Optimising Electric Bus Departure Interval Considering Stochastic Traffic Conditions

Zhenyang Qiu; Xiaowei Hu; Shuai Song; Yu Wang

doi:10.7307/ptt.v35i5.219

Authors

Zhenyang Qiu School of Transportation Science and Engineering, Harbin Institute of Technology
Xiaowei Hu School of Transportation Science and Engineering, Harbin Institute of Technology
Shuai Song School of Transportation Science and Engineering, Harbin Institute of Technology
Yu Wang School of Transportation Science and Engineering, Harbin Institute of Technology

DOI:

https://doi.org/10.7307/ptt.v35i5.219

Keywords:

electric buses, public transit, departure interval, stochastic traffic conditions, genetic algorithm

Abstract

Electric buses (EBs) have attracted more and more attention in recent years because of their energy-saving and pollution-free characteristics. However, very few studies have considered the impact of stochastic traffic conditions on their operations. This paper focuses on the departure interval optimisation of EBs which is a critical problem in the operations. We consider the stochastic traffic conditions in the operations and establish a departure interval optimisation model. The objective function aims at minimising passenger travel costs and enterprise operation costs, including waiting time costs, congestion costs, energy consumption costs and operational fixed costs. To solve this problem, a genetic algorithm (GA) based on fitness adjustment crossover and mutation rate is proposed. Based on the Harbin bus dataset, we find that improved GA performance is 4.481% higher, and it can solve the models more accurately and efficiently. Compared with the current situation, the optimisation model reduces passenger travel costs by 20.2% and helps improve passenger travel quality. Under stochastic traffic conditions, total cost change is small, but passenger travel costs increase significantly. This indicates the high impact degree of random traffic conditions on passenger travel. In addition, a sensitivity analysis is conducted to provide suggestions for improving the EBs operation and management.

Author Biographies

Shuai Song, School of Transportation Science and Engineering, Harbin Institute of Technology

Song Shuai is a master's student at Harbin Institute of Technology, and has a bachelor's degree.

Yu Wang, School of Transportation Science and Engineering, Harbin Institute of Technology

Wang Yu is Ph.D. from Harbin Institute of Technology.

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