基于计划延误成本的出发时间选择模型

出行时间不确定性对出行者造成诸多负面影响,其中之一就是计划延误成本增加.向出行者提供交通信息可减少出行时间不确定性的负面影响,减少计划延误成本.为了估计其负面效应和出行时间信息的价值,研究了一个出行时间不确定性下的出发时间选择模型.假定在不同的出发时间下,出行时间分布的形式不同.模型中出行者可得到交通信息服务系统(A T IS)提供的基于平均出行状况的交通信息或基于当日出行状况的交通信息,同时他对得到的信息有自己的感知值,基于此感知预测出行时间并选择出发时间.用算例反映了出行时间不确定性、不同信息状况和信息预测的质量对出行者广义出行阻抗的影响.结果表明交通信息的收益不应该只表现为传统的出行时间节省,它还能减少约30% ~40% 的计划延误成本.      

考虑风险规避的动态路径选择模型研究

为了研究在不确定性交通环境下路径选择问题,引入前景理论建立动态路径选择模型以规避出行风险,保障出行者准时稳定地到达目的地。首先介绍了前景理论,计算了以行程时间为指标路径前景值和在给定的预算时间内确定路径准时到达可靠度。考虑到绝大多数出行者属于风险规避型,定义可靠度最高的路径为基准路径,将二阶随机占优于基准路径的路径归入有效集。最后定义了延误风险,以有效集中延误风险最低的路径作为可接受的最优路径。通过一个简单测试网络算例,研究了考虑风险规避的动态路径选择,并分析了对路径选择的影响。研究结果分析显示,当考虑风险规避时,出行者不再以单一的期望行程时间为最优目标,而会综合考虑出行选择。     

私家车出行者交通信息需求研究

交通出行信息对私家车出行者行为选择存在不同程度的影响。通过对大连市私家车出行者交通出行信息使用意向调查,获取私家车出行者交通出行信息选择行为特性数据,引入因子分析方法对交通出行信息内容进行合理分类,建立交通出行信息选择累积Logistic回归模型,对私家车出行者交通出行信息需求进行研究。研究结果表明私家车出行者对交通出行信息内容存在不同程度的需求,其中与个人私家车出行质量相关信息的需求最高,面向私家车出行者的城市交通信息服务系统建设与运营应体现针对性。     

动态路阻下的出行时间预测和路径选择模型

通过分析原有路径选择模型的不足之处,在考虑路网中路段路阻的动态性和出行者出行路径选择的随机性的基础上,提出了一种出行期望时间预测和动态路径选择模型,并引用一个具体算例来证明该模型的正确性和适用性;同时分别运用最短路径模型和文中所提出的动态路径选择模型进行求解,得到每条路径被选择的概率和最短期望出行时间,结果证明文中模型能计算出出行者期望时间和可能出行路径,比传统模型更加符合现实状况,更能模拟现实生活中出行者的出行行为和路网路阻随时间变化的状态,对出行者的出行决策更具指导价值。     

基于累积前景理论的拥挤阈值计算模型

信息诱导是缓解交通拥挤的有效途径,为了描述道路拥挤程度对出行者路径选择决策的影响机理,基于累积前景理论分析了出行者的出行决策过程,分析了出行者拥挤认知模式以及不同出行方式的拥挤信息需求。解析了拥挤阈值的概念,将行程时间作为累积前景理论决策指标建立了拥挤阈值的计算模型,以1个简单路网进行算例分析,模拟驾驶员的拥挤认知及出行活动决策。算例结果揭示了拥挤阈值对路径选择决策行为的影响,同时验证了拥挤阈值是出行者在决策过程中的决策变化分界点。出行时间在拥挤阈值内出行者不改变出行路径;出行时间超过拥挤阈值,出行者将改变出行路径。      

拥挤收费下基于前景理论的出行者路径选择行为分析

出行者（特指私家车出行者）路径选择行为的研究对于城市的交通管理和交通组织都有着重要的意义,而且随着交通出行费用的不断增加,以及道路拥挤收费政策逐步实施,仅仅考虑距离最短、时间最短来对出行者路径选择行为模型进行研究,往往实用性欠佳。针对以往的研究中考虑因素单一、模型实用性不强这一问题,运用能有效描述出行者不确定性条件下决策行为的前景理论,综合考虑出行者的出行时间、出行费用以及出行者的个人偏好、出行经验等因素,建立一个更为贴合实际的出行者路径选择行为模型。以实际算例的形式对比分析了,不同收费标准对不同出行者人群、不同性质出行的路径选择行为的影响程度。结果表明：与拥挤收费政策实施前相比,收费3元时,高收入水平出行者购物出行时的路径发生变化,而对于通勤出行,当收费大于6元时出行路径才发生变化;对于中低收入水平出行者来说,因为其较高收入者对费用更为敏感,所以拥挤收费政策实施后他们的反应也不尽相同。     

基于前景理论的出发时刻选择模型研究

介绍了研究出行者出行选择问题的传统理论——“期望效用理论”的不足,分析了“前景理论”的基本思想和结构框架。基于前景理论,以实时交通信息影响下的日常上班出行为研究对象,具有重要的理论意义和实践价值。选择到达目的地的时刻作为参照点,确定了价值函数。通过对可供出行者选择的时间范围进行分段,研究了在不同时段出发,出行者如何预测路径的行程时间。出行者将选择可获得最大价值的时刻作为出发时刻,以此为目标建立了出发时刻的选择模型。通过设定到达目的地时刻的满意区间,研究了出行者下次出行是否改变和将如何改变出发时刻。     

信息影响条件下考虑出行异质性的路径选择模型

出行者路径选择行为的研究对于预测城市交通流时空分布规律及维持网络经济、高效运转具有重要意义.为了探究多类型异质出行者路径选择行为的异同,研究了基于累积前景理论的多类别异质出行者路径选择模型.通过定义出行者参考点,引入考虑时间价值系数的风险敏感系数计算方法,以保证异质出行者拥有异质的风险偏好,研究分析了是否提供实时路况信息2种情境下常规通行者与通勤者路径选择行为的异同.数值算例表明,拥有较大参考点的出行者更倾向于保守地选择一条出行时间较长的道路,而其他人则更加冒险.此外,与通勤者相比,当备选方案均可以满足自身出行需求且自身参考点足够大时,常规出行者更愿意忽视风险,以获得更高的出行收益.      

基于广义出行费用的城市路网行程时间可靠性

为体现出行者路径选择行为,准确评价路网行程时间可靠性,采用行程时间均值和行程时间均方差的加权和定义广义出行费用,以不同的加权系数反映出行者对待风险的态度。从路段层面定义行程时间可靠性,以广义出行费用最小化为目标构建SUE模型,建立基于广义出行费用的路网行程时间可靠性模型。结合模型特点,给出Monte Carlo仿真和交通规划模型相结合的求解算法。并在一个小型测试路网上进行了验证,结果表明:出行者对待风险的不同态度对行程时间可靠性具有明显的影响,保守型出行者将使路网行程时间可靠性有所提高;所建分配模型能够较真实地反映出行者的路径选择行为,使行程时间可靠性分析更加准确。     

交通出行方式决策中的锚定效应影响研究

针对出行者感知效用与实际效用的差异性问题,分析出行方式决策的关键影响因素,并通过实验研究出行经验与关键因素信息对出行者出行方式决策的影响。结果表明,时间和安全是出行方式决策的关键影响因素;出行者的出行决策受个体经验与出行信息共同影响;提供的出行信息对出行方式决策的影响显著,符合锚定效应的作用机理,且该锚定效应具有一定感知递减性。     

交通信息影响下的动态路径选择模型研究

考虑交通信息对出行者选择出行路径的动态影响,建立一种动态路径选择模型。将不同类型的出行者对路段（路径）运行时间的预测看作不同的随机过程,通过对出行路径上节点的到达时间取期望值,利用一阶近似表达式,研究交通信息对出行者的出行路径选择行为的影响。     

Experimental Study of Effects of Travel Time Distribution Information on Dynamic Route Choice Behavior

This study focuses on the possibility that providing travelers with information on travel time distribution, along with the existing travel time information, might affect their decision making and enhance traffic control. As an initial step to confirm the effectiveness of travel time distribution information, we use a laboratory experiment to obtain panel data of route choices when information is provided. The rationality of the behavior of the respondents and the characteristics of the route choice are first analyzed by aggregation analysis and then statistically examined by specifying a mixed logit route choice model. As a result, it is revealed that both travel time information and maximum travel time information significantly affect route choice behavior when a penalty is imposed for late arrival.     

基于出行时间预算的多用户交通分配模型及算法

通过分析在满足一定可靠性要求条件下出行者追求总预期出行时间最小的择路行为,定义交通网络的惯常均衡态。以不同的出行者会增加相异的出行时间预算为假设前提,建立相应的多用户变分不等式交通分配模型。这一模型不仅具有不依赖具体路阻函数形式的一般化建模特征,而且避免了现有文献中必须处理非凸约束集的问题,因此可以对模型的解进行定性分析。针对该模型,提出相应的求解算法。由于求解算法仅需对现有的网络交通分配算法加以局部修改即可实现,因此可被应用于实际规模的网络分析。通过算例结果的比较分析说明新模型及算法不仅有效,而且更符合现实。     

Modeling the Effects of Population Density on Prospect Theory-Based Travel Mode-Choice Equilibrium

In conventional transportation planning models, it was always assumed that the population density is given and fixed in the study areas. Therefore, the effects of population density on travel choice have not been explicitly incorporated into these existing models for long-term transportation planning. Meanwhile, travel choice models in previous studies are usually developed by using discrete choice theories or user equilibrium principle. Thus, many significant characteristics of travelers’ behaviors, such as risk preference and learning process over time, cannot be considered in these conventional models. This article proposes a convex prospect theory-based model to investigate the effects of population density on the travelers’ mode-choice behavior under an advanced transportation information system (ATIS) in a multimodal transportation corridor. It is shown that population density is closely co-related to the modal split results and dependent on the performance of the railway mode in the study corridor. The park-and-ride mode may not be suitable for areas with high population density. This article also investigates the travelers’ reference points on the generalized travel costs by modes. A numerical example is given to illustrate the properties of the proposed model together with some insightful findings.     

基于OBD数据的驾驶人出行模式挖掘

传统的出行模式研究通常依靠问卷调查分析驾驶人出行特征,所得结果易受调查数据主观性影响,针对此问题基于北京市域范围内2个月共计3570辆私家车的车载诊断数据,对驾驶人的不同出行模式进行分析并建模.通过长期采集的车辆各项参数,采用基于密度峰值的聚类算法进行聚类,将不同的驾驶人分为高频出行者、通勤出行者、长距偶发出行者以及危...     

Providing personalized system optimum traveler information in a congested traffic network with mixed users

The advancement of information and communication technology allows the use of more sophisticated information provision strategies for real-time traffic management in a congested network. This article proposes a personalized system optimum traveler information (PSOI) system under ubiquitous communication, which allows traffic system operators to fully optimize and coordinate individuals' trip plans according to the personal attributes, such as real-time location, value of time, allowable budgets for congestion tolling, and willingness to take detours. We also developed an efficient queue-based evaluation and solution heuristic algorithm using mesoscopic simulation models to solve for near-optimal PSOI strategies—route suggestions for each individual traveler. The simulation optimization algorithm can account for different information users and provide predictive information that robustly accounts for potential decisions of other travelers in real time. Case studies were carried out on a test network and a real-world network, and the proposed heuristic algorithm is proven effective. Also, sensitivity analyses show that PSOI not only is an effective traffic management method in reducing average system travel time, but also potentially provides travelers with reasonable or even shorter travel times compared with other information users. Further, simulation results showed that even in mixed traffic, PSOI is able to shorten travel times for both users without information and users of other information types. Thus, PSOI is recommended by this article as an advantageous way for next-generation advanced information systems and dynamic traffic management.     

Development of a behaviorally induced system optimal travel demand management system

The basic design concept of most advanced traveler information systems (ATIS) is to present generic information to travelers, leaving travelers to react to the information in their own way. This “passive” way of managing traffic by providing generic traffic information makes it difficult to predict the outcome and may even incur an adverse effect, such as overreaction (also referred to as the herding effect). Active traffic and demand management (ATDM) is another approach that has received continual attention from both academic research and real-world practice, aiming to effectively influence people's travel demand, provide more travel options, coordinate between travelers, and reduce the need for travel. The research discussed in this article deals with how to provide users with a travel option that aims to minimize the marginal system impact that results from this routing. The goal of this research is to take better advantage of the available real-time traffic information provided by ATIS, to further improve the system level traffic condition from User Equilibrium (UE), or a real-world traffic system that is worse than UE, toward System Optimal (SO), and avoid passively managing traffic. A behaviorally induced, system optimal travel demand management model is presented to achieve this goal through incremental routing. Both analytical derivation and numerical analysis have been conducted on Tucson network in Arizona, as well as on the Capital Area Metropolitan Planning Organization (CAMPO) network in Austin, TX. The outcomes of both studies show that our proposed modeling framework is promising for improving network traffic conditions toward SO, and results in substantial economic savings.