Identifying critical transfer zones to coordinate transit with on-demand services using crowdsourced trajectory data
Identifying critical transfer zones to coordinate transit with on-demand services using crowdsourced trajectory data
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DOI：
10.1080/15472450.2022.2132389
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发表时间：
2022-11
影响因子：
3.6
通讯作者：
Jiahua Qiu;Yue Jing;Wang Peng;L. Du;Yujie Hu
中科院分区：
文献类型：
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作者：
Jiahua Qiu;Yue Jing;Wang Peng;L. Du;Yujie Hu
摘要本研究开发了一种数据驱动的方法，用于确定城市中的关键换乘区，以促进公交和新兴按需服务的协调。首先，该方法将轨迹转换成具有最佳立方体尺寸的3D网格.在此基础上，我们放大并研究立方体中每个模式的轨迹密度，并通过热图呈现结果。之后，我们通过聚类算法放大并聚合这些立方体信息片段，以探索两个关键模式：许多拼车旅行经过的拼车群（RS）区域，以及“三明治模式”区域，其中一个公交轨迹主导区域被两个拼车轨迹主导区域夹在中间。我们的数值分析证实，这些RS区是很好的相关性，有前途的地区/走廊，整合过境和按需服务的“三明治模式”，帮助发现第一/最后一英里（FLM）区。最后，我们进一步开发了一个双通道深度学习网络来预测FLM间隙的变化，以便规划自适应服务。基于成都市二环线地区的实测数据进行的实例分析验证了该方法的有效性和可行性。
Abstract This study develops a data-driven approach for identifying critical transfer zones in the city to facilitate the coordination of transit and emerging on-demand services. First, the methods convert the trajectories into a 3 D grid with an optimal cube size. Built upon that, we zoom in and study the trajectory density of each mode in a cube and present the results by heatmaps. After that, we zoom out and aggregate those cube information fragments through the clustering algorithms to explore two critical patterns: the ridesharing swarm (RS) zones where many ridesharing trips go through, and the “sandwich pattern” zones where a transit trajectory dominant zone is sandwiched by two ridesharing trajectory dominant zones. Our numerical analysis confirms that these RS zones are well correlated to the promising areas/corridors for integrating transit and on-demand services; the “sandwich patterns” help discover first/last mile (FLM) zones. Last, we further develop a two-channel deep learning network to predict the variation of the FLM gaps so that adaptive services can be planned. A case study based on the field data of the second ring region of Chengdu, China confirms the effectiveness and capability of our analysis approach.