为了制订自动驾驶车辆(AV)停车需求管理方案,搭建多智能体停车模拟框架,提出2种空载行驶收费策略:基于行驶距离的静态收费和基于道路拥堵水平的动态收费,研究费率计算方法.建立空载行驶收费策略下停车场停车、居住地停车及持续空载巡航...为了制订自动驾驶车辆(AV)停车需求管理方案,搭建多智能体停车模拟框架,提出2种空载行驶收费策略:基于行驶距离的静态收费和基于道路拥堵水平的动态收费,研究费率计算方法.建立空载行驶收费策略下停车场停车、居住地停车及持续空载巡航3种停车模式的成本函数,使用logit模型描述不同停车模式下的选择行为.利用Simulation of urban mobility(SUMO),以南宁市主城区为例开展大规模路网下的仿真实验,研究2种策略下的AV停车行为及路网运行状态变化.仿真结果表明,静态收费策略和动态收费策略下的AV空载行驶里程分别减少了20.16%和10.85%,车辆总延误分别降低了39.80%和43.52%;动态收费策略能够灵活地根据路况变化进行实时调整,路网运行效率提升更显著.展开更多
在考虑停车诱导率的基础上,综合考虑停车诱导信息板在路网中所处位置、道路交通状况和停车泊位的变化趋势等影响因素,以进入诱导区域内所有停放车辆到达停车场的车公里数(vehicle kilometers of travel,VKT)最小为目标,建立了停车诱导...在考虑停车诱导率的基础上,综合考虑停车诱导信息板在路网中所处位置、道路交通状况和停车泊位的变化趋势等影响因素,以进入诱导区域内所有停放车辆到达停车场的车公里数(vehicle kilometers of travel,VKT)最小为目标,建立了停车诱导信息板泊位状况显示优化模型。该模型可以确定某一显示时间间隔内,诱导区域内所有停车诱导信息板泊位状况显示结果的最优组合。算例分析表明,该模型是可行的,且随着停车诱导率的增加,VKT随之下降,诱导效果随之提高。展开更多
Based on an available parking space occupancy （APSO） survey conducted in Nanjing, China, an APSO forecasting model is proposed. The APSO survey results indicate that the time series of APSO with different time-secti...Based on an available parking space occupancy （APSO） survey conducted in Nanjing, China, an APSO forecasting model is proposed. The APSO survey results indicate that the time series of APSO with different time-sections are periodical and self-similar, and the fluctuation of the APSO increases with the decrease in time-sections. Taking the short-time change behavior into account, an APSO forecasting model combined wavelet analysis and a weighted Markov chain is presented. In this model, an original APSO time series is first decomposed by wavelet analysis, and the results include low frequency signals representing the basic trends of APSO and several high frequency signals representing disturbances of the APSO. Then different Markov models are used to forecast the changes of low and high frequency signals, respectively. Finally, integrating the predicted results induces the final forecasted APSO. A case study verifies the applicability of the proposed model. The comparisons between measured and forecasted results show that the model is a competent model and its accuracy relies on real-time update of the APSO database.展开更多
文摘为了制订自动驾驶车辆(AV)停车需求管理方案,搭建多智能体停车模拟框架,提出2种空载行驶收费策略:基于行驶距离的静态收费和基于道路拥堵水平的动态收费,研究费率计算方法.建立空载行驶收费策略下停车场停车、居住地停车及持续空载巡航3种停车模式的成本函数,使用logit模型描述不同停车模式下的选择行为.利用Simulation of urban mobility(SUMO),以南宁市主城区为例开展大规模路网下的仿真实验,研究2种策略下的AV停车行为及路网运行状态变化.仿真结果表明,静态收费策略和动态收费策略下的AV空载行驶里程分别减少了20.16%和10.85%,车辆总延误分别降低了39.80%和43.52%;动态收费策略能够灵活地根据路况变化进行实时调整,路网运行效率提升更显著.
文摘在考虑停车诱导率的基础上,综合考虑停车诱导信息板在路网中所处位置、道路交通状况和停车泊位的变化趋势等影响因素,以进入诱导区域内所有停放车辆到达停车场的车公里数(vehicle kilometers of travel,VKT)最小为目标,建立了停车诱导信息板泊位状况显示优化模型。该模型可以确定某一显示时间间隔内,诱导区域内所有停车诱导信息板泊位状况显示结果的最优组合。算例分析表明,该模型是可行的,且随着停车诱导率的增加,VKT随之下降,诱导效果随之提高。
基金The National Natural Science Foundation of China(No50738001)the National Basic Research Program of China (973Program) (No2006CB705501)
文摘Based on an available parking space occupancy （APSO） survey conducted in Nanjing, China, an APSO forecasting model is proposed. The APSO survey results indicate that the time series of APSO with different time-sections are periodical and self-similar, and the fluctuation of the APSO increases with the decrease in time-sections. Taking the short-time change behavior into account, an APSO forecasting model combined wavelet analysis and a weighted Markov chain is presented. In this model, an original APSO time series is first decomposed by wavelet analysis, and the results include low frequency signals representing the basic trends of APSO and several high frequency signals representing disturbances of the APSO. Then different Markov models are used to forecast the changes of low and high frequency signals, respectively. Finally, integrating the predicted results induces the final forecasted APSO. A case study verifies the applicability of the proposed model. The comparisons between measured and forecasted results show that the model is a competent model and its accuracy relies on real-time update of the APSO database.
