为了准确预测描述出行路径决策行为,探究出行者感知及先进的出行者信息系统(advanced traveler information systems,ATIS)预测信息对决策行为的作用机制,将期望后悔理论与贝叶斯更新方法相结合,以出行时间为变量构建基于二次后悔更新...为了准确预测描述出行路径决策行为,探究出行者感知及先进的出行者信息系统(advanced traveler information systems,ATIS)预测信息对决策行为的作用机制,将期望后悔理论与贝叶斯更新方法相结合,以出行时间为变量构建基于二次后悔更新的出行路径决策模型,继而提出后悔更新价值的概念,应用数值模拟方法分析在即时性及滞后性2种不同质量水平的ATIS预测信息影响下,对二次后悔更新过程及路径决策行为的影响。研究表明:二次后悔更新过程能够有效修正路径感知偏差及期望后悔水平;常规交通状态下,即时性信息比滞后性信息场景下的二次后悔更新水平高20%,偶发性交通状态下差距可达50%,即有效及时的ATIS预测信息对于保证后悔更新效果及决策准确性具有重要作用。展开更多
为分析先进出行者信息系统(advanced traveler information system,ATIS)对交通均衡的影响,建立一个考虑多用户多准则的交通均衡模型.在模型中,根据ATIS市场占有率和用户收入水平差异将出行者分为4类,对每一类出行者,基于出行时间和出...为分析先进出行者信息系统(advanced traveler information system,ATIS)对交通均衡的影响,建立一个考虑多用户多准则的交通均衡模型.在模型中,根据ATIS市场占有率和用户收入水平差异将出行者分为4类,对每一类出行者,基于出行时间和出行费用线性组合的出行成本,根据Logit模型进行路径选择.以中国深圳市梅林关的一个起讫点(origin-destination,OD)进行实证分析,发现市民出行成本与路段拥堵系数不能同时降低.在现今经济水平下,ATIS可减少人们平均出行成本.随着经济水平的提升,人们才会慢慢利用ATIS选择可以减少平均拥堵系数的路径.最后,对模型参数进行了灵敏度分析.展开更多
灾后先进出行者信息系统(advanced traveler information system,ATIS)能及时为使用者提供交通信息,而非ATIS使用者只能根据自身历史经验调整路径。为了探究两类用户混合情景下的路网韧性,在韧性评价中引入了动态时间维度,构建了两类用...灾后先进出行者信息系统(advanced traveler information system,ATIS)能及时为使用者提供交通信息,而非ATIS使用者只能根据自身历史经验调整路径。为了探究两类用户混合情景下的路网韧性,在韧性评价中引入了动态时间维度,构建了两类用户混合日变动态配流模型,提出了基于历史流量的ATIS信息预测方法,并设计了模型求解算法。算例分析表明,所提出的ATIS信息预测方法与经典的ATIS信息预测方法相比,在研究时域内,前者支持下的平均路网韧性值相比后者提升了2.48%;针对路网韧性最优,最佳的ATIS市场占有率约为30%;当ATIS市场占有率为50%、75%、100%时,增大ATIS信息误差可提升路网韧性值,但当市场占有率为25%时,ATIS信息误差增大将导致路网韧性值降低。展开更多
Advanced traveler information systems （ATIS） can not only improve drivers＇ accessibility to the more accurate route travel time information, but also can improve drivers＇ adaptability to the stochastic network cap...Advanced traveler information systems （ATIS） can not only improve drivers＇ accessibility to the more accurate route travel time information, but also can improve drivers＇ adaptability to the stochastic network capacity degradations. In this paper, a mixed stochastic user equilibrium model was proposed to describe the interactive route choice behaviors between ATIS equipped and unequipped drivers on a degradable transport network. In the proposed model the information accessibility of equipped drivers was reflected by lower degree of uncertainty in their stochastic equilibrium flow distributions, and their behavioral adaptability was captured by multiple equilibrium behaviors over the stochastic network state set. The mixed equilibrium model was formulated as a fixed point problem defined in the mixed route flows, and its solution was achieved by executing an iterative algorithm. Numerical experiments were provided to verify the properties of the mixed network equilibrium model and the efficiency of the iterative algorithm.展开更多
To explore the influence of intelligent highways and advanced traveler information systems(ATIS)on path choice behavior,a day-to-day(DTD)traffic flow evolution model with information from intelligent highways and ATIS...To explore the influence of intelligent highways and advanced traveler information systems(ATIS)on path choice behavior,a day-to-day(DTD)traffic flow evolution model with information from intelligent highways and ATIS is proposed,whereby the network reliability and experiential learning theory are introduced into the decision process for the travelers’route choice.The intelligent highway serves all the travelers who drive on it,whereas ATIS serves vehicles equipped with information systems.Travelers who drive on intelligent highways or vehicles equipped with ATIS determine their trip routes based on real-time traffic information,whereas other travelers use both the road network conditions from the previous day and historical travel experience to choose a route.Both roadway capacity degradation and travel demand fluctuations are considered to demonstrate the uncertainties in the network.The theory of traffic network flow is developed to build a DTD model considering information from intelligent highway and ATIS.The fixed point theorem is adopted to investigate the equivalence,existence and stability of the proposed DTD model.Numerical examples illustrate that using a high confidence level and weight parameter for the traffic flow reduces the stability of the proposed model.The traffic flow reaches a steady state as travelers’routes shift with repetitive learning of road conditions.The proposed model can be used to formulate scientific traffic organization and diversion schemes during road expansion or reconstruction.展开更多
文摘为了准确预测描述出行路径决策行为,探究出行者感知及先进的出行者信息系统(advanced traveler information systems,ATIS)预测信息对决策行为的作用机制,将期望后悔理论与贝叶斯更新方法相结合,以出行时间为变量构建基于二次后悔更新的出行路径决策模型,继而提出后悔更新价值的概念,应用数值模拟方法分析在即时性及滞后性2种不同质量水平的ATIS预测信息影响下,对二次后悔更新过程及路径决策行为的影响。研究表明:二次后悔更新过程能够有效修正路径感知偏差及期望后悔水平;常规交通状态下,即时性信息比滞后性信息场景下的二次后悔更新水平高20%,偶发性交通状态下差距可达50%,即有效及时的ATIS预测信息对于保证后悔更新效果及决策准确性具有重要作用。
文摘为分析先进出行者信息系统(advanced traveler information system,ATIS)对交通均衡的影响,建立一个考虑多用户多准则的交通均衡模型.在模型中,根据ATIS市场占有率和用户收入水平差异将出行者分为4类,对每一类出行者,基于出行时间和出行费用线性组合的出行成本,根据Logit模型进行路径选择.以中国深圳市梅林关的一个起讫点(origin-destination,OD)进行实证分析,发现市民出行成本与路段拥堵系数不能同时降低.在现今经济水平下,ATIS可减少人们平均出行成本.随着经济水平的提升,人们才会慢慢利用ATIS选择可以减少平均拥堵系数的路径.最后,对模型参数进行了灵敏度分析.
文摘灾后先进出行者信息系统(advanced traveler information system,ATIS)能及时为使用者提供交通信息,而非ATIS使用者只能根据自身历史经验调整路径。为了探究两类用户混合情景下的路网韧性,在韧性评价中引入了动态时间维度,构建了两类用户混合日变动态配流模型,提出了基于历史流量的ATIS信息预测方法,并设计了模型求解算法。算例分析表明,所提出的ATIS信息预测方法与经典的ATIS信息预测方法相比,在研究时域内,前者支持下的平均路网韧性值相比后者提升了2.48%;针对路网韧性最优,最佳的ATIS市场占有率约为30%;当ATIS市场占有率为50%、75%、100%时,增大ATIS信息误差可提升路网韧性值,但当市场占有率为25%时,ATIS信息误差增大将导致路网韧性值降低。
基金Projects(51378119,51578150)supported by the National Natural Science Foundation of China
文摘Advanced traveler information systems （ATIS） can not only improve drivers＇ accessibility to the more accurate route travel time information, but also can improve drivers＇ adaptability to the stochastic network capacity degradations. In this paper, a mixed stochastic user equilibrium model was proposed to describe the interactive route choice behaviors between ATIS equipped and unequipped drivers on a degradable transport network. In the proposed model the information accessibility of equipped drivers was reflected by lower degree of uncertainty in their stochastic equilibrium flow distributions, and their behavioral adaptability was captured by multiple equilibrium behaviors over the stochastic network state set. The mixed equilibrium model was formulated as a fixed point problem defined in the mixed route flows, and its solution was achieved by executing an iterative algorithm. Numerical experiments were provided to verify the properties of the mixed network equilibrium model and the efficiency of the iterative algorithm.
基金Project(71801115)supported by the National Natural Science Foundation of ChinaProject(2021M691311)supported by the Postdoctoral Science Foundation of ChinaProject(111041000000180001210102)supported by the Central Public Interest Scientific Institution Basal Research Fund,China。
文摘To explore the influence of intelligent highways and advanced traveler information systems(ATIS)on path choice behavior,a day-to-day(DTD)traffic flow evolution model with information from intelligent highways and ATIS is proposed,whereby the network reliability and experiential learning theory are introduced into the decision process for the travelers’route choice.The intelligent highway serves all the travelers who drive on it,whereas ATIS serves vehicles equipped with information systems.Travelers who drive on intelligent highways or vehicles equipped with ATIS determine their trip routes based on real-time traffic information,whereas other travelers use both the road network conditions from the previous day and historical travel experience to choose a route.Both roadway capacity degradation and travel demand fluctuations are considered to demonstrate the uncertainties in the network.The theory of traffic network flow is developed to build a DTD model considering information from intelligent highway and ATIS.The fixed point theorem is adopted to investigate the equivalence,existence and stability of the proposed DTD model.Numerical examples illustrate that using a high confidence level and weight parameter for the traffic flow reduces the stability of the proposed model.The traffic flow reaches a steady state as travelers’routes shift with repetitive learning of road conditions.The proposed model can be used to formulate scientific traffic organization and diversion schemes during road expansion or reconstruction.
