Urban public transport choice behavior analysis and service improvement policy-making: a case study from the metropolitan city, Chengdu, China

Abstract

As the metropolitan city in Western China, Chengdu has been suffered from serious traffic congestion. The strategy of urban public transport priority was put into agenda to relieve traffic congestion. But the public transport sharing rate is only 27% in Chengdu which is much lower than the developed country. Consequently, it is of great importance to study the measures to improve the service, and provide technical support to the policy-makers. This paper selected the traffic corridor between Southwest Jiaotong University district and downtown as the experiment subject. The orthogonal design was used to generate stated preference questionnaires in order to achieve the reliable parameter estimates. Some variables were used to define the utility of the three alternatives and construct the Logit model. Then, the relationships between the cost, time variable and the choice probability of the public transport were analyzed. According to the results, we found that the orthogonal design does improve the goodness-of-fit. The workability of Multinomial Logit Model was better than Nest Logit model. We also put forward some effective measures to improve the service level of public transit, including reducing the access time to Metro, limiting parking supply to control the car use.

Keywords:

• correlation coefficient
• choice probability
• hypothesis test
• elasticity
• goodness of fit

Additional information

Funding

This work was supported by National Science Foundation of China under grant No. 50908195 and 51178403, the Fundamental Research Funds for the Central Universities (No. SWJTU11CX080 and No. 2682014CX130), Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University (No. K201207), and Supported by Program for New Century Excellent Talents in University (NCET-13-0977) and the Science and Technology Innovation Practice Program for Graduate Student, Southwest Jiaotong University (No. YC201407119).