The battle for kerbside space: An evaluation of the competition between car-hailing and bus services
通过模拟真实公交走廊，研究网约车与公交对路缘车道的竞争如何影响网络拥堵，发现提高公交分担率和实施公交优先政策可缓解网约车带来的负面效应。
• Urban kerbsides face increased rule-breaking due to intense access demand. • Car-hailing services disrupt networks, emphasizing need for kerbside management. • Greater public transport use benefits all, while car-hailing can impede network flow. • Bus improvement policies can prevent adverse effects of car-hailing on network speed. • Future kerbside design requires integration of evolving transport technologies. The kerbside of today is a complex environment with a huge diversity of uses and highly dynamic patterns of use, including for parking, (un)loading, EV charging, and bus stops. The competition for kerbside space is to become more fierce with the rising of car-hailing services (CHS) and soon the connected and autonomous vehicles (CAVs). The kerbside is an often overlooked yet critical urban infrastructure which requires better understanding of its varies uses and functions. In this paper, we examine the competition between CHSs and traditional buses on the use of kerbside lanes. More specifically, we investigate the relationship between the mode share of car-hailing and bus services, network congestion and the performance of bus priority strategies along a real-life bus corridor and quantify their effects through simulation modelling of a set of future scenarios. The results show that the increasing penetration of car-hailing services will negatively impact network performance due to the growing number of kerbside stops, while increasing the share of public transport can help mitigate this loss and improve network resilience. Additionally, bus improvement policies, such as bus lanes and faster boarding techniques, can effectively prevent the adverse effects of car-hailing on network speed. These findings demonstrate the importance of managing kerbside space in a range of scenarios and offer valuable insights to authorities and researchers for policy-making and modelling.