Commuter ride-sharing using topology-based vehicle trajectory clustering: Methodology,application and impact evaluation

This paper illustrates a ride matching method for commuting trips based on clustering trajectories, and a modeling and simulation framework with ride-sharing behaviors to illustrate its potential impact. It proposes data mining solutions to reduce traffic demand and encourage more environment-friendly behaviors. The main contribution is a new data-driven ride-matching method, which tracks personal preferences of road choices and travel patterns to identify potential ride-sharing routes for carpool commuters. Compared with prevalent carpooling algorithms, which allow users to enter departure and destination information for on-demand trips, the proposed method focuses more on regular commuting trips. The potential effectiveness of the approach is evaluated using a traffic simulation-assignment framework with ride-sharing participation using the routes suggested by our algorithm. Two types of ride-sharing participation scenarios, with and without carpooling information, are considered. A case study with the Chicago tested is conducted to demonstrate the proposed framework’s ability to support better decision-making for carpool commuters. The results indicate that with ride-matching recommendations using shared vehicle trajectory data, carpool programs for commuters contribute to a less congested traffic state and environment-friendly travel patterns.     

Hitting the sweet spot: Variability in commute lengths and vehicle emissions across a diverse state

In this paper we consider travel across Virginia and identify sustainability “sweet spots” where commute lengths and vehicle emissions per mile combine to maximize green travel in terms of total CO₂ emissions associated with commuting. The analysis is conducted across local voter precincts (N = 2373 in the state) because they are a useful proxy for neighborhoods and well-sized for implementing policy designed to encourage sustainable travel behavior. Virginia is especially appropriate for an examination of variability in sustainable travel behavior and technologies because the state’s transportation, demographic, and political patterns are particularly diverse and have been changing rapidly. We identify four Virginia precinct-based sustainability clusters: Sweet Spots, Emerging Sweet Spots, Neutral and Non-sustaining. A model of demographic differences among the clusters shows that sustainability outcomes, understood in terms of both local commute behavior and vehicle emissions, are significantly associated with the diverse demography and politics of the state. We also look at changes in transportation sustainability and socio-demographic trends within the clusters over the past half-decade, showing that differences in sustainability and demographic metrics are actually accelerating within the state over time. We conclude with a discussion of the implications of the differences among the clusters for developing and implementing effective transportation sustainability policies across the state.     

The impact of land-use mix on residents' travel energy consumption: New evidence from Beijing

Policies that encourage mixed land use are widely believed to make transport more energy efficient. However, few studies have directly examined the impacts of land-use heterogeneity on travel energy consumption at the individual level. Moreover, the definition and measures of land-use heterogeneity are debated. This paper aims to fill these gaps using the large city of Beijing, China, as a case study. Three types of land use are examined in terms of their effects on individual residents’ travel energy consumption. The results suggest that high land-use diversity and a good jobs-housing balance significantly reduces commuting travel. Interestingly, highly heterogeneous retail and housing areas may have high travel energy use, as residents are more likely to go shopping. There are obvious spatial variations in these effects. Residents of suburban ‘newtowns’, where the jobs-housing balance is particularly good, consume less travel energy. The results suggest that decreased use of conventional planning patterns, such as the socialist danwei system, and increasing urban sprawl, bring new challenges to achieving transport efficiency. Mixed land-use policies can be an effective solution to these challenges.     

Estimation of delay variability at signalized intersections for urban arterial performance evaluation

Urban arterial performance evaluation has been broadly studied, with the major focus on average travel time estimation. However, in view of the stochastic nature of interrupted flow, the ability to capture the characteristics of travel time variability has become a critical step in determining arterial level of service (LOS). This article first presents a stochastic approach that integrates classic cumulative curves and probability theories in order to investigate delay variability at signalized intersections, as a dominant part of the link travel time variability. This serves as a basis for arterial travel time estimation, which can be obtained through a convolution of individual link travel time distributions. The proposed approach is then applied in the estimation of travel time along one arterial in Shanghai, China, with abundant automatic vehicle identification (AVI) data sources. The travel time variability is evaluated thoroughly at 30-min intervals, with promising results achieved in comparison to the field measurements. In addition, the estimated travel time distributions are utilized to illustrate the probability of multiple LOS ranges, namely, reliability LOS. The results provide insights into how we might achieve a more reliable and informative understanding of arterial performance.     

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.     

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.     

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.     

城市灯控平面交叉口的自行车交通设计研究

城市灯控平面交叉口混合交通流安全和效率的核心研究对象应该是自行车与机动车交通流间的相互影响。本丈在大量的理论分析和实践的基础上,探讨时空分离法的自行车交通设计的基本原则和交通空间设计方法,即自行车右转弯专用车道设计、左转自行车二次过街以及自行车与行人过街一体化设计,并进行了南京市太平北路与珠江路交叉口的应用实例分析。本文的城市灯控平面交叉口自行车的交通设计方法可为解决城市灯控平面交叉口混合交通问题提供借鉴。     

基于FDS公路隧道火灾仿真研究

针对公路隧道突发火灾事故问题,采用模拟软件对隧道发生的火灾进行研究。分析火灾条件下公路隧道内被困人群的疏散行为特点,给出隧道内人员疏散的安全依据,按照隧道火灾条件下通风风速不同时火灾烟气蔓延情况,确定火灾可利用疏散时间(ASET),通过分析人员疏散特点计算出所需的疏散时间(RSET),通过对比得出:通风风速至少为3m/s时才能保证人员的安全疏散。     

基于时间约束的铁路空车调配系统可靠性分析

为弥补现有铁路空车调配系统缺乏空车调配可靠性研究的缺陷,在传统空车调配优化模型的基础上,本文提出了一种新的基于时间约束的空车调配可靠性分析方法。通过深入分析铁路空车调配过程,建立效益最大化目标函数,生成约束条件,以规定时间内的实际到达空车数与需求空车数之比计算出各需求站的空车调配可靠性,再利用各需求站可靠性均值求解空车调配系统的可靠性。最后,使用LINGO软件进行算例分析,论证了该方法的可行性和有效性。