Bi-objective routing problem with asymmetrical travel time distributions

Recent studies have confirmed that travelers consider travel time reliability in addition to average travel time when making route choice decisions. In this study, we develop a bi-objective routing model that seeks to simultaneously optimize the average travel time and travel time reliability. The semi-standard deviation (SSD) is chosen as the reliability measure because it reflects travelers' concerns over longer travel time better than the commonly used standard deviation. The Pareto-optimal solutions to the bi-objective model are found by using an improved strength Pareto evolutionary algorithm. Tests on a real-world urban network with field measured travel time data have demonstrated good performance of the algorithm in the aspects, such as computational efficiency, quick convergence, and closeness to the global Pareto-optimal. Overall, the bi-objective routing model generates reasonable path recommendations. The SSD-based model is sensitive to the asymmetry of travel time distribution and tends to avoid paths with excessively long delays. This would be particularly helpful to those users placing high values on travel time reliability.     

Bus travel time modelling using GPS probe and smart card data: A probabilistic approach considering link travel time and station dwell time

Abstract

Path travel time estimation for buses is critical to public transit operation and passenger information system. State-of-the-art methods for estimating path travel time are usually focused on single vehicle with a limited number of road segments, thereby neglecting the interaction among multiple buses, boarding behavior, and traffic flow. This study models path travel time for buses considering link travel time and station dwell time. First, we fit link travel time to shifted lognormal distributions as in previous studies. Then, we propose a probabilistic model to capture interactions among buses in the bus bay as a first-in-first-out queue, with every bus sharing the same set of behaviors: queuing to enter the bus bay, loading/unloading passengers, and merging into traffic flow on the main road. Finally, path travel time distribution is estimated by statistically summarizing link travel time distributions and station dwell time distributions. The path travel time of a bus line in Hangzhou is analyzed to validate the effectiveness of the proposed model. Results show that the model-based estimated path travel time distribution resembles the observed distribution well. Based on the calculation of path travel time, link travel time reliability is identified as the main factor affecting path travel time reliability.     

Travel time statistical modeling with the Halphen distribution family

ABSTRACT

This article introduces the Halphen distribution family for modeling travel time distributions and their reliability on single road links. This probability family originally used in hydrology has a set of relevant characteristics. It is composed of three probability distribution functions for which the mathematical properties are described here. The article uses a graphical representation, the δ-Moment Ratio Diagram (δ-MRD). This tool allows characterizing travel time reliability as well as selecting best distribution candidates within the fitting processes, by considering empirical data sets. A systematic methodology is developed to take advantage of both aspects. From maximum log-likelihood estimation, it is shown that Halphen distributions are among the best state-of-the-art solutions for the travel time modeling purpose. This global framework is validated using two empirical data sets: an urban data set gathered in Portland, Oregon (USA), and a periurban data set from Lyon (France). The model calibration is eased through the use of the δ-MRD; this property opens new research directions about the mapping between traffic states and statistical modeling. It comes from all these considerations that the Halphen family is suitable to describe accurately the travel time dynamics on single links. Therefore, it could be part of a decision support system for practitioners interested in travel time variability.     

基于行程质量的随机用户平衡分配模型

提出行程质量的概念以描述出行者在不确定环境下的路径选择准则。将行程质量定义为行程时间和行程时间可靠性的线性加权和,综合了影响路径选择的两个不同的重要因素:行程时间和行程时间可靠性。假定在路段通行能力随机变化的情况下出行者以估计行程质量费用最小作为路径选择的标准,建立了基于行程质量的随机用户平衡分配模型。证明了模型解的等价性和唯一性,给出了求解模型的MSA算法。在一个小型测试网络上的计算结果表明:模型能够反映出行者在随机路网中的路径选择行为。     

Robust traffic assignment model: Formulation,solution algorithms and empirical application

ABSTRACT

The deterministic traffic assignment problem based on Wardrop's first criterion of traffic network utilization has been widely studied in the literature. However, the assumption of deterministic travel times in these models is restrictive, given the large degree of uncertainty prevalent in urban transportation networks. In this context, this paper proposes a robust traffic assignment model that generalizes Wardrop's principle of traffic network equilibrium to networks with stochastic and correlated link travel times and incorporates the aversion of commuters to unreliable routes.

The user response to travel time uncertainty is modeled using the robust cost (RC) measure (defined as a weighted combination of the mean and standard deviation of path travel time) and the corresponding robust user equilibrium (UE) conditions are defined. The robust traffic assignment problem (RTAP) is subsequently formulated as a Variational Inequality problem. To solve the RTAP, a Gradient Projection algorithm is proposed, which involves solving a series of minimum RC path sub-problems that are theoretically and practically harder than deterministic shortest path problems. In addition, an origin-based heuristic is proposed to enhance computational performance on large networks. Numerical experiments examine the computational performance and convergence characteristics of the exact algorithm and establish the accuracy and efficiency of the origin-based heuristic on various real-world networks. Finally, the proposed RTA model is applied to the Chennai road network using empirical data, and its benefits as a normative benchmark are quantified through comparisons against the standard UE and System Optimum (SO) models.     

供需不确定条件下的预算-超额用户平衡模型

为更加全面、准确反映随机路网中出行者规避风险的择路行为,以预算-超额行程时间作为出行者选择路径的依据,提出了一种供应及需求不确定条件下同时考虑可靠性和不可靠性的交通分配模型——预算-超额用户平衡模型,推导了需求服从Gamma分布、路段通行能力服从均匀分布条件下预算-超额行程时间的解析表达式,并以此为基础建立起用等价变分不等式表示的平衡模型。利用一个小型测试网络比较了用户平衡模型、基于可靠性的用户平衡模型以及预算-超额用户平衡模型的性能。研究结果表明：提出的模型是有效、可行的;其平衡流量模式不同于用户平衡模型和基于可靠性的用户平衡模型;随着需求水平、可靠度以及路段通行能力退化程度的增加,预算-超额行程时间随之增加。     

基于蒙特卡罗模拟方法的快速路运行时间可靠度研究

运行时间可靠度作为一个非常重要的概率测度参数能有效地评价交通网络的动态特性。在对运行时间可靠度的概念界定的基础上，分析了快速路运行时间可靠度的影响因素。提出了运用蒙特卡罗模拟方法计算运行时间可靠度，即采用蒙特卡罗模拟方法随机的对快速路入口的交通需求变量进行抽样，根据得到的样本值确定路径出行时间，然后对此出行时间进行检查，如果超过了规定的阚值，则认为不可靠，否则可靠。并通过一个算例对该模型进行了验证。最后指出了运行时间可靠度这一概率参数的应用前景。     

Analyzing travel time reliability and its influential factors of emergency vehicles with generalized extreme value theory

Travel time reliability is very critical for emergency vehicle (EV) service and operation. The travel time characteristics of EVs are quite different from those of ordinary vehicles (OVs). Although EVs own highest road privilege, they may still experience unexpected delay that results in massive loss to the society. In this study, we employ the generalized extreme value (GEV) theory to measure extremely prolonged travel time and analyze the potential influential factors. First, among three GEV distributions, Weibull distributions are found to be the best distribution model according to several goodness-of-fit tests; a new reliability index is derived to measure travel time reliability. Numerical examples demonstrate the advantages of GEV-based reliability index over variance and percentile value in the applications of EV. This index will be of great practicability in the EV operation performance and reliable route choices. Second, we further investigate the potentially influential factors of EV travel time reliability. Results show that link length and left-turn traffic volume may have an adverse impact on the link reliability while more left-turn lanes may increase the travel time reliability. The influential factor study will help us understand the causes of the EV travel time delay and the differences of travel time reliability between OVs and EVs.     

Freeway path travel time prediction based on heterogeneous traffic data through nonparametric model

Providing reliable travel time prediction is very much needed for commuters for their upcoming trips to reduce travel time and relieve traffic congestion. This article proposes an integrated model for path and multi-step-ahead travel time prediction on freeways using both historical and real-time heterogeneous traffic and weather data. The model's performance is investigated in a case study under various traffic scenarios. Results indicate that the proposed model provides satisfactory prediction results in various performance tests. For practical purposes, general guidelines for selecting the model's parameter sets as well as the efficient size of historical data are also presented.     

基于Vissim仿真的公交行程时间可靠性研究

从公交行程时间可靠性入手,深入分析了公交行程时间可靠性的定义、计算方法及评价指标体系;通过对公交车辆行程时间可靠性特征分析,建立了考虑公交专用道情况下的车辆行程时间可靠性概念模型,并运用仿真软件Vissim对不同影响参数下公交专用道条件下的车辆行程时间概念模型进行了验证,对仿真结果进行了对比分析,其结果可以对公交优先设计工作提供参考.      

运行时间可靠度在单向交通组织中的应用

运用网络可靠性计算的串并联理论,提出了道路网络中节点OD（Origin-Destination）对之间的路径运行时间可靠度计算方法,建立了基于节点OD需求的路网运行时间可靠度计算模型。在节点OD需求已知的情况下,根据交通组织状况,采用动态交通分配和交通仿真方法获得模型参数,计算节点OD对之间分别在双向和单向交通组织条件下的运行时间可靠度,并建立交通组织方案的临界判别条件,作为单向交通组织方案评价和决策的量化指标。     

基于L-偏度和L-峰度的行程时间可变性表征方法

为了定量化测度行程时间可变性右偏且长尾的实证特征，考虑实际观测中可能存在的数据样本量不足和离群值干扰问题，提出基于线性矩的L-偏度和L-峰度用于精确表征行程时间可变性。考虑到线性矩是顺序统计量期望的线性组合，给出了避免遍历所有子样本的线性矩估计方法。根据线性矩概念，探究了L-偏度和L-峰度的数学含义和其表征行程时间可变性的有效性，以及样本L-偏度和L-峰度的计算方法。理论研究发现，在表征范围和样本估计质量方面，相较于传统偏度和峰度，L-偏度和L-峰度对行程时间可变性具有更加优越的表征能力。采用深圳市车牌照识别系统的行程时间数据集进行案例分析，从无偏性、鲁棒性和有效性3个维度证明了L-偏度和L-峰度相较于传统偏度和峰度的优越性。分析结果如下：样本L-偏度和L-峰度在样本量不足时仍然是总体近似的无偏估计，而传统偏度和峰度的系统性误差较大；L-偏度和L-峰度对离群值具有鲁棒性，而传统偏度和峰度对离群值过于敏感；样本L-偏度和L-峰度对总体的估计波动小且精度高，具有良好的估计有效性；L-偏度和L-峰度分别与传统偏度和峰度有较高相关性，但又能够辨识出不同时空下行程时间可变性分布的差异。基于L-偏度和L-峰度所表征的行程时间可变性信息，出行者、规划者和管理者能够更加精准地认知不确定性的路网运行状态，从而做出更合理的行为选择和优化决策。     

基于安全可靠性的多类用户交通分配模型

为探究出行安全对用户出行选择行为的影响,提出了考虑事故风险成本和旅行时间的多类用户交通分配模型。针对事故发生的随机性特征,定义了路径出行安全可靠性概念,并以此计算用户的事故风险成本预算,体现出行者的安全偏好。基于考虑事故机会和事故风险的基础事故预测模型,针对路段和交叉口的不同特征,分别定义了路段和交叉口的事故风险成本分布。构建的交叉口事故风险成本模型,体现了交叉口不同转向的事故风险成本的差异性。为了求解基于安全可靠性的多类用户交通分配模型,采用路径配流法和相继平均法设计了相应的求解算法,并通过算例分析了模型和算法的有效性。研究结果表明：安全可靠性在用户出行选择中具有重要影响。当在广义出行费用中考虑事故风险成本时,出行者会更多地选择事故风险成本较小的路径;不同风险倾向的用户会有不同的选择特征,保守型出行者倾向于选择路径事故风险成本标准差相对较小的路径,即事故风险成本波动小的路径,而中立型出行者倾向于选择事故风险成本均值相对较低的路径;考虑交叉口的事故风险成本与否会直接影响流量分配结果,即路径交叉口数量和转向的差异性同样会影响出行者的选择。所提出的模型对于客流预测和网络安全评价与管理具有潜在的应用价值。     

支持向量机在路段行程时间预测中的应用研究

主要探讨支持向量机理论在路段行程时间预测中的应用。具体的方法是,首先将研究路段根据路段交通状态和车辆检测器设置情况进行分段,然后以前几个时段的各个小路段的交通流量、平均速度和车道占有率和整个路段的行程时间为输入,以下一时段的整个路段的行程时间为输出,选取高斯径向基函数作为核函数,建立了基于支持向量机的路段行程时间预测模型,从而探讨支持向量机在路段行程时间预测中的应用效果。最后,利用交通仿真软件的模拟数据进行验证,并与BP神经网络计算结果比较,计算结果的对比表明本文提出的方法预测效果更好。     

基于数据融合的路段行程时间估计

基于检测器数据的路段行程时间估计通常具有精度不高和可靠性差的特点。论文引入了自适应式卡尔曼滤波,采用K近邻法寻找相似的交通流状态来标定状态转移系数,建立了基于固定型检测器数据和移动型检测器数据的路段行程时间估计融合模型。实际数据的验证结果是,平均相对误差为9.52%,相对误差的标准差为8.92%。研究表明,与基于移动检测器数据的估计方法相比较,该方法极大地改善了估计精度和可靠性,还具有收敛速度快、对初值不敏感、参数少等特点。     

A novel arterial travel time distribution estimation model and its application to energy/emissions estimation

Arterial travel time information is crucial to advanced traffic management systems and advanced traveler information systems. An effective way to represent this information is the estimation of travel time distribution. In this paper, we develop a modified Gaussian mixture model in order to estimate link travel time distributions along arterial with signalized intersections. The proposed model is applicable to traffic data from either fixed-location sensors or mobile sensors. The model performance is validated using real-world traffic data (more than 1,400 vehicles) collected by the wireless magnetic sensors and digital image recognition in the field. The proposed model shows high potential (i.e., the correction rate are above 0.9) to satisfactorily estimate travel time statistics and classify vehicle stop versus non-stop movements. In addition, the resultant movement classification application can significantly improve the estimation of traffic-related energy and emissions along arterial.     

Reliability-based network flow estimation with day-to-day variation: A model validation on real large-scale urban networks

Day-to-day variation in the travel times of congested urban transportation networks is a frustrating phenomenon to the users of these networks. These users look pessimistically at the path travel times, and learn to spend additional time to safeguard against serious penalties that await late arrivals at the destinations. These additional expenses are charges similar to the tolls in system equilibrium flow problem, but may not be collected. With this conjecture, the user equilibrium (UE) formulation of congested network flow problem would lack some necessary factors in addressing appropriate path choices. This study, following a previous work proposing pessimistic UE (PUE) flow, aims to show how to measure this additional travel cost for a link, and investigates how different is PUE from UE, and when such differences are pronounced. Data are collected from the peak-hour travel times for the links of paths in the city of Tehran, to estimate the variance of travel times for typical links. Deterministic functions are obtained by calibrating the standard deviation of the daily variations of link travel times, and probabilistic functions by the technique of copula. UE and PUE traffic assignment models are built and applied to three large cities of Mashhad, Shiraz, and Tehran in Iran. The results show that the estimated flows by PUE model replicate the observed flows in screen lines much better than the UE model, particularly for longer trips. Since PUE is computationally equivalent to UE, this improvement is attained virtually at no cost.     

Effectiveness of travel time reliability indicators in the light of the assessment of dynamic managed lane strategy

ABSTRACT

The aim of traffic management is to ensure a high quality of service for a maximum number of users by decreasing congestion and increasing safety. Uncertainty of travel times decreases the quality of service and leads end users to modify their plans regardless of the average travel time. Indicators describing travel time reliability are being developed and should be used in the future both for the optimization and for the assessment of active traffic management operations. This article discusses the efficiency of certain reliability indicators in an ex-post assessment of a traffic management strategy. Ex-post assessment is based on an observational before–after study. As some factors other than the studied management strategy may intervene between the two periods, and as most reliability indicators require knowledge of the full travel time distribution and not only its average, a methodology is developed for the identification of the impact of these exogenous factors on the whole distribution. Many reliability indicators are split into different parts allowing the identification of the part due to the management strategy impact. The methodology is tested numerically on a managed lane operation consisting of Hard Shoulder Running (HSR) at rush hour on a section of a French motorway. The variation of some reliability indicators appears misleading, whereas the splitting of the indicators increases our understanding of the strategy and highlights its impact. The paper gives the reliability assessment of the HSR field test and discusses different reliability indicators to identify their potential performances and shortcomings.     

Modeling travel time of car with varying demand on an urban midblock section

The present study analyzes the stochastic nature of travel time distribution under the uncertainty of traffic volume and the proportion of cars in the traffic stream. Stochastic response surface method (SRSM) is adopted for modeling the travel time variation under the influence of traffic composition and traffic volume. This model is applied to an uninterrupted urban arterial corridor of 1.7 km length in New Delhi. Video graphic data were collected for 2 days during morning hours between 8 AM and 12 noon and evening hours of 3–7 PM. License plate matching technique was used for measuring the travel time in the study area. This study focused on travel time variation of cars with varying traffic volume and proportion of car in the traffic stream. Linear regression analysis was carried out initially to know the functional relation and significance relation between the input and output variables, and then SRSM analysis was performed. Artificial neural network (ANN) is also considered to map the relation among travel time, traffic volume and composition of traffic stream. A comparative evaluation is made among ANN, SRSM and regression analysis. Results indicate that apart from traffic volume, the influence of car population is more on travel time variation than motorized two-wheelers. It is attributed to the smaller size and comparability better operating condition of motorized two-wheelers. Also, the ANN and SRSM models are more efficient for analyzing the stochastic relation between the response and uncertain explanatory variable than the regression model.     

基于BP神经网络的居民出行方式选择模型

以城市居民出行方式选择行为作为研究对象,分析了影响出行方式选择行为的主要因素,利用BP神经网络可以自动获取研究对象的输入、输出间关系和较强的学习训练特性,建立了基于BP神经网络的居民出行方式选择模型,并通过2009年济南市居民出行调查数据对模型进行了实例分析。结果表明：BP神经网络模型能够较好地描述居民出行交通方式选择行为。