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GIS-based transit trip allocation methods converting stop-level boarding and alighting trips into TAZ trips

机译：基于GIS的过境行程分配方法将停止级别登机和升降机的速度转换为TAZ TRIPS

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摘要

This study introduces a framework to improve the utilization of new data sources such as automated vehicle location (AVL) and automated passenger counting (APC) systems in transit ridership forecasting models. The direct application of AVL/APC data to travel forecasting requires an important intermediary step that links stops and activities - boarding and alighting - to the actual locations (at the traffic analysis zone (TAZ) level) that generated/attracted these trips. GIS-based transit trip allocation methods are developed with a focus on considering the case when the access shed spans multiple TAZs. The proposed methods improve practical applicability with easily obtained data. The performance of the proposed allocation methods is further evaluated using transit on-board survey data. The results show that the methods can effectively handle various conditions, particularly for major activity generators. The average errors between observed data and the proposed method are about 8% for alighting trips and 18% for boarding trips.
机译：本研究介绍了一种框架，可以提高新数据源的利用，例如自动化车辆位置（AVL）和自动乘客计数（APC）系统中的运输乘积预测模型。 AVL / APC数据的直接应用于旅行预测需要一个重要的中介步骤，将停止和活动链接和寄存在寄存和随天 - 在产生/吸引这些旅行的实际位置（在交通分析区（TAZ）级别）。基于GIS的运输途径分配方法是通过专注于考虑当Access Shed跨越多个TAZS时的情况。所提出的方法可以通过易于获得的数据来改善实际适用性。使用过境车载调查数据进一步评估所提出的分配方法的性能。结果表明，该方法可以有效处理各种条件，特别是对于主要活动发生器。观察数据和所提出的方法之间的平均误差约为8％，用于船舶旅行和18％的登机旅行。

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