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
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。
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.
