出租车数据的城市道路网路段通行时间估计方法

Urban link travel time estimation using taxi data

城市路段通行时间估计能够更好地运营和管理城市交通。针对包含起点–终点位置,行程时间和距离信息的GPS行程数据,提出了一种城市道路网短时通行时间的估计模型。首先将城市道路网按照交叉路口分解为多个路段,并基于k-最短路径搜索方法分析司机行进路线。然后针对每一个路段,提出了双车道通行时间多项式关联关系模型,既能提升道路网通行时间精细度,又能避免因训练数据不足导致的路网通行时间过拟合问题。最后以最小化行程期望时间和实际行程时间之间的均方误差为优化目标,拟合道路网通行时间。在纽约出租车数据集上的实验结果表明,所提模型及方法相对于传统单车道估计方法能够更准确地估计城市道路网路段的通行时间。

The accurate estimation of urban link travel time plays a significant role in urban traffic monitoring and su- pervision. Using taxicab GPS trip data, which contains origin and destination locations, travel time, and distances, this paper establishes a model to estimate average short-term urban link travel times. Firstly, the urban road network is divided into many segments based on crossings, and the running route of the driver was analyzed using the k-shortest path search algorithm. Then, for each road segment, a polynomial incidence relation model of the travel time in double lanes is proposed; this increases precision and avoids the overfitting of the travel time of the road network caused by insuffiient training data. Finally, by minimizing the mean square error between the expected path travel time and the observed path travel time as the optimization objective, the travel time of the road network is fitted. The results of experiments conducted on New York taxi datasets show that, relative to the traditional single-lane estimation method, the proposed model and method more efficiently estimate the travel time of the road segments in urban road networks.