Public transport plays a crucial role in economic and social integration by facilitating mobility. However, disruptions frequently impact public transport services, resulting in delays and cancellations that significantly hinder mobility and annoy passengers. This paper focuses on improving mobility resilience through service management, in the stage of line planning where a set of lines for operating services is developed. We propose a line planning model (in an integer linear programming) for public transport networks to determine the offered lines and their frequencies, with consideration of multiple service types such as train, tram, and bus. The objective comprises two terms: passenger convenience and service resilience, with respect to the given operating budget and static demand. We quantify service resilience from three aspects: connectivity, balance, and diversity. A case study is performed on a small 6-node network to examine the effectiveness of the proposed method.

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Improving Mobility Resilience in Line Planning of Urban Public Transport

  • Tianye Zhang,
  • Xiaojie Luan,
  • Lingyun Meng

摘要

Public transport plays a crucial role in economic and social integration by facilitating mobility. However, disruptions frequently impact public transport services, resulting in delays and cancellations that significantly hinder mobility and annoy passengers. This paper focuses on improving mobility resilience through service management, in the stage of line planning where a set of lines for operating services is developed. We propose a line planning model (in an integer linear programming) for public transport networks to determine the offered lines and their frequencies, with consideration of multiple service types such as train, tram, and bus. The objective comprises two terms: passenger convenience and service resilience, with respect to the given operating budget and static demand. We quantify service resilience from three aspects: connectivity, balance, and diversity. A case study is performed on a small 6-node network to examine the effectiveness of the proposed method.