URBAN MOTOR VEHICLE LIMITING POLICY BASED

ON SYSTEM DYNAMICS

Haoxiong Yang, Zhao Zhao

School of Business, Beijing Technology and Business University, Beijing, China

Yongsheng Zhou, Hao Zhang

School of Business, Beijing Technology and Business University, Beijing, China

Keywords: System dynamics, Limiting policy, Public transport, Private cars.

Abstract: The governance of urban traffic jam is a hot topic in the process of urban development in China. From the

perspective of system dynamics, this paper point out that the urban motor vehicle limiting policy used to

control traffic jam needs a strong public transport system, and if there is no convenient public transport

system, the limiting policy only has short-term effect. This paper also put forward some relevant

countermeasures at last.

1 INTRODUCTION

The most typical feature of the city is aggregation.

With the popularization of motor vehicles especially

private cars, almost all the major cities in China are

facing the pressure of traffic jam. Many urban roads

lost their function in rush hours and become a

parking lot. According to the statistics of Beijing

Traffic Management Bureau, traffic congestion in

Beijing caused a social cost of about 40 million

every day and 14.6 billion every year. It follows

that, traffic jam is not a simple transport problem,

but an economic problem even political issue. So it’s

high time to improve the traffic situation in Beijing.

Beijing implemented motor vehicle limiting

policy during the 2008 Olympic Games, and motor

vehicle was limited to travel by the tail number of

their license plate, odd days odd number and even-

numbered days even number. After the Olympics, in

order to consolidate the effect of the policy, this

provision continued, but the limiting extent was

weaker. There is no doubt that the limiting policy

can bring much positive effect, but the negative

effect caused by the policy is obvious too. However,

after the special period, whether this policy is

necessary to continue caused great controversy in

the whole society. Discuss the relation between

urban traffic jam and the limiting policy, and find

measures to alleviate urban traffic jams are of great

significance.

China’s current research on limiting policy is

still less, Shanshan Han (2009) analyzed the limiting

policy and some problems followed from the

perspective of supply and demand, and made some

recommendations accordingly. Pan Zhang (2010)

analyzed personal and social impacts of this policy

based on the cost-benefit theory of public economy,

and a number of recommendations were also

brought out. The current study is mostly qualitative

analysis, and lack of quantitative models and data

support. This research analyzed the main causal

feedback loop between traffic jams and the limiting

policy in Beijing with the use of system dynamics,

and established a system dynamics model. The

model was simulated through VENSIM (the special

software of system dynamics) on the basis of

historical statistics and the specific circumstances of

Beijing. This paper analyzed the effects of the

limiting policy through simulation, and put forward

some relevant proposals.

2 SYSTEM DYNAMICS MODEL

OF THE LIMITING POLICY

System Dynamics (SD) was founded by Forrester (a

professor of Massachusetts Institute of Technology)

in 1960s. This approach focuses on the structure and

539

Yang H., Zhao Z., Zhou Y. and Zhang H..

URBAN MOTOR VEHICLE LIMITING POLICY BASED ON SYSTEM DYNAMICS.

DOI: 10.5220/0003584205390542

In Proceedings of the 13th International Conference on Enterprise Information Systems (PMSS-2011), pages 539-542

ISBN: 978-989-8425-56-0

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

feedback mechanisms within the system, and is good

at dealing with long-period, higher-order, nonlinear,

multi- variables, and more complex feedback system

problems. Urban development problem is part of

social complex system and is suitable to use system

dynamics.

2.1 Determination of System Boundary

The behaviour analysis is based on the interaction of

elements within the system, and System dynamics

assumes that changes in the external environment

will not affect the nature of the system behaviour.

Urban transport system is a complex socio-economic

system, this paper only consider the impact limiting

policy have on urban traffic jam. In this paper, we

use the travel quantity of urban vehicles to measure

the extent of urban traffic jam, assuming that the

larger the travel quantity is the more congested

urban traffic is. City vehicles including private cars,

official cars, buses, taxis and other types of cars,

each type’s inventory, growth rate, travel quantity

will have an impact on urban traffic conditions.

The limiting policy is aimed at alleviate the

existing traffic pressure to some extent. In ideal

condition, the limiting policy can reduce the travel

quantity of urban vehicles, so as to ease urban traffic

jam. However, the limiting policy only reduced the

number of motor vehicles on the road every day, and

the demand for private cars had not changed. People

still need to work and go out every day, and the use

of private cars is limited, so there will be other

options.

The first way is to choose public transport such

as buses, taxis as substitution to private cars, which

is the original intention of the policy. However, this

requires higher availability of public transport,

which including the number of buses, the

arrangement of time, route, site and so on. The

second method is to buy a second car with different

license plate tail number. In this way, people can

enjoy the previous convenience without breaking the

rules. So our model took the service quality of

public transport and the proportion of buying a

second private car into consideration.

Based on the above analysis and the purpose of

this paper, we ultimately determine the scope of the

research system, including the inventory of urban

private cars, official cars, buses, and other motor

vehicles (including taxis, school buses, police cars,

fire engines, etc), the availability of public transport,

the total number of urban vehicle, the limiting

policy, the travel quantity of urban motor vehicle,

the growth rate of motor vehicle (growth rate of

private cars, official vehicles), the proportion of

purchasing a second private car and so on. As the

inventory and growth rate of buses and other motor

vehicles is small, we did not consider the growth

rate of buses and other motor vehicle.

2.2 The Establishment of System

Model

Causal interactions within the system determine the

function and behaviour of the system. Urban

vehicles including private cars, official cars, buses,

taxis and so on, each type’s inventory, growth rate

and the implementation of the limiting policy would

affect the travel quantity of urban motor vehicle, and

then had an impact on urban traffic conditions.

According to the analysis of the causal

relationships between each factor, as is shown in

figure 1,we use VENSIM, the special software of

system dynamics to establish a model of the impact

limiting policy have on urban traffic jams.

inventory of

private cars

annual demand of

private cars

annual learies of

private cars

rejection rate

growth rate of

private cars

total number of urban

motor vehicle

inventory of public

transport

travel quantity of

vehicles limited

urban vehicle's

travel quantiy

limiting p olicy

proportion of purchasing

a second private car

<Time>

inventory of

official vehicle

annual demand for

official cars

growth rate of

official cars

proportion of purchasing

a second official car

inventory of

other motor

vehicle

annual demand

growth rate

annual learies of

official cars

annual rejection

rate

public transport

availability

Figure 1: The stock flow chart of limiting policy’s impact on urban traffic jams.

ICEIS 2011 - 13th International Conference on Enterprise Information Systems

540

2.3 Model Equation and Parameter

Settings

System dynamics model includes level equations,

rate equations, auxiliary equations, parametric

equations and initial value equations. The model can

be simulated after entering equations to the stock

flow diagram of the system. This model took the

data of Beijing from 2004 to 2008 as model

parameters and data of 2009 as model test value.

According to "Beijing Statistical Yearbook",

statistics Beijing Traffic Management provided and

other relevant information, we set the initial value of

the model and some related parameters. Some of the

model equations are as follows:

P = 0.0667 (1)

=Ip + Ql (2)

T = Io + Ip + Ic + Iv (3)

Io = INTEG (Do

–

Lo, 32) (4)

Lo = Io * P (5)

Do = Io * Ro (6)

Iv = INTEG (A

, 50) (7)

Lp = Ip * P (8)

Ip= INTEG (Dp - Lp, 129.8) (9)

Dp = Ip * Rp (10)

Ql = IF THEN ELSE (L = 1, (Iv + Ip + Io) * (11)

= 2004 (12)

= 2020 (13)

Among the above equations, P refers to rejection

rate, Qt refers to urban vehicle’s travel quantity, Ip

refers to inventory of public transport, Ql refers to

travel quantity of vehicles limited, T refers to total

number of urban motor vehicle, Io refers to

Inventory of official cars, Ic refers to inventory of

private cars, Iv refers to inventory of other motor

vehicle, Do refers to annual demand for official cars,

Lo refers to annual learies of official cars, Ro refers

to growth rate of official cars, Ad refers to annual

demand, Dp refers to annual demand of private cars,

Lp refers to annual learies of private cars, Rp refers

to growth rate of private cars, L refers to limiting

policy, It refers to initial time, Ft refers to final time.

3 MODEL SIMULATION

AND ANALYSIS

In this paper, we used VENSIM to simulate the

model, studying the impact urban motor vehicle

limiting policy had on urban traffic conditions. The

simulation time is from 2004 to 2020, the simulation

step is 1 year. By changing relevant parameters, we

could analyze different influence degree the limiting

policy had on traffic jams under different conditions,

thereby sought to improve urban traffic condition in

effective way.

The large travel quantity of private cars is the

main cause of urban traffic jams. After the

implementation of limiting policy, people who

previously travel by private cars will have two

options: choose public transport instead of private

cars or buy a second car with different license plate

tail number. This paper conducted a simulation on

these two conditions.

3.1 Choose Public Transport

People limited by the limiting policy will choose

public transport instead of private cars if the public

transport is convenient enough , and no one would

buy a second or third car on account for the limiting

policy. Urban motor vehicle’s travel quantity before

and after the implementation of the policy is shown

in Figure 2:

Figure 2: Urban motor vehicle’s travel quantity before and

after the policy (choose public transport).

As is shown in the figure, in ideal condition,

most people will choose public transport instead of

private cars after the implementation of the policy.

So the limiting policy can reduce urban motor

vehicle’s travel quantity and play a role in the

mitigation of urban traffic jams. However, this is

based on an efficient public transport system.

3.2 The Purchase of a Second Car

After the implementation of the limiting policy,

many families choose to buy a second car because

the current public transport service is still not so

convenient. The rapid growth of motor vehicle is

slowly offset the positive effects of the limiting

policy. The purchase of a second car will weaken the

impact of the policy, and the impact of the policy

will be different with different purchase rate of a

second car. Unable to determine how many people

will buy a second car, so sensitivity analysis is

URBAN MOTOR VEHICLE LIMITING POLICY BASED ON SYSTEM DYNAMICS

541

made. Urban motor vehicle’s travel quantity before

and after the implementation of the policy when

there are 3% people buy a second car is shown in

Figure 3.

Figure 3: Urban motor vehicle’s travel quantity before and

after the policy (3% buy a second car).

As is shown in the figure, when there are 3%

people buy a second car, the limiting policy can

reduce urban motor vehicle’s travel quantity in the

short term. But in the long run, urban motor

vehicle’s inventory will be greatly increased because

the purchasing of a second car. As the base number

of limiting is much bigger, the limiting policy even

would have opposite effect, leading urban motor

vehicle’s travel quantity much greater.

Increase the proportion of second car’s

purchasing through sensitivity analysis, urban motor

vehicle’s travel quantity before and after the

implementation of the policy when there are 6.25%

people buy a second car is shown in Figure 4.

Figure 4: Urban motor vehicle’s travel quantity before and

after the policy (6.25% buy a second car).

Through the simulation of the model we can find

that when the proportion of purchasing a second car

is small, the limiting policy can play a role in the

short term, but when the purchasing ratio becomes

large enough, the role of the policy will gradually

disappear. And in the long run, the positive effects

of the limiting policy will gradually be offset by the

rapid growth of motor vehicles.

4 CONCLUSIONS

The governance of urban traffic jam is a hot topic in

the process of urban development in China, and

many cities implemented limiting policy to ease

traffic jams. This paper established a model of the

impact limiting policy had on urban traffic jams.

Through the simulation of the model with VENSIM,

we got the conclusion that the limiting policy need

the support of a strong public transport system ,

people constricted will choose public transport

instead of private cars only when the public

transport is convenient enough, thereby ease urban

traffic jams. However, many families choose to buy

a second car because the current public transport

service is still not so convenient. On this occasion,

the limiting policy only has short-term effect. In the

long run, especially when the purchasing ratio

becomes large enough, urban motor vehicle’s

inventory will be greatly increased. As the base

number of limiting is much bigger, the limiting

policy even would have opposite effect. Therefore,

simply limiting is not a scientific way and can not

fundamentally solve the problem of urban traffic

jams. In the long run, the development of public

transport can effectively ease urban traffic jams.

ACKNOWLEDGEMENTS

This work is supported by “Dynamic Allocation of

City Logistics Resource Based on the City

Sustainable Development Perspective”, a research

project of the humanities and social sciences of the

Ministry of Education of the People’s Republic of

China (No. 10YJC630324), and supported by

“Allocation of City Logistics Resource”, a project of

the Beijing Municipal Commission of Education

(No. PHR20110877).

REFERENCES

Qifan Wang, 1994, System Dynamics, Tsinghua

University Press.

Yongguang Zhong, 2009, System Dynamics, Science

Press.

Mingbo Li , 2006, Analysis on congestion charging policy

based on System Dynamics , Railway Transport and

Economy.

Beijing Statistical Yearbook, Beijing Bureau of Statistics

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