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.     

城市土地利用对交通需求特性影响研究

针对长期以来关于城市土地利用对交通需求特性影响研究中存在的争论,系统分析了土地利用、社会经济属性和交通需求三者之间的关系。建立多元线性回归模型,分别考察人口密度、土地混合程度、家庭收入和家庭大小对于交通出行次数、交通方式、出行距离和出行时长的影响。通过对回归模型显著性水平的检验,找出真正具有内在联系的因素。最后阐述了土地利用与交通相关研究的意义,指出未来研究方向。     

基于Fuzzy回归的快速路行程时间预测模型研究

行程时间预测是交通流诱导系统和交通控制系统研究的一项重要内容。在分析各种行程时间预测方法的基础上,本文提出了基于Fuzzy回归的快速路行程时间预测模型,利用深圳市的交通实测数据对行程时间进行了预测分析。     

路网可靠度模型的研究与拓展

回顾了路网可靠度模型的研究进展：包括不同的可靠度指标、模型以及算法。在分析传统可靠度模型优缺点的基础上，提出将随机网络平衡模型用于路网可靠度分析。     

基于 BP神经网络的个体出行模式识别研究

以个体出行模式作为研究对象,利用出行的 GPS 数据,建立用于个体出行模式识别的 BP神经网络计算模型。并实测了公交和出租2种个体出行模式的识别效果,结果验证了建立的 BP神经网络的有效性和实用性,为出行模式的选择预测提供了新的思路。     

基于活动链的出行需求预测方法综述

基于活动链出行预测理论主要研究人们对社会经济活动(以下简称活动)和出行行为的决策特性。介绍此理论的发展过程,给出6个重要的模型系统,并简要评价各个模型的优缺点。然后简单介绍1994年由Ben Akiva提出的基于活动链出行需求模型,进一步说明系统的框架和预测过程。通过对基于活动链理论发展过程和实际模型应用效果的分析,给出目前模型系统存在的缺点和问题,探索改进出行需求预测方法的途径。     

突发事件下路网运行时间可靠性研究

对突发事件下的路网运行时间可靠性进行分析,从路段通行能力与运行时间关系的角度,对交通流量与运行时间关系进行讨论。在研究运行时间的均值、标准差等特征值在突发事件下变化的基础上,利用机会约束模型,建立异常状态下路网容量分析的非线性规划模型。实例分析结果表明,突发事件出现时路网运行时间可靠性与其承担的交通流量有直接的关系。     

道路时间费用预测模型研究

时间费用是公路建设和公路养护投资效益分析所必须考虑的经济指标之一。旅行时间费用、车辆运营费用和道路事故费用三者一起构成了道路的用户费用。随着公路网水平的提高、社会的发展和人们生活水平的不断提高，时间费用将在用户费用中占有更大的比重。本文就时间和由此产生的费用及费用计算方法进行初步探讨。     

新时代下城乡居民出行方式选择模型

选择合理、恰当的运输方式,是实现最佳经济效益和社会效益的前提。文章基于非集计理论,建立了新时代下城乡居民出行方式选择模型,并对模型进行了分析。该模型对城乡交通管理及城乡公路的运输规划和政策制定具有基础性的作用。     

基于行程时间估计的快速路检测器布设密度优化方法研究

随着智能交通运输系统对道路实时交通信息的迫切需求,布设高密度、全方位的交通检测器已成为共识,但是高密度的检测器布设带来的是硬件投资的急剧增加,因此有必要进行检测器布设密度的优化。利用快速路行程时间估计方法分析了检测器布设密度优化问题,认为从行程时间估计误差和合理投资的角度出发,检测器布设并不是密度越大效果越好,而是存在一个合理范围,该范围可以指导工程中实际检测器数目的选择。     

公共交通系统营运可靠性研究

优先发展公共交通是大城市解决交通拥堵,实现城市交通可持续发展的一项重要措施,然而,公交营运水平的低下制约着公交的发展。本文借鉴可靠性理论对公共交通营运可靠性进行定义,并对公交营运时间和乘客服务可靠性分别进行了描述,据此建立起公交系统营运可靠性模型,然后采用随机模拟技术(即Monte Carlo模拟)进行求解,通过算例说明模型的可行性,最后通过分析可靠性模型得出大型活动期间改善公交营运的途径。     

城市信号控制交叉口仿真研究与优化设计

文章分析了西安市翠华路-育才路道路交叉口拥堵的原因,研究了从改善信号配时方案角度对交叉口进行优化的可行性,并相应地进行仿真评估,以出行时间、平均排队长度为指标对现状配时方案比较,说明优化效果。     

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.     

Probabilistic Modeling of Erroneous Human Response to In-Vehicle Route Guidance Systems: A First Look

An integral component of (in-vehicle) navigation systems is the determination of optimal routes to the desired destination. An implicit assumption in the underlying algorithms is that people do not make mistakes when following the prescribed routes. This is, however, not always consistent with reality, especially when driving in unfamiliar environments. This article presents a first look at the possibility of mistakes when driving. This possibility is formalized in a Markov decision process. It is demonstrated that quite paradoxical situations can occur when accounting for mistakes. As the most interesting—but perhaps extreme—example, we have shown that under certain conditions, it is no longer optimal to recommend drivers to take the shortest route. Instead, a longer route (in certain cases even the longest!) becomes optimal. Numerical results are provided throughout the article to reveal the fundamental properties of this problem.