A robust method for estimating transit passenger trajectories using automated data
A robust method for estimating transit passenger trajectories using automated data
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DOI：
10.1016/j.trc.2018.08.006
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发表时间：
2018-10-01
影响因子：
8.3
通讯作者：
He, Qing
中科院分区：
文献类型：
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作者：
Kumar, Pramesh;Khani, Alireza;He, Qing
制定起点-目的地需求矩阵对于过境规划至关重要。自动化的过境智能卡数据有助于开发过程，从而可以逐个挖掘上下车模式。本研究提出了一种新的行程链方法，使用自动票价收集（AFC）和一般公交饲料规格（GTFS）的数据，以推断最有可能的个人过境乘客的轨迹。该方法放宽了现有的行程链算法中使用的各种参数，如转移步行距离阈值，缓冲距离选择登机位置，选择车辆行程的时间窗口等的假设。该方法还解决了与AFC系统记录的GPS定位错误或从GTFS数据中选择不正确的子路线相关的问题。所提出的行程链方法生成一组候选轨迹为每个AFC标签到达下一个标签，计算每个轨迹的概率，并选择最有可能的轨迹来推断登机和下车站。该方法适用于明尼苏达州双子城的交通数据，该数据具有开放的交通系统，乘客在登机时（或在付费出口公交车上下车时）只需点击智能卡一次。基于乘客的连续标签，所提出的算法也被修改为付费出口的情况。该方法与研究人员开发的以前的方法相比，在推断的情况下，显示出改善。最后，将结果可视化，以了解行程的路线乘客量和地理模式。
Development of an origin-destination demand matrix is crucial for transit planning. The development process is facilitated by automated transit smart card data, making it possible to mine boarding and alighting patterns on an individual basis. This research proposes a novel trip chaining method which uses Automatic Fare Collection (AFC) and General Transit Feed Specification (GTFS) data to infer the most likely trajectory of individual transit passengers. The method relaxes the assumptions on various parameters used in the existing trip chaining algorithms such as transfer walking distance threshold, buffer distance for selecting the boarding location, time window for selecting the vehicle trip, etc. The method also resolves issues related to errors in GPS location recorded by AFC systems or selection of incorrect sub-route from GTFS data. The proposed trip chaining method generates a set of candidate trajectories for each AFC tag to reach the next tag, calculates the probability of each trajectory, and selects the most likely trajectory to infer the boarding and alighting stops. The method is applied to transit data from the Twin Cities, MN, which has an open transit system where passengers tap smart cards only once when boarding (or when alighting on pay-exit buses). Based on the consecutive tags of the passenger, the proposed algorithm is also modified for pay-exit cases. The method is compared to previous methods developed by the researchers and shows improvement in the number of inferred cases. Finally, results are visualized to understand the route ridership and geographical pattern of trips.