citysim - validated assessment tool for jarkko … city simulator modeling system was designed to be...

CITYSIM - Validated assessment tool for

simulating urban traffic and environmental

impacts

Jarkko Niittymaki (*), An Karppinen, Jaakko Kukkonen, Pekko

Ilvessalo C ), Erkki Bjork ( ^ )(*) Helsinki University of Technology, Transportation Engineering,7>.a#ox 2700, fW-0207J, TfC/7; FWaWEmail: jarkko. [email protected](**) Finnish Meteorological Institute, Sahaajankatu 20E, FIN-00180,

Helsinki(***) [ /vg z/y q/7[z/qpm, f.O. ojc 7627, fW-70277,

Abstract

The "City Simulator feasibility study" was conducted in 1997 - 1998, with thefunding of Ministry of Trade and Industry in Finland. The aim of this study wasto analyze possibilities to develop a comprehensive modeling system, which canbe used in order to evaluate the traffic volumes, emissions, atmosphericdispersion and noise within cities. The need for such operative tools is mosturgent in the developing countries. The cities of the world will have to improvesubstantially their understanding of the traffic systems and their impacts on theurban environment in the future. The project continues, because of verypromising results.

1. Introduction

Urban population and vehicular traffic are continuously increasing in bothindustrialized and developing countries. The most important problems for urbanatmospheric environment are currently caused by the emissions and noise fromtraffic. In many developing countries, urban vehicular traffic is growing very

Transactions on the Built Environment vol 41, © 1999 WIT Press, www.witpress.com, ISSN 1743-3509

394 Urban Transport and the Environment for the 21st Century

rapidly, and this causes increasing problems for the health of the urbanpopulations and the environment.

Nowadays, about 70 % of the European population live in urban areas. Measureddata shows that the proposed national air quality guidelines have been fairlyoften exceeded in urban areas also in Finland. The exceeding have been mostcommon for paniculate, both for PMio and for total suspended particles. Someexceeding have also occurred for NOz and CO, mainly in places with high trafficdensities, and often in street canyon conditions (Kukkonen et al., 1999).

In urban areas, air pollutant concentrations typically vary substantially spatiallyand temporally: on a distance scale of tens of meters, and on a time scale of tensof minutes. The Finnish national regulatory short-range dispersion models havebeen summarized by Kukkonen et al. (1997). Some integrated systems areavailable, for instance, Karppinen et al. (1998a) present a system containingtraffic macrosimulation, emission and air quality modeling. Despite these efforts,there is a lack of validated, versatile and user-friendly air quality assessment andmanagement tools in the urban scale.

Air pollution dispersion and exposure models are useful tools for understandingurban air pollution and the exposure of urban population to air pollution. Themodels can be applied, for instance, in order to evaluate various emission controlstrategies and to find out efficient and cost-effective solutions to urban air qualityproblems. The models can also be used in order to analyze various scenarios andfuture developments, and to evaluate the representatively of the urbanconcentration measurement stations. Exposure to urban air pollution has becomean increasing concern, as more information has been obtained concerning theadverse health effects of air pollutants.

An urban modeling system is therefore needed, which could be utilized in orderto achieve environmentally sustainable strategic and operative policy decisions.Air quality and noise problems should be properly allowed for in municipal andcity planning. The modeling system should also include traffic simulationmodels; in order to obtain optimal traffic flows, with minimum emissions andenergy consumption. A simulation tool is therefore required, which combinestraffic simulation, emission and air pollution modeling, exposure modeling andnoise modeling. The system should also be able to utilize the results frommeasurement networks for traffic flows, noise and air quality.

The need for such operative tools is most urgent in the developing countries. Thecities of the world will have to improve substantially their understanding of thetraffic systems and their impacts on the urban environment in the future. Ouranswer for this kind of problems will be an integrated modeling tool, "CitySimulator" (CITYSIM). It will be a strategic and operative decision-making tool,which combines the traffic simulation, the emission, and the air pollution andnoise dispersion models.


Urban Transport and the Environment for the 21st Century 395

The aim of this paper is:

* to show the structure of CITYSIM,* to present all proposed software programs contained in CITYSIM,* to discuss the benefits of such an integrated simulation system and* to present plans for pilot study in Turku, Finland.

2. City Simulator feasibility study

2.1 Introduction

The "City Simulator feasibility study" was conducted in 1997 - 1998, with thefunding of Ministry of Trade and Industry in Finland. The aim of this study wasto analyze possibilities to develop a comprehensive modeling system, which canbe used in order to evaluate the traffic volumes, emissions, atmosphericdispersion and noise within cities. For a detailed discussion of the results, thereader is referred to the final report (City Simulator Feasibility Study, 1998).

The City Simulator modeling system was designed to be fairly easily adaptedinternationally, from one city to another. The "Citysimulator" modeling systemwill be used mainly by local authorities, responsible for traffic and urbanplanning. Two primary target cities were selected: Turku in Finland andShenyang in China.

2.2 Participants

Finnish Meteorological Institute (FMI) was the responsible organization. FMIhas also been the expert on air quality modeling and management.Helsinki University of Technology (HUT) has been the expert on trafficsimulation and planning. University of Kuopio (UKU) has been the expert onnoise modeling and management. Cyber Cube Oy has been the expert onvisualization of the results. Environmental office of the City of Turku hasparticipated as a European pilot-city and as an expert on environmentalmanagement and city planning.

Environmental Protection Bureau (EPS) of Shenyang and Urban PlanningInstitute (UPI) of Shenyang have participated as experts on environmental andurban planning, and as an Asian pilot city. Science and Technology Commissionof Shenyang (SSTC) had the main responsibility in China. SSTC has introducedthe project plan to Chinese coworkers, to the Liaoning Science and TechnologyCommission and to the directors of the Shenyang City.

2.3 Results of the pilot phase

The city of Turku was chosen as the Finnish pilot-city, as the traffic andenvironmental situation are reasonably well known. The pilot study in Turkuapplies a preliminary "City Simulator" modeling system, and its main objectiveis to illustrate practical applications and potential usage of the system.



We also surveyed the requirements and possibilities of the system to be appliedin a major developing Asian city, Shenyang.

3. Objectives of the work

3.1 Integrated modeling system

The work aims to develop a software package "City Simulator", and to apply andtest it in practice (Fig. 1). The model contains micro- and macro-level trafficsimulation tools, results from a numerical weather prediction model, emissionand atmospheric dispersion models, a population exposure model, noisedispersion models and visualization of the simulation results.

The individual modules and methods have been previously developed by theparticipating institutes. Work is in progress in order to design and test theinterfaces between different model components and the database.

MOD Simulation of trafficf.ex.. HUISIM)

Figure 1. Components of the "City Simulator" modeling system.



The structure of the CITYSIM system will be defined on several levels ofapplication. The lowest level is defined by the model user or decision-maker.The second level is typical microsimulation environment and the third level is amacrolevel program with traffic assignment algorithm. The levels are connectedwith each other and there has to be a feedback connection between the user leveland the macrolevel. The calculations of pollutant and noise emissions areconnected to both the micro- and macrolevels. The animation part is alsoconnected to each level. From the user's point of view, visualization andanimation is essential.

The use of the real-time data and measurements gives us a tool for estimating thepresent traffic-, air quality- and noise situations in cities. The visualization of theresults with 3D graphics in each level is needed in order to achieve anunderstanding of the traffic network considered.

3.2 Traffic modeling

Increasingly accurate methods are needed, when planning traffic infrastructure ortraffic management. The crucial question is how to optimise existinginfrastructure and investments in terms of economical, ecological and trafficsafety matters. Commonly these problems concern situations, in which trafficvolumes and load factors are high. In those cases traditional planning methods areinsufficient. The details of the infrastructure and traffic flow are increasinglyimportant, when traffic volumes are larger. Methods which are based on averagecapacities and speeds are invalid in such cases.

Computer simulation can play a major role in the analysis and assessment of thehighway transportation system and its components. Often they incorporate otheranalytical techniques, such as demand-supply analysis, capacity analysis, trafficflow models, car-following theory, shock wave analysis, and queuing analysis,into a framework for simulating complex components or systems of interactivecomponents. These components may be individual signalized or non-signalized,residential or central business district dense networks, linear or network signalsystems, linear or corridor freeway systems, or rural two-lane or multilanehighways systems.

Microscopic simulation of traffic is based on the kinematics of individualvehicles. The properties and operations of each vehicle are modelled separately.As the microscopic simulation method is based on the vehicle - vehicle andvehicle - infrastructure interactions, it facilitates versatile analyses. For example,in addition to the average speeds of traffic flow, also the variations andconfidence intervals are available. Modelling the effects of incidents is alsopossible using microscopic simulation.

In Finland, microscopic simulation of traffic has been applied to traffic analysisfor about ten years. The interest to simulate high-class roads with interchangesand weaving areas was raised in later 1990's. The methodology is traffic



simulation with specially designed additional computer program for thecalculation of the fuel consumption and the exhaust pollution. HUTSIM - thesimulation model developed in the Laboratory of Transportation Engineering atHelsinki University of Technology has given the traffic flow parameters, such asaverage delays, number of stops, average travel speeds, etc. HUTSIM alsogenerates singular vehicle speed profiles containing information of acceleration,deceleration, idling time and cruising speed.

In combination with the computer program, called HUTEMCA, for emissioncalculations, based on three-dimensional emission matrixes and the singularvehicle driving characteristics, continuous emission profiles are derived for eachvehicle in the road or intersection considered. Clearly, the model needs to beverified, calibrated and validated. HUTSIM - microscopic simulator is especiallydesigned for the analysis of modern traffic actuated signals, complicatedintersections and changing traffic conditions. All validation results of HUTSIMhave been good. The additional methods of these analyses were a normal videorecording and video recording from the helicopter.

3.3 Atmospheric dispersion and meteorological modeling

The following regulatory local-scale atmospheric dispersion models are availableat the FMI: the urban dispersion modeling system, models for dispersion ofvehicular pollution, the air pollution information system and the dispersion modelfor odorous compounds (Kukkonen et al., 1997). All of these models areconnected to a meteorological pre-processing model, based on atmosphericboundary layer scaling.

The urban dispersion modeling system (UDM-FMI; Karppinen et al., 1998b)includes a multiple source Gaussian plume model and the meteorological pre-processor. The dispersion model is an integrated urban-scale model, taking intoaccount all source categories (point, line, area and volume sources). It includes atreatment of chemical transformation (for NO2) and deposition (dry and wetdeposition for SO2), plume rise, downwash phenomena and the dispersion ofinert particles.

The dispersion from a road network is evaluated with the Gaussian finite-linesource model CAR-FMI (Contaminants in the Air from a Road) (Harkonen et al.,1996). The model includes an emission model, a dispersion model, statisticalanalysis of the computed time series of concentrations and a graphical Windows-based user interface. The meteorological data for the model is evaluated by theFMI meteorological pre-processor.

The meteorological pre-processing model MPP-FMI (Karppinen et al., 1997 and1999) is based mainly on the energy budget method of van Ulden and Holtslag(1985). The model utilises meteorological synoptic and sounding observations,and its output consists of estimates of the hourly time series of the relevantatmospheric turbulence parameters (the Monin-Obukhov length scale, the frictionvelocity and the connective velocity scale) and the boundary layer height.



The model used for production of short-range numerical weather predictions atFMI is HIRLAM. The HIRLAM model is used operationally since 1990. Atpresent the model produces daily four 48-hour regional forecasts and four 36-hour mesoscale forecasts for the Northern Europe. FMI participates also in theinternational HIRLAM project.

The Air Pollution Information System API-FMI has been developed fordisseminating real-time and forecast air pollution information to the public. Thesystem includes computational methods for forecasting air pollution in time(Bremer, 1993, Bremer and Valtanen, 1995), a mathematical model forcomputing an air quality index and a system for disseminating the results to thepublic in an easily readable form.

Air pollution forecasting methods can be divided into two categories: (i)application of the weather forecasts of the synoptic situation and meteorologicalparameters, and (ii) computation of pollutant concentrations, using statisticalmethods and the urban dispersion modeling system (UDM-FMI). The statisticalmethods are based on regression analysis of measured concentrations andmeteorological parameters. These correlations have been derived frommeasurements in the Helsinki metropolitan area. Air pollution forecasts are madefor the compounds SO%, NO% and CO. The system is applicable in an urban area.It can also be used prognostically, as a warning system for high pollutionconcentrations.

3.4 CITYSIM - noise model

The use of computer simulation, case studies or physical models are used fortraffic noise analysis outdoors and for acoustical analysis of halls indoors. Theuse of computer simulation rather than case studies or physical models shouldprovide a greater degree of reliability, and flexibility to the results achieved. Inthis paper a basis for a computer simulation model of noise in cities (CITYSIMNoise Model) is described. In a noise model of city, both the noise source andthe noise propagation must be modeled. For instance, The Nordic Traffic NoiseComputing Model is executed as numerous computer-versions by differentconsulting companies. The modeling of noise source and propagation in theNordic model and in other traffic noise models is not very suitable for computermodeling of noise in cities.

Some attributes of propagation are better considered in the indoor noise models,for example, in the room acoustic computer model Odeon created by Tech. Univ.of Denmark. Sound propagation in city must therefore be modeled with a model,which is a hybrid of outdoor and indoor noise models. The problem of urbannoise pollution can almost be considered as an architectural acoustics problem.City can be treated in a manner, which is mathematically analogous to theacoustics of rooms.



In current acoustical theory there are five main sound propagation attributes thatare considered to be most important for the design of the noise model of city:attenuation due to geometrical divergence, attenuation due to air absorption,attenuation due to screening, attenuation due to the ground effect and effect ofreflections. Today, acoustical theory is considered to be quite advanced andstandardized, with these factors which affect the propagation of sound outdoors(ISO 9613-2.2). Recent developments in computer modeling now enable theaccurate modeling of the basic acoustical forms on which today's acoustic theoryis based.

The CITYSIM Noise Model, which will be created in this project, can thereforebe based on the acoustical theory known in a new way suitable for computerprogramming. Most of the sound propagation attributes are frequency-dependent.Current traffic noise models assume 500 Hz as a representative sound frequencyand apply attenuations respectively. To increase the precision of traffic noisepredictions, a model which accounts for the frequency-dependent nature ofsound propagation and source characterization is needed - supporting the idea toincorporate frequency-based algorithms in the CITYSIM Noise Model.

Traffic noise models predict the equivalent A-weighted sound pressure levelswhich observers are subjected to. A-weighted sound pressure level characterizesloudness of sound. Thorough sound quality and noise-effect evaluation needsalso predictions of sound sharpness i.e.frequency-contents and fluctuationstrength of sound pressure level at least. CITYSIM Noise Model shall be able topredict all parameters, which are relevant to evaluation of effects of noise.CITYSIM Noise Model will be composed of the following steps. The soundemission levels and sound directivity produced by various sources (cars andtrucks) in the city are found by measuring or by prediction models. Theacoustical characteristics of the city are modeled. Vehicle locations andmovements on roadways are specified. Predicative noise level equations basedon the sound level from a point source and the physical characteristics of the cityare generated. Noise levels at the receiving points is calculated and visualized.

3.5 Visualization

The role of graphics is very important in microscopic simulation of complextransportation systems. Interactive use of graphics is essential in the developmentof the model. It is much easier to locate errors and problem areas in the model bylooking at the graphic presentation of the system with objects imitating the realoperations than it is by checking numerical results. The development of three-dimensional graphics has given the user the freedom to move inside the modeland look at it from different angles.

The visuality is an important feature in modeling. Visuality means that the objectmodel, its structure and operation, is shown to the user in other ways than mereseries of numbers. In visual orientation a graphic "picture" is shown to the userinstead of plain characters. Normally, visuality offers a good interaction between



the user and the model. Entering data and obtaining output can be simplified bythe visual framework. Visuality also improves the communication betweenvarious users. It offers a common language between various people in systemdevelopment and usage. A graphical representation of a model and itscomponents helps in communication between programmers, researchers, expertsand regular users.

The visuality is important in usage of the object models. The on-linerepresentation of vehicles and other objects during simulation is also calledanimation. The animation offers the user a rapid overview of functioning of theexamined traffic system. Capacity problems, lane blockages, etc. can be locatedand any unexpected behavior can be found by observing the animation for sometime. Animation is an important facility in demonstrating proposed solutions todecision-makers and public. Animation is also an excellent tool for error seeking.(Kosonen, 1996.)

Figure 2. Example of visualization (Niittymaki, 1999).



4. Conclusions

There is a lack of validated, versatile and user-friendly traffic planning andenvironmental assessment tools in the urban scale. This paper outlined anintegrated system, which contains micro- and macro-level traffic simulationtools, results from a numerical weather prediction model, emission andatmospheric dispersion models, a population exposure model, noise dispersionmodels and visualization of the simulation results.

5. References

Bremer, P., 1993. Assessment of two methods to predict SO% concentrations inthe Helsinki area. Finnish Meteorological Institute, Publications on Air Quality15. Helsinki, 42 p.

Bremer, P. and Valtanen, K., 1995. Air pollution predictions in Finland. In:Anttila, P. et al. (ed.), Proceedings of the 10th World Clean Air Congress, Espoo,Finland, May 28 - June 2, 1995. Vol. 2. The Finnish Air Pollution PreventionSociety, Helsinki, p. 258 (4 pages).

City Simulator Feasibility Study (1998). Final Report to Ministry of Trade andIndustry, Helsinki, Finland.

Harkonen, J., Valkonen, E., Kukkonen, J., Rantakrans, E., Lahtinen, K.,Karppinen, A. and Jalkanen, L., 1996. A model for the dispersion of pollutionfrom a road network. Finnish Meteorological Institute, Publications on AirQuality 23. Helsinki, 34 p.

Kukkonen, J., Salmi, T., Saari, H., Konttinen, M. and Kartastenpaa, R., 1999.Review of urban air quality in Finland. Boreal Environment Research, Vol. 4,No. 1, pp. 55-65.

Karppinen, A., Joffre, S. and Vaajama, P., 1997. Boundary layer parametrizationfor Finnish regulatory dispersion models. International Journal of Environmentand Pollution, Vol. 8, Nos. 3-6, p. 557-564.

Karppinen, A., Kukkonen, J., Konttinen, M., Harkonen, J., Valkonen, E.,Rantakrans, E., Koskentalo, T., and Elolahde, T., 1998a. The emissions,dispersion and chemical transformation of traffic-originated nitrogen oxides atthe Helsinki metropolitan area. International Journal of Vehicle Design, Vol. 20,Nos. 1-4, p. 131-136.

Karppinen, A., Kukkonen, J., Nordlund, G., Rantakrans, E. and Valkama, I.,1998b. A dispersion modelling system for urban air pollution. FinnishMeteorological Institute, Publications on Air Quality 28. Helsinki, 58 p.



Karppinen, A., S. M. Joffre and J. Kukkonen, 1999. The refinement of ameteorological preprocessor for the urban environment. International Journal ofEnvironment and Pollution (in print).

Kosonen 1., 1996. HUTSIM - Simulation Tool for Traffic Signal ControlPlanning. Licetiate thesis, publication 89, Helsinki University of Technology,Transportation Engineering, Espoo, Finland, 121 pages.

Kukkonen, J., Harkonen, J., Valkonen, E., Karppinen, A. and Rantakrans, E.,1997. Regulatory dispersion modelling in Finland. International Journal ofEnvironment and Pollution, Vol. 8., Nos. 3-6, p. 782-788.

Niittymaki J., 1999. Microscopic simulation as a visualization tool. UrbanMobility Professional Issue 10: Traffic Information Services. April, 1999.

van Ulden, A. and Holtslag, A., 1985. Estimation of atmospheric boundary layerparameters for diffusion applications, J. Climate Appl. Meteor. 24, p. 1196-1207.


citysim - validated assessment tool for jarkko … city simulator modeling system was designed to be...

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