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Identification Method for Optimal Urban Bus Corridor Location

Author

Listed:
  • Weichuan Yin

    (One Belt–One Road Strategy Institute, Tsinghua University, Beijing 100084, China)

  • Yingqun Zhang

    (External Liaison Office, Beijing Jiaotong University, Beijing 100044, China)

Abstract
Locating urban bus corridors based on corridor characteristics can increase the transportation capacity, improve transportation efficiency, and increase the attractiveness and commercial value of urban bus corridors. In this paper, we describe the comprehensive optimization of the urban bus corridor location and setting of bus lanes, while considering the aggregation effect of the corridor. First, we use a K- shortest path algorithm to generate a candidate set of bus corridors. Then, we analyze the influencing factors of the bus corridor. Following this, we take the minimum generalized cost and the maximum aggregation utility along the path as the objective function and design a bus corridor location identification optimization model, considering arc capacity, plot ratio, corridor development, and time constraints. Finally, we examine the real-world example of the Beijing city and identify the location of the bus corridors in the morning and evening peak hours. The one-way traffic of most of the roads identified as bus corridors was found to be greater than 6671 people/h. Thus, the location of the bus corridor and setting of bus lanes in the corridor are closely related to passenger flow, and the method can provide scientific guidance for transportation and urban planning departments and facilitate collaboration between these departments.

Suggested Citation

  • Weichuan Yin & Yingqun Zhang, 2020. "Identification Method for Optimal Urban Bus Corridor Location," Sustainability, MDPI, vol. 12(17), pages 1-22, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:7167-:d:407776
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    References listed on IDEAS

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    Cited by:

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    3. Li, Jin-Yang & Teng, Jing & Wang, Hui, 2024. "Measuring route diversity in spatial and spatial-temporal public transport networks," Transport Policy, Elsevier, vol. 146(C), pages 42-58.
    4. Jiajun Shen & Guangchuan Yang, 2020. "Crash Risk Assessment for Heterogeneity Traffic and Different Vehicle-Following Patterns Using Microscopic Traffic Flow Data," Sustainability, MDPI, vol. 12(23), pages 1-18, November.

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