ABSTRACT 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.

中文翻译：

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基于GIS的公交出行分配方法将停靠级上下车出行转化为TAZ出行

摘要 本研究引入了一个框架，以提高新数据源的利用率，例如在公交乘客量预测模型中自动车辆定位 (AVL) 和自动乘客计数 (APC) 系统。将 AVL/APC 数据直接应用于旅行预测需要一个重要的中间步骤，将停靠点和活动（上下车）与生成/吸引这些旅行的实际位置（在交通分析区 (TAZ) 级别）联系起来。开发了基于 GIS 的交通出行分配方法，重点是考虑访问棚跨越多个 TAZ 的情况。所提出的方法通过容易获得的数据提高了实际适用性。使用过境车载调查数据进一步评估提议的分配方法的性能。结果表明，该方法可以有效地处理各种条件，特别是对于主要活动发生器。观测数据与所提出的方法之间的平均误差对于下车行程约为 8%，对于上车行程约为 18%。