Evaluation, Classification, and Influential Factors Analysis of Traffic Congestion in Chinese Cities Using the Online Map Data

Changzhi Bian,Wenbo Kuang,Dayong Wu,Changwei Yuan

doi:10.1155/2016/1693729

Abstract

This study proposes a new method to describe, compare, and classify the traffic congestion states in 23 Chinese cities using the online map data and further reveals the influential factors that may affect them. First, the real-time traffic congestion information is obtained from the online map of AutoNavi in a 15-minute interval. Next, a new measuring index is introduced to describe the overall characterization of congestion patterns in each city based on online map data, which is named as the congestion ratio. The next analysis is the cluster analysis based on the temporal distribution of the congestion ratio, which helps to identify groups of the selected cities with similar traffic congestion states. These cities are categorized as four groups according to the severity of traffic congestion: severely congested, less severely congested, amble, and smooth cities. Lastly, multiple linear regression models are developed to identify the primary factors that affect the congestion ratio. The result shows that the influences of per capita road area, car ownership, and vehicle miles traveled (VMT) on the congestion ratio are significant. Sensitivity analyses are also implemented in order to reveal more effective policy measures in mitigating traffic congestion in urban areas.

Highlights

Accurate description and classification of traffic congestion states of an urban transportation network will improve our understanding of the performance of an entire country or region’s transportation network
Where α is the estimable intercept term, which equals zero; Xα is the 1/per capita road area; Xq represents the vehicles per 1,000 people; Xm is the daily Vehicle Mileage Traveled (VMT); βα, βq, and βm are the estimable coefficient vectors of Xα, Xq, and Xm
One of the main contributions of this study is that it provides a feasible way for researchers, especially for those in countries with limited standard traffic data, to analyze traffic congestion from the online map data

Summary

Introduction

Accurate description and classification of traffic congestion states of an urban transportation network will improve our understanding of the performance of an entire country or region’s transportation network. Over the past 50 years, a lots of studies focused on distinguishing congestion state on certain road links. Researchers have developed a huge number of traffic flow models (see, e.g., reviews [1,2,3]), including well-known Macroscopic Fundamental Diagram (MFD) [4, 5], and Kerner’s three-phase traffic theory [6,7,8,9,10] (see papers of Rehborn et al [11, 12] about the development of Kerner’s theory) is used to distinguish and describe congestion patterns on road links. A standard similar to HCM had been established in China These standards were mainly based on proximity to other vehicles, travel speed, volume capacity ratio, and so forth. Road-link based congestion evaluation methods above are detailed and accurate, they can hardly be used to evaluate the traffic congestion condition in the whole wide city and further to compare different cities’ congestion circumstances

Methods

Results

Conclusion