Scholarlink

<p>Shared autonomous vehicles (SAVs) are reshaping the mobility system, with ride-hailing services being regarded as a pioneering application market. Despite its benefits, SAVs inevitably exacerbate the competition for limited curb space, which is already a scarce resource. Designated pick-up and drop-off sites, if reasonably planned, present a promising solution to this challenge. In this paper, we propose a novel network-based data-driven approach to optimize PUDO locations for SAVs. Network kernel density estimation (NKDE) is used to measure the PUDO demand along the road network based on historical ride-hailing data. The results are fed into a maximal coverage location problem (MCLP) model to optimize the spatial distribution of PUDO sites by maximizing the demand they can cover. The spatial optimization model is used both prescriptively and descriptively, assessing transport policies and enhancing the operations of SAVs. Results show that our model outperforms the baseline models. We also reveal that a suitable PUDO site density is about 20/10 <i>km</i><sup>2</sup>, with denser sites in bustling urban centre and sparser sites in suburbs. The framework is highly generalizable and reproducible and can be extended to future studies.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Network-based pick-up and drop-off location optimization for shared autonomous vehicles

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月7日

查看全文
  • Sunyu Wang,
  • Fangyi Ding,
  • Yineng Wang,
  • Anthony Gar on Yeh,
  • Guan Huang

摘要

Shared autonomous vehicles (SAVs) are reshaping the mobility system, with ride-hailing services being regarded as a pioneering application market. Despite its benefits, SAVs inevitably exacerbate the competition for limited curb space, which is already a scarce resource. Designated pick-up and drop-off sites, if reasonably planned, present a promising solution to this challenge. In this paper, we propose a novel network-based data-driven approach to optimize PUDO locations for SAVs. Network kernel density estimation (NKDE) is used to measure the PUDO demand along the road network based on historical ride-hailing data. The results are fed into a maximal coverage location problem (MCLP) model to optimize the spatial distribution of PUDO sites by maximizing the demand they can cover. The spatial optimization model is used both prescriptively and descriptively, assessing transport policies and enhancing the operations of SAVs. Results show that our model outperforms the baseline models. We also reveal that a suitable PUDO site density is about 20/10 km2, with denser sites in bustling urban centre and sparser sites in suburbs. The framework is highly generalizable and reproducible and can be extended to future studies.