Volume 46 Issue 12
Dec.  2020
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DAI Zhuang, CHEN Xi, MA Xiaoleiet al. Semi-autonomous driving bus platooning and scheduling optimization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(12): 2284-2292. doi: 10.13700/j.bh.1001-5965.2019.0627(in Chinese)
Citation: DAI Zhuang, CHEN Xi, MA Xiaoleiet al. Semi-autonomous driving bus platooning and scheduling optimization[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(12): 2284-2292. doi: 10.13700/j.bh.1001-5965.2019.0627(in Chinese)

Semi-autonomous driving bus platooning and scheduling optimization

doi: 10.13700/j.bh.1001-5965.2019.0627
Funds:

National Natural Science Foundation of China U1811463

National Natural Science Foundation of China 61773036

Beijing Natural Science Foundation 9172011

More Information
  • Corresponding author: MA Xiaolei, E-mail: xiaolei@buaa.edu.cn
  • Received Date: 16 Dec 2019
  • Accepted Date: 28 Feb 2020
  • Publish Date: 20 Dec 2020
  • Semi-autonomous driving bus platooning refers to the vehicle organization technology that connects bus units together through vehicle communication technologies to realize coordinated driving of vehicles and dynamic design of vehicle capacity. Based on semi-autonomous driving us platooning, a dynamic bus operation model is first proposed to model bus arrival and departure time at stops, passenger dwelling process, bus capacity constraint and onboard passenger dynamics. On this basis, a semi-autonomous driving bus scheduling optimization model is proposed to jointly optimize platooning size and bus dispatching time with the objective of the sum of the optimizing operating cost and passenger waiting time cost. An improved genetic algorithm is proposed to solve the model efficiently. The model is validated using a real-world example of bus route 55, Hangzhou, China. Simulation results show that the proposed semi-autonomous driving bus scheduling can reduce bus operating cost by 29.2% and reduce passenger waiting time cost by 18.2%, when compared with conventional human-driven bus scheduling. The result verifies the efficiency of the proposed model in scheduling semi-autonomous driving bus.

     

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