To understand the route choices of public transport users, it is important to know the information available to them, and the context present at that moment. In fact, each choice situation in a transport network has different characteristics and possibilities, also depending on the current status of the transport network. In this regard, travel diaries based on tracking technologies can capture precise observations for a long term. In this work, we exploit a large-scale tracking dataset, collected through a mode detection algorithm, to understand route choices of public transport users. We propose a choice set generation algorithm, able to cover more than 94% of the collected trips without any computational constraint. We compare the users’ paths in the public transport network with different choice sets, under multiple performance indicators, including coverage, size, and fit. This latter is computed by the estimation of a Path Size Logit model.

The use of Automatic Vehicle Location (AVL) data allows comparing the available paths in terms of public transport vehicles used. We also consider different information provisions of network conditions and disturbances (full knowledge, no knowledge and current knowledge), and study which information provision best represents the choice set inferred by the observed users’ behaviour. Estimating a Mixed Path Size Logit model, we identified high heterogeneity among the users in only a few aspects. Overall, a condition of no knowledge results as the best fit, i.e. users seem to take into account in a minor way the realized delays in the alternatives considered when deciding their public transport route.

要了解公共交通用户的路线选择，重要的是要了解他们可获得的信息以及当时的情况。实际上，传输网络中的每种选择情况都有不同的特征和可能性，这也取决于传输网络的当前状态。在这方面，基于跟踪技术的旅行日记可以长期捕获精确的观测结果。在这项工作中，我们利用通过模式检测算法收集的大规模跟踪数据集来了解公共交通用户的路线选择。我们提出了一种选择集生成算法，该算法可以覆盖94％以上的所收集行程，而没有任何计算约束。在多个效果指标下，我们将用户在公共交通网络中的路径与不同的选择集进行比较，包括覆盖范围，尺寸和适合度。后者是通过估计路径大小Logit模型来计算的。

使用自动车辆定位（AVL）数据可以根据所使用的公共交通工具比较可用的路径。我们还考虑了网络状况和干扰的各种信息规定（全面知识，无知识和当前知识），并研究哪种信息规定最能代表观察到的用户行为推断出的选择集。估计混合路径大小Logit模型，我们仅在几个方面确定了用户之间的高度异质性。总体而言，没有知识的条件会导致最佳匹配，即，用户在决定其公共交通路线时似乎以较小的方式考虑了所考虑的替代方案的实际延误。