一种多属性条件下的交通网络路径选择模型

本文讨论了多属性条件下的城市交通网络的路径选择问题.当路径的客观信息已知时,出行者对所关注的属性给出具体的主观偏好值.由于出行者的主观偏好与客观信息之间存在一定的偏差,为了尽可能的减小这种偏差,在此建立了单目标优化模型,并采用解析的方法对模型进行了求解.在此基础上,给出了路径选择问题的求解步骤,其实现过程简单可操作.最后通过算例说明了主观偏好对路径选择结果的影响.     

基于博弈论混合策略下的路径选择模型

为了缓解交通拥挤的状况,预测出行者路径选择行为是一项值得研究的工作.在混合策略下,根据出行者之间的博弈关系,定义了混合策略下的得益函数,并引入求解混合策略纳什均衡的一种新方法,即极值法,对出行者的路径选择博弈模型进行了求解,有效弥补了传统划线法的不足,最终得到每个出行者的最优选择,实现了博弈各方的平衡.通过算例,有效地说明了混合策略下出行者最可能做出的路径选择.     

城市交通多类型随机用户出行决策模型研究

考虑城市交通中有多类出行者的一般情况,在ATIS等交通信息系统的影响下,不同类型的出行者对路径出行时间有不同理解,用不同的参数来反映.在此基础上,给出考虑路径选择、方式选择、讫点选择及是否出行的多类型随机用户出行决策模型,证明了模型的一阶条件满足路径选择、方式选择、讫点选择及是否出行的条件,最后给出模型算法.     

基于内生参考点的交通网络均衡模型

应用累积前景理论研究了随机交通网络中出行者有限理性的路径选择行为,选择期望 超额出行时间作为参考点,反映出行者同时考虑出行时间的可靠性和不可靠性,建立了基于累积前景理论的随机网络均衡及其等价的变分不等式模型,运用基于连续平均法的启发式算法求解,并给出算例验证了该模型和算法,最后分析了有限理性的假设和内生的参考点对出行者路径选择行为和随机网络均衡的影响．     

基于可靠性的随机拥挤收费模型

提出了一种需求不确定环境下的基于可靠性的拥挤收费模型.在需求不确定的环境下,出行时间的变动使得无论是出行者还是网络决策者都面临着风险.出行者需要提高出行的可靠性以避免迟到,决策者需要在缓解拥堵的同时提高网络整体的可靠性.以基于路段的平均超额出行时间作为出行者的路径选择准则,以平均超额系统总阻抗最小作为决策者拥挤收费的目标,建立了具有均衡约束的数学规划模型.提出了模型能分析出行者的路径选择行为和决策者的决策过程中的可靠性,同时充分考虑迟到等不可靠因素的影响,全面刻画不确定环境下路径出行时间和系统总阻抗的分布特征.数值算例表明,忽略不确定性的影响可能会得到对于网络整体性能过于乐观的评价,得到脱离实际的收费方案.网络的不确定性越高,对于网络性能和收费方案的影响就越大,从而考虑网络可靠性和出行者路径选择的可靠性的必要性就越大.此外,以平均超额系统总阻抗作为拥挤收费的目标可以进一步改善网络的可靠性.     

最优公交线路选择问题的数学模型及算法

公交线路选择问题是城市公共交通信息查询的重要内容，本文建立了满足不同公交线路查询者需求的最优线路选择模型并给出了相应的算法。首先通过引入各条公交线路直达最短距离矩阵构造了公交网络直达关系图（直达矩阵），在直达关系图（直达矩阵）上，利用修改了的最短路算法，即可求得最优换乘路线。根据出行者的不同需求，通过在直达关系图上定义不同的权系数，可以分别求得换乘次数最少的公交出行线路、经过站点最少的公交出行线路；通过修改最短路算法，可以求得出行耗时最少的线路及出行费用最低的线路，另外，本模型还可以综合考虑出行者的需求情况，求得出行者满意度最大的出行路线。     

多用户类多准则交通分配的势博弈与拥挤定价

交通管理者在解决路网拥挤问题时,并不知道出行者的出行效用,同时管理者难以对出行者的路径选择行为做出准确的观测.运用势博弈理论分析多用户类多准则交通行为的演化过程,得到了固定需求和弹性需求情形下的可容许动态(一种刻画出行者通过转换路径增加当前效用的近似调整行为的演化动态),证明当路段时间函数和逆需求函数为严格单调、连续、可微时,所对应的交通分配是势博弈问题的惟一Nash均衡点.进一步研究了固定需求下的可变拥挤道路收费问题,得到了在当前系统状态下实现系统最优交通分配的拥挤收费水平.     

基于后悔理论的拼车决策问题研究

本文针对瓶颈路段,研究了出行者在单独驾车和拼车这两种出行模式下的早高峰出行行为。首先根据瓶颈模型的均衡条件,分别推导了收费和不收费机制下单独出行和拼车出行的出行时间及广义出行成本。为进一步研究出行者的出行方式选择,本文建立了基于后悔理论的随机Logit模型,并提出了相应的迭代平均算法。研究表明,在不收费机制下单独驾车和拼车出行者混合出发,而最优收费机制则实现了交通流的分离,其中拼车出行者在瓶颈的高峰时段出行而单独驾车者在瓶颈的颈部出行。算例结果表明,后悔厌恶水平是影响出行者出行方式选择行为的重要参数,其中,选择最低成本的出行者随着后悔厌恶水平的增加而增加。     

出行选择行为的演化博弈分析

为缓解城市交通拥堵,通过分析出行者出行选择方式的博弈心理,建立了交管部门与出行者策略选择的决策博弈模型,应用演化博弈的思想分析了交管部门对私家车管理与不管理、出行者选择私家车与选择公交车出行各自两种策略选择的演化过程.根据雅克比矩阵的局部稳定性,对演化稳定状态进行了分析,并对一些主要参数对选择行为的影响进行了研究,结果表明,交管部门对公交车的激励效应以及对私家车管理效应对出行者出行方式选择行为的演化起着至关重要的作用.在讨论过程中,对各种情况做了详细地分析和解释,可以为政府决策部门提供相应的参考.     

考虑路网可靠性和空间公平性的次优拥挤收费模型

根据随机路网环境下出行者规避风险的路径选择行为,提出了一种考虑路网可靠性和空间公平性的次优拥挤收费双层规划模型。其中,上层模型以具有空间公平性约束条件下最大化路网的社会福利为目标,下层模型是实施拥挤收费条件下考虑行程时间可靠性的弹性需求用户平衡模型。鉴于双层规划模型的复杂性,设计了基于遗传算法和FrankWolfe算法的组合式算法来求解提出的模型。算例结果表明:考虑行程时间可靠性的次优拥挤收费会产生不同于传统次优拥挤收费的平衡流量分布模式,表明出行者的路径选择行为对拥挤收费结果会产生直接影响;此外,算例结果还说明遗传算法对参数设置具有很强的鲁棒性。     

Evaluation of choice set generation algorithms for route choice models

This paper discusses choice set generation and route choice model estimation for large-scale urban networks. Evaluating the effectiveness of Advanced Traveler Information Systems (ATIS) requires accurate models of how drivers choose routes based on their awareness of the roadway network and their perceptions of travel time. Many of the route choice models presented in the literature pay little attention to empirical estimation and validation procedures. In this paper, a route choice data set collected in Boston is described and the ability of several different route generation algorithms to produce paths similar to those observed in the survey is analyzed. The paper also presents estimation results of some route choice models recently developed using the data set collected.     

制定铁路枢纽编组站分工方案的分层优化方法

揭示了铁路枢纽编组站分工问题可以分解为车流在枢纽内作业地点的选择和走行径路的选择两个层次,根据问题的实际背景和内在机理,构造了作业地点选择和走行径路选择两个层次的数学优化模型,并自然展示了二者间的联系,针对所建模型为NP完全问题的特点,提出了利用遗传算法求解模型的主要策略,并进行了仿真计算。     

Handling uncertainty in route choice models: From probabilistic to possibilistic approaches

In order to design effective advanced traffic information systems (ATIS) suitable mathematical models have to be defined to simulate the effects of information on users route choice behaviour and then to incorporate it into traffic assignment models to estimate how traffic demand loads the roads network.To face this problem it is necessary to deal with uncertainty that plays a crucial role in the users decision-making processes.To this purpose this paper first analyses how uncertainty affects users’ route choice process and how traffic assignment models may take it into account.In literature route choice behaviour modelling is widely solved within the random utility theory framework but, we show in this paper that such an approach only considers one type of uncertainty. More precisely, the consideration of randomness of traffic by drivers is, for example, hardly ever represented in classical models in spite of its importance in the management of information by drivers.Starting from the presented analysis a new route choice model is also proposed to represent explicitly the uncertainty lying in users’ route choice behaviour. It is based on recent developments in possibility theory which is an alternate way to probability theory in order to represent or measure uncertainty.     

Doubly uncertain transportation network: Degradable capacity and stochastic demand

This study developed a methodology to model doubly uncertain transportation network with stochastic link capacity degradation and stochastic demand. We consider that the total travel demand comprises of two parts, infrequent travelers and commuters. The traffic volume of infrequent travelers is stochastic, which adds to the network traffic in a random manner based on fixed route choice proportions. On the other hand, the traffic volume of commuters is stable or deterministic. Commuters acquire the network travel time variability from past experiences, factor them into their route choice considerations, and settle into a long-term habitual route choice equilibrium in which they have no incentive of switching away. To define this equilibrium, we introduce the notion of “travel time budget” to relate commuters’ risk aversion on route choices in the presence of travel time variability. The travel time budget varies among commuters according to their degrees of risk aversion and requirements on punctual arrivals. We then developed a mixed-equilibrium formulation to capture these stochastic considerations and illustrated its properties through some numerical studies.     

Dynamic capacitated user-optimal departure time/route choice problem with time-window

A discrete-time predictive dynamic user-optimal departure time/route choice problem has been formulated and solved with the nested diagonalization method by Chen, H.K., Hsueh, C.F. (1998a. A discrete-time dynamic user-optimal departure time/route choice model. Transportation Engineering, ASCE 124 (3), 246–254). That model did not impose constraints on either departure times at origin or arrival times at destination, which are typically required for regular work trips. However, for better representing practical situations, the time-window constraints associated with both the departure times and arrival times, along with link capacity side constraints, need to be incorporated into the aforementioned model. The resulting dynamic capacitated user-optimal departure time/route choice model with time-window can appropriately model the queuing delay associated with each capacitated time–space link and also can properly differentiate off-peak and peak phenomena within the analysis period. A numerical example is provided for demonstration.     

Integrated fleet mix and routing decision for hazmat transportation: A developing country perspective

In developing countries, truck purchase cost is the dominant criteria for fleet acquisition-related decisions. However, we contend that other cost factors such as loss due to the number of en route truck stoppages based on a truck type and recovery cost associated with a route choice decision, should also be considered for deciding the fleet mix and minimizing the overall costs for long-haul shipments. The resulting non-linear model, with integer variables for the number and type of trucks, and the route choices, is solved via genetic algorithm. Using real data from a bulk liquid hazmat transporter, the trade-offs between the cost of travel, loss due to number of truck stoppages, and the long-term recovery cost associated with the risk of exposure due to a hazmat carrier accident are discussed. The numerical experiments show that when factors related to public safety and truck stoppages are taken into account for transportation, the lowest total cost and optimal route choice do not align with the cheapest truck type option; rather, the optimal solution corresponds to another truck type and route with total costs significantly less than the total costs associated with the cheapest truck type. Our model challenges the current truck purchasing strategy adopted in developing countries using the cheapest truck criteria.