摘要
针对随机路网中出行者规避风险的择路行为,提出一种同时考虑行程时间可靠性和不可靠性的次优拥挤收费双层规划模型。上层模型以最大化路网社会福利为目标,下层模型为弹性需求期望-超额交通平衡模型。鉴于双层规划模型的复杂性,设计遗传算法求解该模型。仿真结果表明,使用遗传算法求解该模型是可行的,运行50代后,算法可收敛至目标值。
In view of travelers’ risk aversive route choice behaviors under a stochastic road network,a second-best congestion pricing bi-level programming model considering both the reliability and the unreliability of travel time is proposed.In the upper level model,the optimization objective is to maximize the social welfare of the road network in the presence of congestion pricing,while the lower objective is an elastic demand mean-excess traffic equilibrium model.In consideration of the complexity of bi-level programming model,Genetic Algorithm（GA） is presented to solve the proposed model.Simulation results show that,it is feasible to solve the proposed model using GA,which can converge to the target value after 50 iterations.
出处
《计算机工程》
CAS
CSCD
2013年第8期257-261,共5页
Computer Engineering
基金
国家自然科学基金资助项目(50678153
51278429)
关键词
交通经济
次优拥挤收费
双层规划
遗传算法
随机路网
traffic economics
second-best congestion pricing
bi-level programming
GeneticAlgorithm（GA）
stochastic road network
