Urban Traffic Modelling and Prediction Using Large Scale Taxi GPS Traces
Urban Traffic Modelling and Prediction Using Large Scale Taxi GPS Traces
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DOI：
10.1007/978-3-642-31205-2_4
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发表时间：
2012-06
期刊：
影响因子：
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通讯作者：
P. S. Castro;Daqing Zhang;Shijian Li
中科院分区：
文献类型：
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作者：
P. S. Castro;Daqing Zhang;Shijian Li
监测、预测和了解城市交通状况是城市规划和环境监测的重要问题。配备GPS的出租车可以被视为无处不在的传感器，产生的大规模数字痕迹让我们能够对城市道路网络的潜在动态有一个独特的看法。本文提出了一种基于大规模出租车轨迹的交通密度模型的构建方法。该模型可以用来预测未来的交通状况，并估计排放对城市空气质量的影响。我们认为，仅考虑交通密度不足以深入了解潜在的交通动态，因此提出了一种自动确定每个路段通行能力的新方法。我们在一个大规模的出租车GPS日志数据库上对我们的方法进行了评估，并展示了它们的优异性能。
Monitoring, predicting and understanding traffic conditions in a city is an important problem for city planning and environmental monitoring. GPS-equipped taxis can be viewed as pervasive sensors and the large-scale digital traces produced allow us to have a unique view of the underlying dynamics of a city’s road network. In this paper, we propose a method to construct a model of traffic density based on large scale taxi traces. This model can be used to predict future traffic conditions and estimate the effect of emissions on the city’s air quality. We argue that considering traffic density on its own is insufficient for a deep understanding of the underlying traffic dynamics, and hence propose a novel method for automatically determining the capacity of each road segment. We evaluate our methods on a large scale database of taxi GPS logs and demonstrate their outstanding performance.