3142003 3 32 9 38 10 81 56 2752004

(31.08.2004)8 21 9 33 7 37 44 185

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Thrissur DistrictPeechi Police Station Mannoothy Police Station Total (Thrissur District)

Year Fatal Non-Fatal Fatal Non-Fatal Fatal Non-Fatal1999 13 61 9 40 22 1012000 23 77 4 43 27 1202001 14 65 6 60 20 1252002 6 69 13 51 19 1202003 2 70 13 43 15 1132004

(31.08.2004)13 34 5 29 18 63

7.8.2 Black Spots (Accident Prone Areas):

According to the information received from the Police Department, the following black spots with in the project area have been identified.

Palakkad District

Chullimada Palam, KTC Junction, Kurikad, Kannannur, Vadakkumuri, Kazchaparambu, Manalur, Chelikad, Kadumuthurthi, Pirivusula, Kadamkode, Charapparambu, Vellappara Chekkode, Swathi Junction, Kindimukku, ITCAnchumoorthimangalam, Kariyangodu Junction, Infront of Dayana Tower Thankam Theater Junction and Chemmanamukku

Thrissur District

Pattikad, Kompazha, Vazhukkumpara and Chemboothara, DonBosco School Area, Mullakkara, Thottapatti, Agri. University Area, Thrissur Bypass Junction.

7.9 Axle load survey

7.9.1 General

The intensity of traffic loading and the corresponding damaging factor of different categories of vehicles is an important parameter for the design of pavements. The intensity of traffic loading is defined in terms of cumulative number of standard axle load repetitions in a given period of time. IRC-37-1999 provides a scientific method for design for flexible pavements based on the concept of Equivalent Standard Axle and Vehicle Damage Factor.

7.9.2 Location of Survey

Axle load studies were carried out at two locations, at Km 190/000 in Palakkad District on 9th and 10th August 2004 and the other at Km

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263/700 in Thrissur district on 7th and 8th August 2004. The studies were conducted using two calibrated electronic weigh axle load pads, one in each direction. The studies were carried out for a continuous period of 48 hours at each location covering both directions of traffic. To avoid traffic bottlenecks at site, the axle load survey was conducted alternatively in each direction for 3 hours. Two axle, three axle and multi axle trucks and light goods vehicles (both loaded and empty) were selected at random and weighed to determine their individual axle weights. A number of buses were also weighed at each location.

7.9.3 Vehicle Damage Factor (VDF)

Vehicle Damage Factor (VDF) can be defined as the relative pavement damage potential of a particular vehicle of known axle load as compared to that of a vehicle with standard axle load of 18000 lb (8.16 Tonnes). The equivalent single axle (ESA) load factor termed as Vehicle Damage Factor (VDF) for each vehicle is calculated using the following formula:

VDF=(AXLj/SXLj)LE

Where,VDF= Vehicle Damage FactorLE = Axle load equivalency exponent=4.0AXLj = Axle loadSXLj = Standard load

The detailed calculations on derivation of the V.D.F are given in Annexure X. Vehicle Damage Factors (VDF) were assessed for each category of vehicles that contribute significantly to pavement distress for project road as under:

Light Commercial Vehicles (LCV) Two Axle Single Unit Commercial Vehicle (2 axle) Three Axle Single Unit Commercial Vehicle (3 Axle) Multi Axle Vehicle Bus

Remaining vehicle types were not considered in the analysis since most of them are too light to provide significant distress to the pavement (e.g cars, two-wheelers, tractors, etc).

The VDF of the different types of vehicles weighed at the above two locations and in either directions are indicated in Figure 7.34 and 7.35.

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The values of VDF to be used in pavement design needs to be carefully selected. The combined VDF calculated for both the locations is presented in Table 7.17.

Table 7.17: Combined VDF

Location: 190/000

Direction: Kerala Border to Thrissur Direction:Thrissur to TN/Kerala Border

Vehicle Type A.A.D.T VDF ESAL A.A.D.T VDF ESAL

LCV 252 0.148 37.296 354 0.11 38.94

2-AXLE TRUCK

921

1.6861552.806 1228

2.1662659.848

3-AXLE TRUCK

267

3.186850.662 355

3.6571298.235

M-AXLE TRUCK

97

3.576346.872 134

6.625887.75

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BUS 488 0.305 148.84 508 0.65 330.2

TOTAL 2025 2936.476 2579 5214.973

Combined VDF 1.450Combined VDF 2.022

Location: 263/700

Direction: Kerala Border to Thrissur Direction:Thrissur to TN/Kerala Border

Vehicle Type A.A.D.T VDF ESAL A.A.D.T VDF ESAL

LCV 479 0.081 38.799 510 0.168 85.68

2-AXLE TRUCK

1603

1.8332938.299 1807

2.8715187.897

3-AXLE TRUCK

393

3.3491316.157 428

5.1172190.076

M-AXLE TRUCK

83

5.241435.003 89

5.336474.904

BUS 780 0.45 351 801 0.533 426.933

TOTAL 3338 5079.258 3635 8365.49

Combined VDF 1.522Combined VDF 2.301

From the above analysis the consultants recommendation is for adopting V.D.F. value as 2.30

7.10 Traffic Forecast

7.10.1 Background

Adopting the transport demand elasticity method, which is a proven technique worldwide and is the preferred technique in India, the traffic forecast for the project road has been carried out. Efforts have been made to build time series past traffic data for the project road from the available data. This past traffic data has been obtained from the PWD (NH) offices located along the project road. Similar time series past data on population, state income (NSDP) and per capita income for the Study State’s has been developed. Motor vehicle registration data for Kerala and Tamilnadu States has also been collected.

Based on the available data for km 191/000, and 263/000 log-log regression curve fits have been developed, for each type of vehicle and the elasticity values obtained. Initially, past traffic census data is regressed with the selected economic variable such as population, per capita income and State income (NSDP). Consequently, as suggested by the Indian Roads Congress (IRC: 108-1996), motor vehicle registration data has been used which resulted in a good-fit. Recent trend in traffic/vehicle registration growth, over their long-term growth for which elasticity was worked out, is considered in moderating the elasticity values for future periods.

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Considering the projected elasticity values, economic development indicators for the State’s within the project influence area and the traffic distribution between them as arrived from traffic surveys, vehicle type wise growth rates were worked out for different time periods. The projected growth rates worked out major vehicle groups such as car; bus, two wheelers and trucks were further moderated to detailed vehicle categories, considering the likely future shift among the vehicle categories. The probable structural shift and modal choice of vehicle ownership from 2 axle trucks to MAV’s and 2/3 wheeler market to car have been taken in to account while moderating the elasticity values. This is considered necessary because as the purchasing power increases there will be a shift from low cost vehicle to high speed/power and more comfortable vehicles. Further, with the road improvement and realization of economics of scale, goods operators will tend to transfer from 2 axle trucks to Multi-Axle vehicles. These market driven forces have been realistically considered in the traffic forecast.

7.10.2 Past Traffic Growth

Past traffic data on the Project road is available for Km 191/000, Km 203/000, Km 239/000 and Km 263/000. Past traffic data collected for each of the sections has been analyzed. Table 7.18 below summarizes the Annual Average Compound Growth Rate (AACGR) of traffic by vehicle for the project road for the PWD count locations.

Table 7.18 Past Traffic Growth on the Project Road

Location (Km) Year Cars/3Wheelers Buses Goods Vehicles TW

191/000

1992 3709 1186 3172 2393

1993 4981 2028 4039 3398

1994 5494 1715 4244 3708

1995 5974 2115 4844 4387

1996 6492 2480 5198 4709

1997 6795 2718 4456 51371998 7185 3161 6064 55931999 7485 3399 6366 56962000 7924 3756 6815 60172001 7904 3694 6546 59742002 7343 3385 6189 5480

AACGR(%) 1992-2002 7.07 11.06 6.91 8.64

203/0001992 5251 1592 3516 3467

1993 4312 1756 4832 3824

1994 6011 1858 4911 3914

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1995 6291 1785 5396 4135

1996 7024 1815 7044 5602

1997 7776 1622 8232 71351998 8274 1782 9473 80721999 8887 2022 9982 84542000 9740 2330 10392 92122001 8457 2265 8364 85742002 7261 1703 5511 8034

AACGR(%) 1992-2002 3.30 0.68 4.60 8.77

239/000

1992 2739 1064 2975 494

1993 2745 1170 3279 500

1994 3710 2624 2566 820

1995 3306 1423 3357 832

1996 3630 2158 3484 824

1997 4958 1626 4154 11741998 6775 1815 5268 22901999 5748 2026 4084 18562000 4367 1571 4745 13562001 5427 1943 4614 15172002 5981 1690 5521 1404

AACGR(%) 1992-2002 8.12 4.73 6.38 11.01

263/000

1992 2396 1133 2824 511

1993 2746 1476 3402 488

1994 2915 1483 3051 578

1995 4100 2042 4518 924

1996 5585 2266 5468 1290

1997 5556 1918 4839 17871998 6167 2422 5822 16431999 6625 2901 6037 17592000 5982 2853 7010 19292001 5552 3042 8289 22532002 5850 3087 9145 2622

AACGR(%) 1992-2002 9.34 10.54 12.47 17.77

Vehicle type wise annual growth rates are observed with fluctuations among the count stations. Hence an attempt was made to calculate elasticity values for two count stations (km 191/000 and Km 263/000) to verify its suitability. The economic indicators of Kerala and Tamilnadu states, which are used for the regression analysis along with traffic data considered (km 191/000 and Km 263/000), are given in Tables 7.19to Tables 7.22 and their detailed analysis is presented in Annexure - XI.

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Table 7.19 Data Considered for Regression Analysis (Past Traffic at Km 191/000 and Kerala Economy)

YearNSDP (Rs.

Lakhs)2

PopulationPer capita

NSDP2 Cars/3 Wheelers1 Buses1

Goods Vehicles1 TW1('000) (Rs)

1993-94 2385107 30048 7938 4981 2028 4039 3398

1994-95 2590792 30424 8516 5494 1715 4244 3708

1995-96 2694747 30805 8748 5974 2115 4844 4387

1996-97 2802645 31186 8987 6492 2480 5198 4709

1997-98 2863315 31539 9079 6795 2718 4456 5137

1998-99 2985358 31856 9371 7185 3161 6064 5593

1999-00 3111132 32145 9678 7485 3399 6366 5696

1. Average Daily Traffic (ADT) at Km 191/000 of NH 472. Pertaining to Kerala State at 1993-94 Constant Prices

Table 7.20 Data Considered for Regression Analysis (Past Traffic at Km 263/000 and Kerala Economy)

YearNSDP (Rs.

Lakhs)2


NSDP2 Cars/3 Wheelers1 Buses1 Goods Vehicles1 TW1('000) (Rs)

1993-94 2385107 30048 7938 2746 1476 3402 488

1994-95 2590792 30424 8516 2915 1483 3051 578

1995-96 2694747 30805 8748 4100 2042 4518 924

1996-97 2802645 31186 8987 5585 2266 5468 1290

1997-98 2863315 31539 9079 5556 1918 4839 1787

1998-99 2985358 31856 9371 6167 2422 5822 1643

1999-00 3111132 32145 9678 6625 2901 6037 1759

1. Average Daily Traffic (ADT) at Km 263/000 of NH 472. Pertaining to Kerala State at 1993-94 Constant Prices

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Table 7.21 Data Considered for Regression Analysis (Past Traffic at Km 191/000 and Tamilnadu Economy)

YearNSDP (Rs.

Lakhs)2


NSDP2Cars/3

Wheelers1 Buses1Goods

Vehicles1 TW1('000) (Rs)

1993-94 5164169 576872 8952 4981 2028 4039 3398

1994-95 5808144 584109 9944 5494 1715 4244 3708

1995-96 6027459 591436 10191 5974 2115 4844 4387

1996-97 6331673 598261 10583 6492 2480 5198 4709

1997-98 6793632 604442 11240 6795 2718 4456 5137

1998-99 7185175 610207 11775 7185 3161 6064 5593

1999-00 7697144 615574 12504 7485 3399 6366 5696

1. Average Daily Traffic (ADT) at Km 191/000 of NH 472. Pertaining to Tamilnadu State at 1993-94 Constant Prices

Table 7.22 Data Considered for Regression Analysis (Past Traffic at Km 263/000 and Tamilnadu Economy)

YearNSDP (Rs.

Lakhs)2


NSDP2Cars/3



1993-94 5164169 576872 8952 2746 1476 3402 488

1994-95 5808144 584109 9944 2915 1483 3051 578

1995-96 6027459 591436 10191 4100 2042 4518 924

1996-97 6331673 598261 10583 5585 2266 5468 1290

1997-98 6793632 604442 11240 5556 1918 4839 1787

1998-99 7185175 610207 11775 6167 2422 5822 1643

1999-00 7697144 615574 12504 6625 2901 6037 1759

1. Average Daily Traffic (ADT) at Km 263/000 of NH 472. Pertaining to Tamilnadu State at 1993-94 Constant Prices

7.10.3 Elasticity’s

Passenger traffic demand is a function of growth of population and percapita income. Similarly, State income growth, mostly govern freight traffic growth. In line with this philosophy, the elasticity of traffic demand for 2 W, car, bus and truck with respect to the past traffic growth was estimated. The resultant elasticity values are presented in Tables 7.23 to 7.26.

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Table 7.23 Regression Analysis Results (Past Traffic Data at Km 191.000 with Kerala Economy)

Dependent Variable

Independent Variable Period

R2(Co-relation

Coefficient)Elasticity

Value

t - stat.(Statistical test Co-efficient)

log of Car Traffic

log of Population 1993-2000 0.99 5.97 21.69

log of PCI 1993-2000 0.97 2.23 13.21

log of Bus Traffic


log of PCI 1993-2000 0.73 3.24 3.67

log of TW Traffic


log of PCI 1993-2000 0.94 2.96 8.86log of Truck Traffic log of NSDP 1993-2000 0.78 1.73 4.22

Table 7.24 Regression Analysis Results (Past Traffic Data at Km 263.000 with Kerala Economy)

Dependent Variable


R2(Co-relation


Value


log of Car Traffic


log of PCI 1993-2000 0.88 5.19 6.12

log of Bus Traffic


log of PCI 1993-2000 0.83 3.5 5

log of TW Traffic



Table 7.25 Regression Analysis Results (Past Traffic Data at Km 191.000 with Tamilnadu Economy)

Dependent Variable


R2(Co-relation


Value


log of Car Traffic


log of PCI 1993-2000 0.96 1.29 11.3

log of Bus Traffic


log of PCI 1993-2000 0.78 1.95 5.78

log of TW log of Population 1993-2000 0.98 8.38 16.72

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Traffic log of PCI 1993-2000 0.93 1.71 8.4log of Truck Traffic log of NSDP 1993-2000 0.75 1.12 3.87

Table 7.26 Regression Analysis Results (Past Traffic Data at Km 263.000 with Tamilnadu Economy)

Dependent VariableIndependent

Variable PeriodR2(Co-relation


Valuet - stat.(Statistical test Co-efficient)

log of Car Traffic


log of PCI 1993-2000 0.85 2.95 5.22

log of Bus Traffic


log of PCI 1993-2000 0.78 1.96 4.16

log of TW Traffic


log of PCI 1993-2000 0.85 4.42 5.26

log of Truck Traffic log of NSDP 1993-2000 0.71 1.99 3.49

The recent growth trend (1997-2002) with respect to long-term growth trend achieved during 1993-2002 for past traffic for different vehicle categories shows differences. The economic changes taking place in Kerala and Tamilnadu states may explain these changes. Assuming that these changes will have more impact in future traffic scenario of the project road, the arrived elasticity values were moderated and recommended for forecast, as given below in Table 7.27.

Table 7.27 Recommended Elasticity for Past Traffic Growth Weighted Average

Vehicle Type

Weighted Average* Elasticity for 1993-

2000(Present Study)

Economic variable

considered

Weighted Average* Elasticity for 1993-

2000(Present Study)

Economic variable

considered

Recommended Elasticity(Weighted Average)*

2005-2009 2010-2014

2015 and beyond

Car 10.17 Population 3.31 PCI 4.88 4.25 3.75

Bus 9.43 Population 5.34 PCI 3.75 3.25 2.75Two wheelers 14.70 Population 2.88 PCI 4.50 3.88 3.50

Trucks 1.96 NSDP 1.79 1.59 1.36

* Passenger Vehicles Contribution is 75% Kerala State and 25% Tamilnadu State

Goods Vehicles contribution is 65% Kerala State and 35% Tamilnadu State (Based on O-D Analysis)7.10.4 Vehicle Registration Growth

To have a better idea of projections elasticity values has also been worked out adopting the procedure as suggested by Indian Roads Congress (IRC: 108-1996). Past data for 7 years from 1993 to 2000 on vehicle type wise registration and economic variable performance in Kerala and Tamilnadu States was considered for log-log regression technique. In tune with the

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analysis philosophy indicated earlier, as well the past performance and the composition of Kerala and Tamilnadu economy, the passenger and goods vehicles growth were regressed with the following economic indicators in different combinations: Population Per Capita income (PCI) NSDP (Total)

Time series data considered for regression analysis is presented in Table 7.28 to Table 7.29.This analysis has resulted in the following ‘e’ values, as given in Table 7.30 to Table 7.32, are found with good fit, as reflected in their R2 and t-stat. values. Hence it is appropriate is consider the growth pattern that has emerged out of the economic model, which related the economic growth with the growth in vehicle registration data. Details of statistical relationship arrived for the above ‘e’ values are given in Annexure XI.

Table 7.28 Data Considered for Regrssion Analysis (Vehicle Registration Data and Economic Indices for Kerala State)

YearNSDP (Rs.

Lakhs)2

Population

Per capita NSDP2 Cars/3



1993-94 2385107 30048 7938 226806 30370 88455 428641

1994-95 2590792 30424 8516 247605 34862 100252 496873

1995-96 2694747 30805 8748 273922 38177 111762 591923

1996-97 2802645 31186 8987 257048 43030 131311 694242

1997-98 2863315 31539 9079 330603 55482 139145 798982

1998-99 2985358 31856 9371 370964 48884 151082 904961

1999-00 3111132 32145 9678 396874 58888 163443 1020797AACGR(%) from 1994-

20004.53 1.13 3.36 9.77 11.67 10.77 15.56

1. Vehicle Registration Data Pertaining to Kerala State2. Pertaining to Kerala State at 1993-94 Constant Prices

Table 7.29 Data Considered for Regrssion Analysis (Vehicle Registration Data and Economic Indices for Tamilnadu State)

Year

NSDP (Rs.

Lakhs)2

Population

Per capita NSDP2 Cars/3



1993-94 5164169 576872 8952 280799 20568 115279 1586412

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1994-95 5808144 584109 9944 297878 21080 126916 1815233

1995-96 6027459 591436 10191 319837 21578 142490 2101787

1996-97 6331673 598261 10583 345587 21936 152897 2454067

1997-98 6793632 604442 11240 376746 22989 164890 2816820

1998-99 7185175 610207 11775 407975 22713 174017 3214068

1999-00 7697144 615574 12504 443023 22738 183476 3678275

AACGR(%) from 1994-2000 6.88 1.09 5.73 7.90 1.69 8.05 15.05

1. Vehicle Registration Data Pertaining to Tamilnadu State2. Pertaining to Tamilnadu State at 1993-94 Constant Prices

Table 7.30 Regression Analysis Results (Vehicle Registration Data with Kerala Economy)

Dependent Variable Independent Variable Period

R2(Co-relation



log of Car Traffic


log of PCI 1993-2000 0.83 3.01 4.95

log of Bus Traffic


log of PCI 1993-2000 0.87 3.5 5.74

log of TW Traffic

log of Population 1993-2000 1 12.91 132.86


Table 7.31 Regression Analysis Results (Vehicle Registration Data with Tamilnadu Economy)

Dependent Variable Independent Variable Period

R2(Co-relation



log of Car Traffic


log of PCI 1993-2000 0.97 1.46 12.52

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log of Bus Traffic


log of PCI 1993-2000 0.86 0.35 5.65

log of TW Traffic

log of Population 1993-2000 1 12.98 41.52


7.10.5 Moderated Transport Demand Elasticity

The estimated ‘e’ values, as presented in Table 7.32, are considered to represent mainly the past traffic growth pattern. However, the future traffic growth pattern is likely to be affected by the following factors, in addition:

Recent economic changes taking place in the project influence region.

Future modal shift among vehicle types, in road traffic as well as between various modes;

Development in vehicle technology.

Elasticity values considered in the recent highway projects in the study region or recommended by multilateral funding institutions for Indian Highway Projects are also studied.

With this background, it is necessary to moderate the elasticity values to accommodate the above factors discussed as well as to arrive at the growth rates that are realistic.

The arrived elasticity values were moderated and recommended for forecast, as given below in Table 7.32.

Table 7.32 Recommended Elasticity for Vehicle Registration Growth Weighted Average

Vehicle Type

Weighted Average*

Elasticity for 1993-

2000(Present Study)

Economic variable

considered

Weighted Average*

Elasticity for 1993-

2000(Present Study)

Economic variable

considered

Recommended Elasticity(Weighted Average)*

2005-2009 2010-20142015 and beyond

Car 8.00 Population 2.62 PCI 3.78 3.38 3.06

Bus 7.62 Population 2.71 PCI 2.63 2.13 1.69

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Two wheelers 12.93 Population 4.24 PCI 3.50 2.88 2.69

Trucks 2.04 NSDP 1.40 1.13 0.93

* Passenger Vehicles Contribution is 75% Kerala State and 25% Tamilnadu State

Goods Vehicles contribution is 65% Kerala State and 35% Tamilnadu State (Based on O-D Analysis)

7.10.6 Future Economy Prospects

Ongoing economic liberalization measures, introduced in the nineties, were oriented to make the national economy with a strong competitive base, leading to an open economy system. But unfortunately, this system makes Indian economy more prone to the global fluctuations. With the strengths and weakness of the present system, equipped with adequate safeguards, Indian economy has performed fairly well during the liberalization period, as well as before, though the targets were mostly never achieved.

Recent economic performance of the nation, in terms of net domestic product, was found to be in the range of 5 to 6 percent growth. The Ninth Five Year Plan Documents (1997-2002) set 7% target for economic growth for the Nation. Independent evaluations by different agencies put the nation’s economic growth during the Ninth Plan period, in the range of 5.5-6.0 percent. The individual state’s economy performances and their targets may slightly vary to the national level

Considering the factors such as past performance of the economy against their set targets, recent developments in economic liberalization measures, shift in between sectors, opportunities available in local and global markets etc., future economic growth scenario is formulated for the following time periods.

2005 – 2009 2010 – 2014 2015 and beyond

We feel, any projection beyond 10-15 years will have little relevance in current context, and no further projection is attempted. Hence, the entire period beyond 2015 has been kept as one slab. The consideration of time periods is mainly based on the Five Year Plan periods practiced at national and state levels.

Based on the performance of the economy and the near term economic development contemplated in the states, the future growth pattern of Kerala and Tamilnadu states economy is likely to follow the growth rates indicated below:

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Future Economic Growth Pattern(% per annum)

Year 2005-2009 2010-2014 >=2015

Kerala State

NSDP 4.00 4.50 4.00

PCI 2.86 3.46 3.07

Population 1.10 1.00 0.90

Tamilnadu State

NSDP 6.00 6.50 6.50

PCI 4.95 5.50 5.60

Population 1.00 0.90 0.85

7.10.7 Traffic growth rates

Based on the moderated elasticity values and the projected economic/demographic indicators and with the given model as follows, the future average annual compound traffic growth rates by vehicle type are estimated.

Passenger Vehicles

Tgr = [(Pgr x 0.5) + (PCIgr x 0.5)] x E

Where,Tgr – Traffic growth ratePgr – Population growth ratePCIgr – Per capita income growth rateE – Elasticity value

Above passenger vehicles model assigns equal weightage to the economic variables of population and per capita income.

Goods Vehicles

Tgr = ( NSDPgr) x E

Where,

NSDPgr – NSDP Growth RateE – Elasticity Value

7.10.8 Optimistic, Normal & Pessimistic Growth Rates

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The forecast of the traffic has been based further analyzed under three scenarios as follows:

Optimistic Scenario: In this scenario, the growth rates are assumed at the maximum value observed in methods described above.

Pessimistic Scenario: In this scenario, the growth rates are assumed at the worst scenario.

Normal Scenario: Normal scenario depicts the average growth rate considering optimistic and pessimistic scenarios

For the project road traffic growth rates were arrived on weighted average basis. To assess the effect of individual state’s economy on the project road from the states within the project influence area, traffic composition weight for different vehicle types from different states were arrived from origin – destination surveys. Based on the recommended elasticity’s and the weighted economy, growth scenarios for different vehicle types on the Project Road are presented in Table 7.33.

Table 7.33 Projected Traffic Growth Rates adopted for the study

S.No Vehicle Type

Projected Annual Traffic Growth Rate (%) - Pessimistic Approach

2005-2009 2010-20142015 and beyond

1 Car 8.41 8.34 7.03

2 Goods Vehicles 6.56 5.86 4.53

3 Bus 5.85 5.25 3.87

4 TW 7.8 7.11 6.17

5Agri Tractor/Trailor 5.86 4.53 3.87

S.No Vehicle Type

Projected Annual Traffic Growth Rate (%) - Optimistic Approach

2005-2009 2010-20142015 and beyond

1 Car 10.87 10.51 8.61

2 Goods Vehicles 8.41 8.28 6.63

3 Bus 8.36 8.04 6.31

4 TW 10.03 9.58 8.03

5Agri Tractor/Trailor 8.04 6.63 6.31

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S.No Vehicle Type

Projected Annual Traffic Growth Rate (%) – Normal Approach

2005-2009 2010-20142015 and beyond

1 Car 9.5 8.5 7.5

2 Goods Vehicles 7.5 7 5.5

3 Bus 7 6.5 5

4 TW 9 8.2 7.1

5Agri Tractor/Trailor 7 5.5 5

Traffic growth rates, as estimated and presented in Table 7.33 are recommended for further planning purpose, as it represent the most likely scenario of future traffic growth for the project road. For pavement design and economic analysis purposes, growth rates under ‘normal scenario’ are considered for traffic forecast. In the event the proposed project road is considered for implementation through BOT route, it is advisable to consider traffic growth rates under pessimistic scenario to project the future traffic, as gathered from the limited experience available with recent BOT toll road projects in India. Thus growth rates under pessimistic scenario are recommended for BOT analysis.

7.10.9 Generated/Diverted Traffic

Post evaluation studies carried out along few completed four lane roads as well the actual observations on similar completed roads indicate that following completion of the road improvements, new developments have occurred along the road which includes industry, educational institutions, commercial and residential buildings. As a result of this development, generated traffic can be expected but as yet not has been quantified. Thus the proposed 4 lane divided highway facility for the Project Road is also likely to attract generated and diverted traffic in view of its better performance characteristics such as higher operating speeds and better riding quality. However, in the absence of precise information regarding future growth factors of parallel transport facilities such as railways, it is reasonable to assume a notional increase in the road traffic due to generated/diverted traffic. This generated traffic could be expected to add to the normal traffic during the initial years when the 4-lane road facility becomes operational. Accordingly, the generated/diverted traffic is assumed in a phased manner as follows:

Year 1 - 1%Year 2 - 2%Year 3 - 3%

7.10.10 Projected Traffic

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The mode-wise AADT has been projected in vehicles and PCUs for different years and sections of the project road under different growth scenarios and is presented in Annexure-XI. The Summary of projected motorized traffic at different locations given at Table 7.34

Table 7.34 Projected Motorised A.A.D.T at Various Locations

S.No Location

Projected Motorised A.A.D.T(PCUs) Optimistic Approach

2010 2015 2020 2025 20301 191/000 32069 48227 68211 96654 1372192 210/600 35194 52729 74279 104822 1481943 237/000 36865 55242 77830 109848 1553184 263/100 47781 71763 101330 143347 203178

S.No Location

Projected Motorised A.A.D.T(PCUs) Normal Approach

2010 2015 2020 2025 20301 191/000 30100 42356 56757 76211 1025512 210/600 33031 46321 61795 82601 1106383 237/000 34615 48556 64789 86616 1160284 263/100 44817 62963 84195 112821 151504

S.No Location

Projected Motorised A.A.D.T(PCUs) Pessimistic Approach

2010 2015 2020 2025 20301 191/000 28465 38392 49441 63878 828072 210/600 31230 41920 53697 68996 889453 237/000 32740 43974 56353 72440 934204 263/100 42377 57047 73299 94492 122232

7.10.11Lane Requirements

The IRC guidelines (IRC: 64-1990) stipulates the daily capacity for two and four lanes highways as follows:

Two Lane – 30,000 PCUs/day (Maximum at LOS-F)

Four Lane – 80,000 PCUs/day (Maximum at LOS-F)

However, IRC guidelines suggest service volume corresponding to level of service B (which corresponds to a service volume 50% of the maximum

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capacity, i.e. 15000 PCU/day for 2 lane road and 40,000 PCU/day for 4 lane road) as design capacity.

Based on the above capacity criteria, the lane configuration for the project road during the analysis period is worked out and results of same are summarized in Table 7.35

Table 7.35 Section-wise Lane Requirements

Homogeneous Section

Eligible for 4-lane widening Eligible for 6-lane widening

Normal Growth Scenario

Optimistic Growth Scenario

Pessimistic Growth Scenario

Normal Growth Scenario

Optimistic Growth Scenario

Pessimistic Growth Scenario

Kerala Border to Palakkad 2004 2004 2004 2014 2013 2016

Palakkad to Alathoor 2004 2004 2004 2013 2012 2014

Alathoor to Vadakanchery 2004 2004 2004 2012 2011 2014

Vadakanchery to Thrissur 2004 2004 2004 2009 2008 2010

As shown above, all the sections of the project road are eligible for widening to 4-lane configuration.

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