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首页> 外文期刊>Australian & New Zealand journal of statistics >Modelling the travel time of transit vehicles in real-time through a GTFS-based road network using GPS vehicle locations
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Modelling the travel time of transit vehicles in real-time through a GTFS-based road network using GPS vehicle locations

机译：使用GPS车辆位置实时建模运输车辆的旅行时间

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Summary 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.
机译：发明内容预测运输车辆的到达时间不仅涉及其当前位置和安排依从性，而且沿着剩余路线的交通状况。道路网络是动态的，可以从自由流动到高度拥塞的动态变化，这会影响过境车辆的到达时间，特别是经常与其他车辆分享道路的公共汽车，所以可靠的预测需要考虑实时和未来的交通状况。该过程的第一步是通过实时过渡车位数据构造可以估计道路状态（交通状况）的框架。我们所提出的框架使用粒子过滤器实现车辆模型来估计道路行程时间，该行程时间用于第二模型以估计实时交通条件。虽然开发和测试在新西兰奥克兰进行了持续的，但我们概括了每个组成部分，使框架与世界各地的其他公共交通系统兼容。我们实时展示了我们的方法的实时可行性和性能，其中R和C ++的组合用于获得必要的性能结果。未来的工作将与历史数据结合使用这些估计的交通状况，以获得运输车辆的可靠到达时间预测。

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