Traffic flow prediction is an important component for real-time traffic-adaptive signal control in urban arterial networks.By exploring available detector and signal controller information from neighboring intersectio...Traffic flow prediction is an important component for real-time traffic-adaptive signal control in urban arterial networks.By exploring available detector and signal controller information from neighboring intersections,a dynamic data-driven flow prediction model was developed.The model consists of two prediction components based on the signal states(red or green) for each movement at an upstream intersection.The characteristics of each signal state were carefully examined and the corresponding travel time from the upstream intersection to the approach in question at the downstream intersection was predicted.With an online turning proportion estimation method,along with the predicted travel times,the anticipated vehicle arrivals can be forecasted at the downstream intersection.The model performance was tested at a set of two signalized intersections located in the city of Gainesville,Florida,USA,using the CORSIM microscopic simulation package.Analysis results show that the model agrees well with empirical arrival data measured at 10 s intervals within an acceptable range of 10%-20%,and show a normal distribution.It is reasonably believed that the model has potential applicability for use in truly proactive real-time traffic adaptive signal control systems.展开更多
窄路段作为交通场景中不可避免的瓶颈路段,其短时车流量预测对优化路径规划、改善交通状况具有重要意义。针对窄路段的时效性,同时考虑适用模型的准确度,提出一种基于佳点集初始化种群、非线性参数控制及柯西变异扰动的改进鲸鱼优化算法...窄路段作为交通场景中不可避免的瓶颈路段,其短时车流量预测对优化路径规划、改善交通状况具有重要意义。针对窄路段的时效性,同时考虑适用模型的准确度,提出一种基于佳点集初始化种群、非线性参数控制及柯西变异扰动的改进鲸鱼优化算法(IWOA)-门控循环单元(GRU)的窄路短时车流量预测模型,以SUMO(Simulation of Urban Mobility)仿真数据进行了实证研究。对比实验结果显示,IWOA具有较好的全局性、收敛速度且更加稳定。基于IWOA-GRU的窄路短时车流量预测模型,均方根误差(RMSE)指标相较于WOA-GRU、PSO-GRU、长短期记忆神经(LSTM)网络分别降低10.96%、28.71%、42.23%,平均绝对百分比误差(MAPE)指标分别降低13.92%、46.18%、52.83%,有较为显著的准确性和稳定性。展开更多
基金Project(71101109) supported by the National Natural Science Foundation of China
文摘Traffic flow prediction is an important component for real-time traffic-adaptive signal control in urban arterial networks.By exploring available detector and signal controller information from neighboring intersections,a dynamic data-driven flow prediction model was developed.The model consists of two prediction components based on the signal states(red or green) for each movement at an upstream intersection.The characteristics of each signal state were carefully examined and the corresponding travel time from the upstream intersection to the approach in question at the downstream intersection was predicted.With an online turning proportion estimation method,along with the predicted travel times,the anticipated vehicle arrivals can be forecasted at the downstream intersection.The model performance was tested at a set of two signalized intersections located in the city of Gainesville,Florida,USA,using the CORSIM microscopic simulation package.Analysis results show that the model agrees well with empirical arrival data measured at 10 s intervals within an acceptable range of 10%-20%,and show a normal distribution.It is reasonably believed that the model has potential applicability for use in truly proactive real-time traffic adaptive signal control systems.
文摘窄路段作为交通场景中不可避免的瓶颈路段,其短时车流量预测对优化路径规划、改善交通状况具有重要意义。针对窄路段的时效性,同时考虑适用模型的准确度,提出一种基于佳点集初始化种群、非线性参数控制及柯西变异扰动的改进鲸鱼优化算法(IWOA)-门控循环单元(GRU)的窄路短时车流量预测模型,以SUMO(Simulation of Urban Mobility)仿真数据进行了实证研究。对比实验结果显示,IWOA具有较好的全局性、收敛速度且更加稳定。基于IWOA-GRU的窄路短时车流量预测模型,均方根误差(RMSE)指标相较于WOA-GRU、PSO-GRU、长短期记忆神经(LSTM)网络分别降低10.96%、28.71%、42.23%,平均绝对百分比误差(MAPE)指标分别降低13.92%、46.18%、52.83%,有较为显著的准确性和稳定性。
