Feeder transit routing optimization of driverless electric buses for heterogeneous demands
QI Geqi1,2,3, ZOU Kaijie1, ZOU Jie1, LI Wenqian1, CAO Jingxuan1, WU Jiehao1, ZHANG Wenyi1,2
1. School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China; 2. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China; 3. Beijing Research Center of Urban Traffic Information Sensing and Service Technologies, Beijing Jiaotong University, Beijing 100044, China
Abstract：The discomfort, inconvenience, and delays caused by congestion, environmental exposure and long wait time for conventional buses, shared bicycles or taxis for short feeder trips to and from subway stations are often the main reasons for passengers to quit public transportation. The wide variety of passenger service quality requirements for punctuality, speed, and comfort are difficult to fully satisfy when assuming demand homogenization. This study presents a feeder transit routing optimization method for driverless electric buses for meeting the heterogeneous demands of travelers. In the demand generation stage, users choose personalized indicators such as punctuality, speed, and comfort so that the model can fully consider the impact of personal preferences on the objective function. A vehicle route planning model is then developed with multiple routes and multiple soft time windows with the Tabu search algorithm used to solve the problem. A questionnaire is used to survey the user preferences for each demand index for a case study of the Beijing Fengtai Science and Technology Park subway station and its surrounding area of 4 km2. This method significantly improves the ability of the planning results to satisfy heterogeneous commuter demands than the traditional method.
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2022, Vol. 62 
