Modelling and multi-criteria optimization of road traffic flows considering social and economic aspects

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MODELLING AND MULTI-CRITERIA OPTIMIZATION OF ROAD TRAFFIC FLOWS CONSIDERING SOCIAL AND ECONOMIC ASPECTS

Ferenc MESZAROS, Rita MARKOVITS-SOMOGYI, Zoltan BOKOR

1 Introduction

Growing mobility demand, increasingly congested traffic infrastructures, the augmenting environmental burden and the high social sensitivity in this direction make professional decision makers face bigger and bigger challenges. This phenomenon is all the more present in the field of road transport. The most recent transport policies show a clear direction ahead: the aims are to decrease oil dependence, ensure smaller and more efficient energy consumption, increase efficiency without limiting transport demand and enable the more efficient utilization of the whole transport infrastructure capacity by applying up-to-date traffic control and information systems in order to mitigate the most severe transport problems (e.g. congestion, air pollution and noise) [3]. Furthermore, transport planning has also the responsibility of taking into account the negative effects of this industry [4]. Thus, solutions need to be found which make our available tools more efficient but are at the same time capable of fulfilling the professional, political and social expectations [5, 6]. Besides, such “policy” type measures need to be applied which provide relatively cheap solutions and which can easily be introduced in practice [8].

2 Innovative approach to traffic control

These considerations led to the development of the CONTRA research project which aims to model and optimize road traffic flows as based on multi-criteria aspects, taking into account social and economic efficiency [2]. As a result of the project new flow control strategies are to be developed which, apart from the classical traffic parameters, integrate the social and economic effects of transport into the models as well. Thus, the generally applied transport control principles are extended to include costs of environmental pollution, safety and waiting times. The most up-to-date mathematical tools, traffic simulation applications and other innovative solutions were applied in the course of the research. After the development and tests on a prototype to be realized in a subsequent research phase, the structure of the developed system will make it capable to be implemented in practice as well.

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The basis of research is the control loop developed and extended along the principles outlined above and which is shown in Fig. 1.

Fig. 1 Outline of the extended traffic control loop

Source: [2]

The present article endeavours to introduce in general those branches of the control circuit which take into consideration the social and economic aspects. The basic formulas of the envisaged calculations are identified, furthermore, it is analyzed how the input data needed for the control process can be produced. The authors would like to give an insight into the general methodological framework or background of the complex traffic control system integrating the principles of control theory and the main factors of social-economic considerations.

3 Modelling traffic parameters and their effects

The classical approach is based on traffic parameters only, here, as a new element, the increased travel times of road users caused by congestion have also been considered. This can be calculated the following way [7]:

F TV v v MEC_congestion F

2 (1)

where

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MEC_congestion - social marginal external costs of congestion

km vehicle F - traffic volume

h vehicle

v - velocity of traffic flow ^h

km

TV - value of time ^h

4 Modelling emissions and their effects

The environmental effect of the vehicles can primarily be accorded to the polluted exhaust gases they emit. The effects can be calculated as based on the following relationship [7]:

production air

air EF FC

MEC (2)

where

MEC_air - social marginal external costs of road traffic air pollution km

vehicle

EF - emission factor

km vehicle

g

air - damage factor of direct emission g

FC - fuel consumption factor

km vehicle

g

production - damage factor of fuel production

g

In order to incorporate the aspects outlined above into the traffic control system, the recorded data regarding the environmental categories of the Hungarian road vehicle fleet have to be statistically analyzed. Since there was no public database available where these data could have been extracted from, the horizontal data of the vehicle fleet of each year and the age of the vehicles was used to determine the

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environmental categories applicable at the time of registration. The starting point of the analysis was the state of the Hungarian vehicle fleet in 2008. (Fig. 2.)

Source: [2]

In 2008 72% of the Hungarian vehicle fleet had a spark-ignition engine while 28% disposed of a compression-ignition engine, which is in line with the tendencies observed in the preceding years. The average age of the spark-ignited fleet was 8.71 years, while this index was 10.35 for their compression ignited counterparts.

Otherwise, it can be stated that the car fleet is significantly younger than in the earlier years. This phenomenon can supposedly be attributed to the fact that the age of used cars imported from the member states of the European Union had decreased. The environmental categories of the road vehicle fleet as based on EUR emission norms are shown in Fig. 3-8 for private cars, buses and lorries [11].

Fig. 3-8 Share of EURO emission classes by vehicle categories and by fuel types

Share of EURO emission classes among diesel driven passenger cars

6%

26% 29%

10% 7%

21%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

EURO 5 EURO 4 EURO 3 EURO 2 EURO 1 EURO 0

Share of EURO emission classes among gasoline driven passenger cars

3%

17%

32%

16% 9%

23%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

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Share of EURO emission classes among diesel driven buses

4%

12%

23% 17%

9%

35%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Share of EURO emission classes among gasoline driven buses

0% 2% 7%

15%

3%

72%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Share of EURO emission classes among diesel driven lorries

5%

18%

36%

21%

7% 13%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Share of EURO emission classes among gasoline driven lorries

1% 4%

21% 28%

13%

34%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Source: [11]

In order to be able to estimate how the vehicle flows are distributed on the network, the connection between the recorded data and the traffic flow on the public network has been established. As based on the first research results, the traffic performance of national and international traffic being realized on the national public network could be represented: especially on the main network elements where the dominant share of traffic flow is being realized and also, considering a city environment, on the main routes of Budapest. The EURO environmental categories of the vehicles running on the characteristic network elements have also been determined.

The vehicle flow on the national public network shows the following presumed distribution grouped along the different vehicle classes, the fuel type and EURO categories:

Table 1 Annual daily traffic volumes in vehicle categories, fuel types and EURO emission classes

Gasoline [veh/day]

Diesel [veh/day]

Total [veh/day]

Euro 0 7918 3248 11167 14 226 240 334 1289 1623

Euro 1 3099 1083 4181 1 58 59 128 694 822

Euro 2 5509 1547 7055 3 110 113 275 2083 2357

Euro 3 11017 4486 15503 1 149 150 206 3571 3777

Euro 4 5853 4022 9874 0 78 78 39 1785 1824

Euro 5 1033 928 1961 0 26 26 10 496 506

Total 34428 15313 49741 20 646 666 991 9918 10909

Lorry

Passenger car Bus

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Source: [2]

The EURO categories of the different vehicle classes in case of Budapest is distributed in the following way: it is taken into account that the economic development of Budapest is 50% higher than the national average as based on the GDP/capita ratio, thus the average age of the vehicles is also significantly different.

Accordingly, their environmental categories will also show a more favourable picture.

The subsequent diagrams for the different vehicle classes are based on derived data, thus the real life data may differ from these. (The estimates are to be validated in a later work package of the research project.) Further, it has to be noted that in case of bus transport, the traffic of gasoline driven buses has been neglected, as bus transport is dominated by diesel oil driven buses utilized in the public transport (Fig. 9 – 13).

Fig. 9-13 Share of EURO emission classes in traffic performances by vehicle categories and by fuel types

Share of EURO emission classes in traffic performances of diesel driven passenger cars

0 500 1000 1500 2000

EURO 0EURO 1EURO 2EURO 3EURO 4EURO 5 EURO emission classes

Vehkm/day/1000 pcs

Share of EURO emisssion classes in traffic performances of gasoline driven passenger cars

0 1000 2000 3000 4000 5000

EURO 0 EURO 1 EURO 2 EURO 3 EURO 4 EURO 5 EURO emission classes

Vehkm/day/1000 pcs

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Share of EURO emission classes in traffic performances of diesel driven buses

0 20 40 60 80 100

Vehkm/day/1000 buses

Share of EURO emission classes in traffic performances of diesel driven lorries

0 200 400 600 800 1000 1200

Vehkm/day/1000 lorries

Share of EURO emission classes in traffic performances of gasoline driven lorries

0 5 10 15 20

Vehkm/day/1000 lorries

Source: [2]

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5 Modelling accidents and their effects

The next branch of the control circuit is transport safety, i.e. taking into account the social and economic effects of road accidents. Accidents are one of the major external effects of transport [1]. The costs of accidents add up from the following elements: financial damage, administrative costs, medical costs, loss in production and value of risks.

m

j n

i

risk j

i j i

accident MVA

P

MEC NC^, 1 (3)

where

MEC_accident - social marginal external costs of accidents

km vehicle NC_i,j - number of injuries by vehicle type i and injury type j [-]

P_i – traffic performance by vehicle type i vehicle km MVA_j – monetary value of accident type j

- coefficient [-]

ε_risk - risk elasticity [-]

The factors mainly determining the costs are the number and the severity of accidents. Monetary cost data can be attained from the statistics of insurance companies and medical institutions. Loss of production and the value of risks can be deduced from internationally accepted norms knowing the number and the severity of accidents. One part of the transport accident costs is extern to the transport sector (it is born by the society) and principally these extern cost items are to be considered from the point of view of social pricing.

Table 2 Share of EURO emission classes in traffic performances by vehicle categories and by fuel types

Category Elements First-best

valuation

Substitute valuation

Accident costs

Property damage costs

Based on insurance

data Calculation based

on average costs and number of accidents

Administrative costs

Based on police data and other statistics

Medical costs Recorded costs of

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medical appointment

Production losses

Monetary value of loss of human resource from production (in function of salary, duration,

substitution, etc.)

Derived from Value of Statistical Life in the ratio of GDP

Risk costs

Based on Stated Preference

techniques

Source: [9]

Regarding the cost elements of accidents, the value of risks is non-monetary while the rest are monetary factors. Naturally, the evaluation of the non-monetary factors poses the bigger problem, where, ideally, different modelling and preference revealing methods are to be applied or, as a substitutive solution, the widely accepted international values are to be adapted to the national circumstances.

6 Conclusion

The statistical analysis of the road vehicle fleet in Hungary has covered the size, the composition and environmental categories of the national vehicle fleet. As based on these results, it has become possible to provide input data to the traffic model regarding the environmental (EURO) categories of the Hungarian vehicle fleet. Beside the environmental characteristics of the recorded vehicle fleet, it is necessary to know the composition of the national traffic flow and also the spatial and time-based distribution of national and, where it is observable in the performances, international traffic.

In the related analyses the traffic composition was investigated along two dimensions: with regard to the high speed network representing the core of the national public network, and at the main routes of Budapest, representing the road network in a city environment. Thus, it has become possible to integrate the environmental data of the recorded vehicle fleet into a dynamic model.

A much emphasized task of the traffic model is to incorporate the social and economic effects of the traffic flow into the decision making process, thus, as part of the research project, the most characteristic social and economic specific costs (arising due to congestion, accidents and emissions) as related to transport performance have been determined. Essential conclusion of the present phase of research is that the input

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parameters of the drafted theoretic control circuit can be produced and it is justified to further analyze the concept [10].

This work is connected to the scientific program of the " Development of quality- oriented and harmonized R+D+I strategy and functional model at BME" and

“Modelling and multi-objective optimization based control of road traffic flow considering social and economical aspects” projects. These projects are supported by the New Széchenyi Plan (Project ID: TÁMOP-4.2.1/B-09/1/KMR-2010-0002) and by program CNK 78168 of OTKA.

References

[1] Bokor, Z, Mészáros, F, Török, Á: Theoretical investigation of changes in road transport accident statistics due to legal and regulatory aspects of road traffic safety. In: Jose Viegas, Rosario Macario (ed.) 12th World Conference on Transport Research. Lisbon, Portugal, 11-15.07.2010. Lisbon: 11 p. Paper 03385.

[2] CONTRA: Periodic report (1) of Modeling and multi-objective optimization based control of road traffic flow considering social and economical aspects. OTKA CNK 78168, BME, 2010.

[3] European Commission (EC): WHITE PAPER Roadmap to a Single European Transport Area – Towards a competitive and resource efficient transport system. 2011, Brussels [4] Gogola, M: The analysis of traffic impact on urban environment in city of Zilina, LOGI

– Scientific Journal on Transport and Logistics, Vol. 1. No. 1, 2010, pp. 44-52.

[5] Markovits-Somogyi, R: Review and Systematization of Efficiency Measurement Methods Used in the Transport Sector. Promet – Traffic&Transportation, Vol. 23, No 1, 2011, pp. 39-47.

[6] Mészáros, F: Policy instruments in managing road traffic demand: literature review (in Hungarian). In: Péter, T (ed.) Innovation and sustainable surface transport – conference (IFFK-2010). Budapest, Hungary, 02-04.09.2010. Budapest: Magyar Mérnökakadémia, pp. 1-5. Paper 7.5.

[7] Mészáros, F, Török, Á: Marginal cost based road transport accounts and estimation methods (in Hungarian). In: Székely Tünde (ed.) 11^th RODOSZ conference proceedings. Cluj-Napoca, Romania, 19.11.2010. Metaforma Kft., pp. 461-468.

[8] Mészáros, F: Strategic policy instruments in managing freight transport demand (in Hungarian). Logisztikai Évkönyv 2011. pp. 128-133. Paper 128.

[9] Tánczos, L, Bokor, Z: Social costs of transportation and their general and modal dependent national specialities (in Hungarian). Hungarian Review of Transport Sciences 53:(8) pp. 281-291. (2003)

[10] Torok, A, Meszaros, F: Control possibilities of intersections based on estimated costs of traffic. In: SAMI 2012 - 10th IEEE Jubilee International Symposium on Applied Machine Intelligence and Informatics. Herlany, Slovakia, 26-28.01.2012. Herlany:

IEEE, pp. 431-434. Paper 80.

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[11] Varga, R, Kibedi-Varga, L, Markovits-Somogyi, R, Torok, A, Meszaros, F:

STATISTICAL ASSESSMENT OF TRAFFIC QUALITY IN BUDAPEST. In:

Stanislaw Borkowski, Marcin Nabialek (ed.) Toyotarity: Knowledge using in production management. Dnepropetrovsk: Yurii V Makovetsky, 2010. pp. 143-154.

Resume

This article deals with an innovative road traffic control model, considering also social and economic aspects. The approach tends to meet the latest policy, social and environmental requirements, therefore it contributes to cutting back and to making more predictable and reliable travel times, to reducing environmental impacts of road traffic and to enhancing road safety. The authors outline the planned control model.

They sketch the theoretical correlations between real processes and their economic impacts, and derive the required input parameters for the advanced control policy.

Key words

traffic control, travel time, environmental pollution, road safety

Ferenc Meszaros, Ph.D.

Budapest University of Technology and Economics

Faculty of Transportation Engineering and Vehicle Engineering Department of Transport Economics

e-mail: fmeszaros@kgazd.bme.hu

Rita Markovits-Somogyi, MSc

e-mail: rsomogyi@kgazd.bme.hu

Zoltan Bokor, Ph.D.

e-mail: zbokor@kgazd.bme.hu