基于车联网的城市路网实时行程时间预测

Real-time Travel Time Prediction of Urban Road Network Based on Internet of Vehicle(IOV)

近年来,车联网技术快速发展,其不仅具备车辆对车辆和车辆对路侧的联网通讯功能,而且还能提供交通信息的实时交换功能。在车联网条件下,假设所有车辆均为浮动车,则基于浮动车和交通检测器信息可构建城市路网的行程时间预测模型。该模型针对路网行程时间进行预测,并对浮动车实时和历史数据进行比较和分析。分析结果表明:使用浮动车实时数据预测的行程时间误差最小,但变异系数很高;而使用融合模型,则误差和变异系数都较低。

In recent years,the rapid development of IOV technology enables not only vehicle - vehicle network communication, vehicle-road side network communication, but also real-time exchange of traffic information. This paper assumes, under the condition of IOV, that all vehicles are floating, then a urban road net travel time prediction model can be set up based on floating vehicles and traffic detector information. This model is used to predict travel time in road network, and comparison/analysis to real time and historical data of floating vehicles. Results show： The tolerance of travel time predicted by real time data of floating vehicles is minimum, but the coefficient of variation is quite high; By using fusion model, the tolerance and coefficient of variation are both relatively low.