To investigate the travel time prediction method of the freeway, a model based on the gradient boosting decision tree (GBDT) is proposed. Eleven variables (namely, travel time in current period T i , traffic flow in c...To investigate the travel time prediction method of the freeway, a model based on the gradient boosting decision tree (GBDT) is proposed. Eleven variables (namely, travel time in current period T i , traffic flow in current period Q i , speed in current period V i , density in current period K i , the number of vehicles in current period N i , occupancy in current period R i , traffic state parameter in current period X i , travel time in previous time period T i -1 , etc.) are selected to predict the travel time for 10 min ahead in the proposed model. Data obtained from VISSIM simulation is used to train and test the model. The results demonstrate that the prediction error of the GBDT model is smaller than those of the back propagation (BP) neural network model and the support vector machine (SVM) model. Travel time in current period T i is the most important variable among all variables in the GBDT model. The GBDT model can produce more accurate prediction results and mine the hidden nonlinear relationships deeply between variables and the predicted travel time.展开更多
In order to better describe the commuter's travel decision-making behavior under different travel environment,heterogeneous commuters and types are defined,and the commuters are divided into three types,including ...In order to better describe the commuter's travel decision-making behavior under different travel environment,heterogeneous commuters and types are defined,and the commuters are divided into three types,including conservative type,neutral type and adventure type,respectively,analysis on the travel environment supply and the travel environment demand.Suppose the travel demand obeys the gamma distribution and the capacity obeys the beta distribution,and the travel time function of different commuter type is deduced,the travel decision model based on the cumulative foreground theory is established.Analyze the example results,compared with the fluctuation of travel demand,the degradation of traffic capacity has a more significant impact on travel decision-making behavior;and different types of commuters cause different disturbances to travel decision-making behavior.展开更多
Based on air quality perception, researching through field survey forms on Shanghai＇ s public travel decisions, article give a comparative analysis and a descriptive statistics on survey data. The study found, differ...Based on air quality perception, researching through field survey forms on Shanghai＇ s public travel decisions, article give a comparative analysis and a descriptive statistics on survey data. The study found, different identity, qualifications, age, background of Shanghai＇ s Public perception to air quality perform a universal character, and the rate of concerning for air pollution is relatively high, but to air pollution, they lack of understanding of its essence. Shanghai＇s public have a large usage of mobile devices, the way of concerns for air quality shows a ＂move concern＂ feature. There is a certain influence of air quality on public＇s travel decisions, on the condition that public often go out and show their concern on air quality information.展开更多
露天矿无人矿车在装卸载作业区内运输过程中的长时间停车等待是制约露天矿无人运输系统效率提升的瓶颈。为提高无人矿车的运输效率,本文结合作业区内的运输作业流程,提出一种基于动态可行驶距离的多车协同通行决策方法。首先,将决策模...露天矿无人矿车在装卸载作业区内运输过程中的长时间停车等待是制约露天矿无人运输系统效率提升的瓶颈。为提高无人矿车的运输效率,本文结合作业区内的运输作业流程,提出一种基于动态可行驶距离的多车协同通行决策方法。首先,将决策模型建模为混合整数线性规划(Mixed Integer Linear Programming, MILP)模型,表述优化目标和问题约束;其次,考虑到求解MILP模型存在难以满足动态决策实时性的问题,基于蒙特卡洛树搜索(Monte Carlo Tree Search,MCTS)实现多车冲突消解,核心思想是利用搜索树的推演能力进行多车通行前瞻模拟,计算多车的最优通行优先级,动态调整多车的可行驶距离;此外,根据无人矿车在作业区内的作业特征设计不同的MCTS节点价值函数,实现综合考虑运输效率与作业特征的通行优先级排序;最后,设计作业区4,8,12个停车位场景下的多车通行仿真实验,与基于先到先服务(First-Come-FirstServed, FCFS)的方法进行对比,吞吐量提升22.03%~28.00%,平均停车等待时间缩短31.71%~50.79%。同时,搭建微缩智能车辆的6停车位作业区场景实验平台,多车单次运输作业总用时相比FCFS缩短了18.84%。仿真与微缩智能车辆的实验结果表明,本文提出的方法能够提升露天矿作业区多车运输效率。展开更多
基金The National Natural Science Foundation of China(No.51478114,51778136)
文摘To investigate the travel time prediction method of the freeway, a model based on the gradient boosting decision tree (GBDT) is proposed. Eleven variables (namely, travel time in current period T i , traffic flow in current period Q i , speed in current period V i , density in current period K i , the number of vehicles in current period N i , occupancy in current period R i , traffic state parameter in current period X i , travel time in previous time period T i -1 , etc.) are selected to predict the travel time for 10 min ahead in the proposed model. Data obtained from VISSIM simulation is used to train and test the model. The results demonstrate that the prediction error of the GBDT model is smaller than those of the back propagation (BP) neural network model and the support vector machine (SVM) model. Travel time in current period T i is the most important variable among all variables in the GBDT model. The GBDT model can produce more accurate prediction results and mine the hidden nonlinear relationships deeply between variables and the predicted travel time.
基金Sponsored by the National Natural Science Foundation of China(Grant o.50808021)
文摘In order to better describe the commuter's travel decision-making behavior under different travel environment,heterogeneous commuters and types are defined,and the commuters are divided into three types,including conservative type,neutral type and adventure type,respectively,analysis on the travel environment supply and the travel environment demand.Suppose the travel demand obeys the gamma distribution and the capacity obeys the beta distribution,and the travel time function of different commuter type is deduced,the travel decision model based on the cumulative foreground theory is established.Analyze the example results,compared with the fluctuation of travel demand,the degradation of traffic capacity has a more significant impact on travel decision-making behavior;and different types of commuters cause different disturbances to travel decision-making behavior.
文摘Based on air quality perception, researching through field survey forms on Shanghai＇ s public travel decisions, article give a comparative analysis and a descriptive statistics on survey data. The study found, different identity, qualifications, age, background of Shanghai＇ s Public perception to air quality perform a universal character, and the rate of concerning for air pollution is relatively high, but to air pollution, they lack of understanding of its essence. Shanghai＇s public have a large usage of mobile devices, the way of concerns for air quality shows a ＂move concern＂ feature. There is a certain influence of air quality on public＇s travel decisions, on the condition that public often go out and show their concern on air quality information.
文摘露天矿无人矿车在装卸载作业区内运输过程中的长时间停车等待是制约露天矿无人运输系统效率提升的瓶颈。为提高无人矿车的运输效率,本文结合作业区内的运输作业流程,提出一种基于动态可行驶距离的多车协同通行决策方法。首先,将决策模型建模为混合整数线性规划(Mixed Integer Linear Programming, MILP)模型,表述优化目标和问题约束;其次,考虑到求解MILP模型存在难以满足动态决策实时性的问题,基于蒙特卡洛树搜索(Monte Carlo Tree Search,MCTS)实现多车冲突消解,核心思想是利用搜索树的推演能力进行多车通行前瞻模拟,计算多车的最优通行优先级,动态调整多车的可行驶距离;此外,根据无人矿车在作业区内的作业特征设计不同的MCTS节点价值函数,实现综合考虑运输效率与作业特征的通行优先级排序;最后,设计作业区4,8,12个停车位场景下的多车通行仿真实验,与基于先到先服务(First-Come-FirstServed, FCFS)的方法进行对比,吞吐量提升22.03%~28.00%,平均停车等待时间缩短31.71%~50.79%。同时,搭建微缩智能车辆的6停车位作业区场景实验平台,多车单次运输作业总用时相比FCFS缩短了18.84%。仿真与微缩智能车辆的实验结果表明,本文提出的方法能够提升露天矿作业区多车运输效率。
