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Author:

Zhou, Yuyang (Zhou, Yuyang.) | Zhao, Qianyang (Zhao, Qianyang.) | Chen, Yanyan (Chen, Yanyan.) (Scholars:陈艳艳) | Lee, Der-Horng (Lee, Der-Horng.) | Pang, Jiajun (Pang, Jiajun.) | Liu, Guopeng (Liu, Guopeng.)

Indexed by:

EI Scopus

Abstract:

With increased urbanization, the rapid growth in the travel demand of rural residents has put enormous pressure on public transit systems. The above-average running length costs long turnover time inevitably. Sometimes the ridership fluctuates with high opportunity cost in the urban segment. The ridership and bus arrival time (BAT) from smart card data (SCD) are introduced to analyze the travel demand and the bus operation conditions. Semi-buspool scheduling is proposed to improve the flexibility and the utility of the intercity bus operation. The pooling is operated at a key station, where the first bus runs along the initial route to the terminal in the central city with the pooling passengers, whereas the following bus turns back as the zone bus route. The optimization object of this scheduling is to maximize the ridership and minimize the transit opportunity cost, in terms of the benefit. The restrictions address both the operation resource and the service level for the passengers, including the waiting time and the load factor. An intercity bus line of 38 kilometers in Beijing is taken as the case study, with 2,718 OD pairs of SCD at the morning peak. Under the novel bus-pooling schedule, the operation efficiency is optimized as the transit ridership can increase by 20.0% with notably opportunity cost decrease. © 2019 IEEE.

Keyword:

Urban transportation Intelligent vehicle highway systems Buses Intelligent systems Bus transportation Smart cards Scheduling

Author Community:

  • [ 1 ] [Zhou, Yuyang]Beijing University of Technology, Key Laboratory of Advanced Public Transportation Science, Beijing Key Laboratory of Traffic Engineering, Ministry of Transport, No. 100 Pingleyuan, Chaoyang District, Beijing; 100124, China
  • [ 2 ] [Zhao, Qianyang]Beijing University of Technology, Academy of Transportation Sciences, Beijing Key Laboratory of Traffic Engineering, Ministry of Transport, 240 Huixinli, Chaoyang District, Beijing; 100029, China
  • [ 3 ] [Chen, Yanyan]Beijing University of Technology, Key Laboratory of Advanced Public Transportation Science, Beijing Key Laboratory of Traffic Engineering, Ministry of Transport, No. 100 Pingleyuan, Chaoyang District, Beijing; 100124, China
  • [ 4 ] [Lee, Der-Horng]National University of Singapore, Beijing Key Laboratory of Traffic Engineering, Department of Civil Engineering, 1 Engineering Drive 2, Singapore; 117575, Singapore
  • [ 5 ] [Pang, Jiajun]Beijing University of Technology, Key Laboratory of Advanced Public Transportation Science, Beijing Key Laboratory of Traffic Engineering, Ministry of Transport, No. 100 Pingleyuan, Chaoyang District, Beijing; 100124, China
  • [ 6 ] [Liu, Guopeng]Beijing University of Technology, Key Laboratory of Advanced Public Transportation Science, Beijing Key Laboratory of Traffic Engineering, Ministry of Transport, No. 100 Pingleyuan, Chaoyang District, Beijing; 100124, China

Reprint Author's Address:

  • [zhou, yuyang]beijing university of technology, key laboratory of advanced public transportation science, beijing key laboratory of traffic engineering, ministry of transport, no. 100 pingleyuan, chaoyang district, beijing; 100124, china

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Source :

Year: 2019

Page: 752-757

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 15

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