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.     

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。     

Subjective-utility travel time budget modeling in the stochastic traffic network assignment

ABSTRACT

In this article, we propose a new model called subjective-utility travel time budget (SU-TTB) model to capture travelers' risk-averse route choices. In the travel time budget (TTB) and mean-excess travel time (METT) model, a predefined confidence level is needed to capture the risk-aversion in route choice. Due to the day-to-day route travel time variations, the exact confidence level is hard to be predicted. With the SU-TTB model, we assume travelers' confidence level belongs to an interval that they may comply with in the route choice. The two main components of SU-TTB are the utility function and the TTB model. We can show that the SU-TTB can be reduced to the TTB and METT model with proper utility function for the confidence levels. We can also prove its equivalence with our recently proposed nonlinear-expectation route travel time (NERTT) model in some cases and give some new interpretation on the NERTT with this equivalence. Finally, we formulate the SU-TTB model as a variational inequality (VI) problem to model the risk-averse user equilibrium (RAUE), termed as generalized RAUE (GRAUE). The GRAUE is solved via a heuristic gradient projection algorithm, and the model and solution algorithm are demonstrated with the Braess's traffic network and the Nguyen and Dupuis's traffic network.     

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.     

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.     

Urban travel time reliability at different traffic conditions

The decision making of travelers for route choice and departure time choice depends on the expected travel time and its reliability. A common understanding of reliability is that it is related to several statistical properties of the travel time distribution, especially to the standard deviation of the travel time and also to the skewness. For an important corridor in Changsha (P.R. China) the travel time reliability has been evaluated and a linear model is proposed for the relationship between travel time, standard deviation, skewness, and some other traffic characteristics. Statistical analysis is done for both simulation data from a delay distribution model and for real life data from automated number plate recognition (ANPR) cameras. ANPR data give unbiased travel time data, which is more representative than probe vehicles. The relationship between the mean travel time and its standard deviation is verified with an analytical model for travel time distributions as well as with the ANPR travel times. Average travel time and the standard deviation are linearly correlated for single links as well as corridors. Other influence factors are related to skewness and travel time standard deviations, such as vehicle density and degree of saturation. Skewness appears to be less well to explain from traffic characteristics than the standard deviation is.     

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.     

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.     

A route guidance system considering travel time unreliability

Under a stochastic roadway, drivers need a route guidance system incorporating travel time variability. To recommend a customized path depending on the trip purpose and the driver’s risk-taking behavior, various path ranking methods have been developed. Unlike those methods, our proposed disutility method can easily incorporate a target arrival time in the ranking process by measuring how late the travel is and by penalizing it depending on the severity of lateness. In addition, the disutility-based route guidance system can properly address travel time unreliability that causes unacceptable disruptions to the driver’s schedule (i.e., unexpected long delay). We compare the disutility-based path ranking method with other ranking methods, the percentile travel time, the mean excess travel time, and the on-time arrival probability. We show that the disutility has stronger discriminating power and requires less solution space to find an optimal path. The most important advantage is that it can estimate a driver’s risk-taking behavior for each trip purpose by using the discrete choice analysis. We construct a simulation framework to acquire the travel time data on a hypothetical roadway. We analyze the data and show how various ranking methods recommend a customized path. Using the data, we show the advantage of the disutiltiy method over the other methods, which is generating a customized path with respect to the target arrival time by properly penalizing the travel time lateness.     

引入网络影响分析的道路选线优化模型

提出一个基于遗传算法的道路选线优化模型,该模型首先随机生成新建道路空间位置的候选方案集,并依据GIS平台自动设计新建道路的平竖曲线,并计算其各项工程费用;接下来,分析变化了的路网的拓扑结构,并在新路网中分配OD交通量,获得OD交通在新路网上的交通流特征,进而计算OD交通量的走行时间和在路网上的环境负荷;然后,利用工程费用、OD交通走行时间费用和环境负荷当量费用构造适应度函数,以判断各候选方案的优劣,同时实施选择、交叉、变异操作以获得更多的候选方案,直至得到一个最佳的新建道路方案为止。数字试验结果显示:新建道路对路网的影响不容忽视。     

Quantifying travel time variability at a single bottleneck based on stochastic capacity and demand distributions

Travel time reliability, an essential factor in traveler route and departure time decisions, serves as an important quality of service measure for dynamic transportation systems. This article investigates a fundamental problem of quantifying travel time variability from its root sources: stochastic capacity and demand variations that follow commonly used log-normal distributions. A volume-to-capacity ratio-based travel time function and a point queue model are used to demonstrate how day-to-day travel time variability can be explained from the underlying demand and capacity variations. One important finding is that closed-form solutions can be derived to formulate travel time variations as a function of random demand/capacity distributions, but there are certain cases in which a closed-form expression does not exist and numerical approximation methods are required. This article also uses probabilistic capacity reduction information to estimate time-dependent travel time variability distributions under conditions of non-recurring traffic congestion. The proposed models provide theoretically rigorous and practically useful tools for understanding the causes of travel time unreliability and evaluating the system-wide benefit of reducing demand and capacity variability.     

高速路段动态OD反推模型与算法研究

各进出口匝道间的实时交通量是高速公路管理系统重要的输入数据,也是难以获得的数据。本文回顾了动态OD反推理论的发展历程,提出了动态路径走行时间的完善和简化计算方法,在此基础上建立了基于高速路段的动态OD反推模型,设计了遗传算法对问题进行求解,并讨论了算法中的六个关键问题,提供了一种获得高速公路进出口间动态OD交通量的有效方法。实例研究表明,模型和算法具有较高的效率和准确性,能够应用于在线系统。     

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.      

Modeling arterial travel time distribution by accounting for link correlations: a copula-based approach

The estimation of urban arterial travel time distribution (TTD) is critical to help implement Intelligent Transportation Systems (ITS) and provide travelers with timely and reliable route guidance. The state-of-practice procedure for arterial TTD estimation commonly assumes that the path travel time follows a certain distribution without considering link correlations. However, this approach appears inappropriate since travel times on successive links are essentially dependent along signalized arterials. In this study, a copula-based approach is proposed to model arterial TTD by accounting for spatial link correlations. First, TTDs on consecutive links along one arterial in Hangzhou, China are investigated. Link TTDs are estimated through the nonparametric kernel smoothing method. Link correlations are analyzed in both unfavorable and favorable coordination cases. Then, Gaussian copula models are introduced to model the dependent structure between link TTDs. The parameters of Gaussian copula are obtained by Maximum-Likelihood Estimation (MLE). Next, path TTDs covering consecutive links are estimated based on the estimated copula models. The results demonstrate the advantage of the proposed copula-based approach, compared with the convolution without capturing link correlations and the empirical distribution fitting methods in both unfavorable and favorable coordination cases.     

Exploiting floating car data for time-dependent Origin–Destination matrices estimation

The study evaluates the added value generated by estimating dynamic demand matrices by information gathered from Floating Car Data (FCD).

Firstly, adopting a large dataset of FCD collected in Rome, Italy, during May 2010, all the monitored trips on a specific district of the city (Eur district) have been collected and analysed in terms of (i) spatial and temporal distribution; (ii) actual route choices and travel times. The data analysis showed that demand data from FCD are usually not suitable to retrieve directly demand matrices, due to a strong dependence of this information from the penetration rate of the monitoring device. Instead, origin–destination travel times and route choice probabilities from FCD are a much more reliable and powerful information with respect to FCD origin–destination flows, since they represent the traffic conditions and behaviors that vehicles experiment along the path.

Thus, several synthetic experiments have been conducted adopting both travel times and route choice probabilities as additional information, with respect to standard link measurements, in the dynamic demand estimation problem. Results demonstrated the strength and robustness associated to these network based data, while link measurements alone are not able to define the real traffic pattern. Adopting both the information of origin–destination travel times and route choice probabilities during the demand estimation process, the spatial and temporal reliability of the estimated demand matrices consistently increases.     

基于道路设计与交通规划的道路选线优化模型

结合道路设计理论与交通规划理论,以地理信息系统为平台,开发道路选线优化模型。该模型是一个费用指向的优化问题,最小化费用目标函数中包括道路建设费用、土方工程费、道路交通诱发的环境污染的不经济费用,OD交通在路网上总走行时间的时间费用等。优化模型首先随机生成新建道路的空间位置候选方案集,并自动设计新建道路的平曲线和竖曲线,计算新建道路的各项工程费用。然后,对变化的路网进行自动拓扑,通过交通量分配得到OD交通在新路网上的走行时间和交通流特征,计算OD交通的环境负荷。最后,在遗传算法中判断候选方案的优劣,直到得到一个最佳的新建道路的空间位置方案为止。     

Developing travel time estimation methods using sparse GPS data

Existing methods of estimating travel time from GPS data are not able to simultaneously take account of the issues related to uncertainties associated with GPS and spatial road network data. Moreover, they typically depend upon high-frequency data sources from specialist data providers, which can be expensive and are not always readily available. The study reported here therefore sought to better estimate travel time using “readily available” vehicle trajectory data from moving sensors such as buses, taxis, and logistical vehicles equipped with GPS in “near” real time. To do this, accurate locations of vehicles on a link were first map-matched to reduce the positioning errors associated with GPS and digital road maps. Two mathematical methods were then developed to estimate link travel times from map-matched GPS fixes, vehicle speeds, and network connectivity information with a special focus on sampling frequencies, vehicle penetration rates, and time window lengths. Global positioning system (GPS) data from Interstate I-880 (California) for a total of 73 vehicles over 6 h were obtained from the University of California Berkeley's Mobile Century Project, and these were used to evaluate several travel time estimation methods, the results of which were then validated against reference travel time data collected from high resolution video cameras. The results indicate that vehicle penetration rates, data sampling frequencies, vehicle coverage on the links, and time window lengths all influence the accuracy of link travel time estimation. The performance was found to be best in the 5-min time window length and for a GPS sampling frequency of 60 s.     

Evaluation of transportation alternatives for aging population in the era of self-driving vehicles

ObjectiveAccess to transportation is very important for older adults as it allows them to lead an independent and social life. With a growing elderly population, providing access to transportation is one of the greatest societal challenges in the United States. A feasible solution to this problem is adoption of self-driving vehicles in order to provide safe, convenient, and affordable transportation services to older adults. This study analyzed older adults' acceptance of three transportation alternatives that utilize self-driving vehicles. In addition, older adults' travel behavior and transportation needs were also assessed.MethodsAn online survey approach was adopted to collect data on older adults' travel behavior, transportation needs, and to evaluate three transportation alternatives. The three self-driving transportation alternatives presented were owning a self-driving vehicle, using prepaid taxi services, and getting rides through community services. A total of 173 survey responses were collected from participants aged 60 years or older.ResultsParticipants (80% or more) responded that they are satisfied with the number of trips they make to stores, medical centers, and to religious and social events. On the other hand, more than one-third of the participants would make more trips than they currently do to visit family and friends and to recreational places if rides were available. Among the three alternatives, prepaid taxi services received the best overall ratings. A majority of the participants also responded that owning a self-driving vehicle was not affordable to them.ConclusionsThis study provides valuable information that could be used to design future transportation services for the elderly population. Older adults are willing to adopt self-driving vehicles as a mode of transportation. However, automakers and federal authorities need to make efforts to inform older adults about the benefits of these vehicles, and their safety features.     

Outsourced probe data effectiveness on signalized arterials*

ABSTRACT

This paper presents results of an I-95 Corridor Coalition sponsored project to assess the ability of outsourced vehicle probe data to provide accurate travel time on signalized roadways for the purposes of real-time operations as well as performance measures. The quality of outsourced probe data on freeways has led many departments of transportation to consider such data for arterial performance monitoring. From April 2013 through June of 2014, the University of Maryland Center for Advanced Transportation Technology gathered travel times from several arterial corridors within the mid-Atlantic region using Bluetooth traffic monitoring (BTM) equipment, and compared these travel times with the data reported to the I95 Vehicle Probe Project (VPP) from an outsourced probe data vendor. The analysis consisted of several methodologies: (1) a traditional analysis that used precision and bias speed metrics; (2) a slowdown analysis that quantified the percentage of significant traffic disruptions accurately captured in the VPP data; (3) a sampled distribution method that uses overlay methods to enhance and analyze recurring congestion patterns. (4) Last, the BTM and VPP data from each 24-hour period of data collection were reviewed by the research team to assess the extent to which VPP captured the nature of the traffic flow. Based on the analysis, probe data is recommended only on arterial roadways with signal densities (measured in signals per mile) up to one, and it should be tested and used with caution for signal densities between one and two, and is not recommended when signal density exceeds two.     

考虑异质性出行需求的主支线公交树网络优化设计

公交线网优化设计是指在一定的运行约束条件下,选择1组公交线路和相关频率以达到优化目标的设计过程,可以表示为一个优化问题。针对具有高异质性出行需求的主支线公交树网络,在考虑客流需求和运营约束的前提下,以用户和运营者的成本最小为目标,提出了1种多目标非线性混合整数优化模型。优化变量为候选线路服务频率。为求解这一模型,设计了1种基于改进的布谷鸟算法的高效元启发式方法。该方法包括初始候选路线集生成过程;基于MNL模型的公交分配过程;确定路线服务频率的改进布谷鸟算法过程。通过算例验证了该方法的有效性和适用性。数值分析结果表明,该算法通过对所有可能的候选路径的服务频率选择得到接近最优的公交线路网络。另一方面,通过保持高峰时的公交线路为有效备择线路,为具有异质性出行需求的网络的重新设计提供了更好的解决方案。此外,该系统在1次运行中产生了1组帕累托解,其允许公交线网设计师评估运营商成本和乘客成本并做出折中方案。通过比较3种算法的计算结果和CPU时间,证明了改进的布谷鸟算法的可靠性和有效性。另外还研究了最优公交网络设计与公交运行速度、总需求规模等关键设计输入参数之间的关系,分析结果表明,关键设计输入参数与最优公交网络具有一定的协同效应。模型与算法为实际的大规模主支线公交树网络的优化设计提供了1种有效的工具。