视频检测技术的交通时间预测实证研究

为了实现利用视频车辆检测器数据计算和预测路段行程时间，将排队长度数据应用到路段行程时间的计算中，采用改进粒子群的BP神经网络算法和时间序列分析对路段进行实证研究.将排队长度加入计算得到的决定系数为93.36%，比只有流量数据的BP神经网络算法改善了41.03%，比BPR（bureau of public roads）路阻函数算法改善了23.37%.利用实时的路段行程时间对后续行程时间预测通过时间序列分析得到相对误差为0.06，预测下个时段和下个周期的路段行程时间平均相对误差分别为0.14、0.15.结果表明排队长度对于路段行程时间的计算具有较高的准确性，可以用于城市道路交通时间的预测，并能有效为智能交通算法的其他指数计算提供思路，为改善交通状况提供决策支持.

Empirical Study on Travel Time Prediction with Video Detection Technology

In order to calculate and estimate travel time with the data of video vehicle detectors, data of queue length is applied to the calculation of travel time and the roads are researched with the improved BP neural network algorithm and time series analysis. The decision coefficient is 93.36% when queue length is added to the calculation, which is improved by 41.03% compared with the neural network algorithm for the traffic data only, and 23.37% compared with the BPR algorithm. Using real-time travel time can been used to predict the follow-up travel time. And through the time series analysis, the relative error is 0.06. The average relative errors are 0.14 and 0.15 respectively for forecasting the travel time of the next period and next cycle. Results show that the queue length has higher accuracy for calculating travel time, which can be used to predict travel time of the urban road. The algorithm can provide ideas for calculation of index for other algorithms in the field of intelligent transportation and can also provide decision support for improving the traffic situation.