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。
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.
