ABSTRACT

Short-term travel time prediction on freeways is the most valuable information for drivers when selecting their routes and departure times. Furthermore, this information is also essential at traffic management centers in order to monitor the network performance and anticipate the activation of traffic management strategies. The importance of reliable short-term travel time predictions will even increase with the advent of autonomous vehicles, when vehicle routing will strongly rely on this information. In this context, it is important to develop a real-time method to accurately predict travel times. The present paper uses vehicle accumulation, obtained from input-output diagrams constructed from loop detector data, to predict travel times on freeway sections. Loop detector count drift, which typically invalidates vehicle accumulation measurements, is corrected by means of a data fusion algorithm using GPS measurements. The goodness of the methodology has been proven under different boundary conditions using simulated data.

抽象的

对于驾驶员来说，在选择路线和出发时间时，短期行驶时间预测是最有价值的信息。此外，此信息在流量管理中心也是必不可少的，以便监视网络性能并预期流量管理策略的激活。随着自动驾驶汽车的问世将极大地依赖于此信息，可靠的短期旅行时间预测的重要性甚至会随着自动驾驶汽车的出现而增加。在这种情况下，开发一种实时方法以准确预测行驶时间非常重要。本文利用从环路检测器数据构建的输入输出图获得的车辆累积量来预测高速公路路段的行驶时间。环路检测器计数漂移，通常会使车辆蓄积测量无效，借助于使用GPS测量的数据融合算法进行校正。使用模拟数据已在不同边界条件下证明了该方法的优越性。