Predicting the arrival time of a transit vehicle involves not only knowledge of its current position and schedule adherence, but also traffic conditions along the remainder of the route. Road networks are dynamic and can quickly change from free‐flowing to highly congested, which impacts the arrival time of transit vehicles, particularly buses which often share the road with other vehicles, so reliable predictions need to account for real‐time and future traffic conditions. The first step in this process is to construct a framework with which road state (traffic conditions) can be estimated using real‐time transit vehicle position data. Our proposed framework implements a vehicle model using a particle filter to estimate road travel times, which are used in a second model to estimate real‐time traffic conditions. Although development and testing took place in Auckland, New Zealand, we generalised each component to make the framework compatible with other public transport systems around the world. We demonstrate the real‐time feasibility and performance of our approach in real‐time, where a combination of R and C++ was used to obtain the necessary performance results. Future work will use these estimated traffic conditions in combination with historical data to obtain reliable arrival time predictions of transit vehicles.

中文翻译：

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使用GPS车辆位置，通过基于GTFS的道路网络实时建模运输车辆的行驶时间

预测运输车辆的到达时间不仅涉及其当前位置和时间表遵守情况的知识，而且还涉及沿该路线其余部分的交通状况。道路网络是动态的，可以从自由流动迅速变为高度拥堵，这会影响过境车辆的到达时间，尤其是经常与其他车辆共用道路的公交车的到达时间，因此可靠的预测需要考虑到实时和未来的交通状况。此过程的第一步是构建一个框架，利用该框架可以使用实时的过境车辆位置数据估算道路状态（交通状况）。我们提出的框架使用颗粒过滤器来估计车辆的行驶时间，从而实现车辆模型，然后在第二个模型中使用该模型来估计实时交通状况。尽管开发和测试在新西兰的奥克兰进行，但我们对每个组件进行了概括，以使该框架与世界各地的其他公共交通系统兼容。我们实时演示了我们方法的实时可行性和性能，其中使用R和C ++获得必要的性能结果。未来的工作将结合这些估计的交通状况和历史数据来获得对过境车辆的可靠到达时间的预测。