Abstract

With the rapid development of society, urban traffic congestion has gradually become an important social problem that many cities need to solve. For Chongqing, traffic congestion not only affects residents’ normal travel but also brings more serious environmental pollution. Aiming at the problem of urban traffic congestion and automobile exhaust pollution, this paper adopts the system dynamics method to establish a model for studying urban traffic congestion system from the perspectives of private cars, trucks, and public transportation. First, we determine city motor vehicle trips as an indicator of the degree of traffic congestion in this paper. Second, we analyze the causal relationship between the growth of private cars, the travel of trucks, public transportation, population, and other factors and then build a model and test the stability of the model. Then, we add some practical policies to the model for policy analysis. Finally, it is concluded that the private car restriction policy is effective in controlling the amount of private car travel, and the purchase restriction policy controls the growth of the number of private cars from the root cause, but the development of public transportation is the most effective treatment measure in the long run.

Highlights

  • With the rapid development of China’s economy and the improvement of people’s income level, motor vehicles are more needed in all aspects of clothing, food, housing, and transportation. e rapid development of the use of motor vehicles has brought convenience to people and changed the pattern of the entire transportation system. e increasing popularity of online shopping has promoted the development of the logistics industry, which has led to the increasing use of freight cars and increased traffic pressure

  • Taking Chongqing as an example, the core of this paper is to analyze private car restriction policy, private car purchase restriction policy, and freight car limit policy and estimate the impact of improving the public transport sharing rate. rough the simulation of the above policies, effective suggestions can be put forward to reduce the amount of city motor vehicle travel so as to improve the valuable reference for solving the traffic congestion problem

  • As the city’s economy and population grow, the demand for people to buy vehicles increases. e growth rate of motor vehicle ownership far exceeds the growth rate of urban road area, resulting in greater traffic congestion. e increase in the amount of motor vehicles will emit more exhaust gas, resulting in greater environmental pollution, which in turn will reduce the growth of private cars. is paper attempts to mitigate the implementation of urban policy, such as controlling the growth of private car trips and freight car trips, in order to reduce the number of motor vehicle trips and alleviate traffic congestion

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Summary

Introduction

With the rapid development of China’s economy and the improvement of people’s income level, motor vehicles are more needed in all aspects of clothing, food, housing, and transportation. e rapid development of the use of motor vehicles has brought convenience to people and changed the pattern of the entire transportation system. e increasing popularity of online shopping has promoted the development of the logistics industry, which has led to the increasing use of freight cars and increased traffic pressure. With the rapid development of China’s economy and the improvement of people’s income level, motor vehicles are more needed in all aspects of clothing, food, housing, and transportation. Baidu Maps had released a report called “Research report on urban traffic in China in the third quarter of 2018” in January 2019. It was concluded from the analysis of various data indicators that Chongqing’s congestion index was as close as that of Beijing and Harbin. Baidu Map divides cities into several categories according to the number of motor vehicles: more than 3 million, more than 2 million, more than 1 million, and less than 1 million.

Literature Review
Model Description
Model Simulation
Policy Optimization
M 4 M 2 M
Findings
Conclusions
Full Text
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