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In this paper, we develop a customized path-based algorithm for solving the stochastic multi-class traffic assignment problem with asymmetric interactions, route overlapping, and vehicle restrictions. The algorithm consists of an iterative balancing scheme to find the search direction, a self-regulated averaging line search scheme to determine a suitable stepsize, and a column generation scheme to generate a universal path set for multiple vehicle classes. These three schemes work together in the customized path-based algorithm to solve the stochastic multi-class traffic assignment problem. The solution algorithm simultaneously considers the asymmetric interactions among different vehicle types through the link travel time functions, various vehicle restrictions in a transportation network, and route overlapping using the path-size logit model for accounting random perceptions of network conditions in a stochastic user equilibrium framework. A real network in the city of Winnipeg, Canada, is used to examine the computational performance of the customized path-based algorithm. In addition, sensitivity analyses are conducted to test the algorithmic effectiveness with respect to several model parameters and percentages of trucks in the transportation network. Numerical results reveal that the path-based algorithm with the self-regulated averaging line search scheme is computationally effective in solving the stochastic multi-class traffic assignment problem with different modeling considerations. The algorithm is also computationally robust against various model parameters in the sensitivity analyses. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY Recent empirical studies have revealed that travel time variability plays an important role in travelers' route choice decisions. To simultaneously account for both reliability and unreliability aspects of travel time variability, the concept of mean-excess travel time (METT) was recently proposed as a new risk-averse route choice criterion. In this paper, we extend the mean-excess traffic equilibrium model to include heterogeneous risk-aversion attitudes and elastic demand. Specifically, this model explicitly considers (1) multiple user classes with different risk-aversions toward travel time variability when making route choice decisions under uncertainty and (2) the elasticity of travel demand as a function of METT when making travel choice decisions under uncertainty. This model is thus capable of modeling travelers' heterogeneous risk-averse behaviors with both travel choice and route choice considerations. The proposed model is formulated as a variational inequality problem and solved via a route-based algorithm using the modified alternating direction method. Numerical analyses are also provided to illustrate the features of the proposed model and the applicability of the solution algorithm. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY Capacity reliability is defined as the probability that the network capacity can accommodate a certain volume of traffic demand at a required service level. It is a supply-side reliability measure for assessing the adequacy of a degradable transportation network. The network capacity model used to calculate the capacity reliability measure is based on the concept of reserve capacity, which requires preserving a pre-determined origin-destination (O-D) demand pattern. In this paper, we relax this assumption by allowing a non-uniform growth in the spatial distribution of the O-D demand pattern. By using this non-uniform O-D growth approach, two network capacity models related to the concepts of ultimate capacity and practical capacity are developed to estimate alternate capacity reliability measures. Numerical results are provided to analyze the features of three capacity reliability measures for transportation networks. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Estimation of intersection turning movements is one of the key inputs required for a variety of transportation analysis, including intersection geometric design, signal timing design, traffic impact assessment, and transportation planning. Conventional approaches that use manual techniques for estimation of turning movements are insensitive to congestion. The drawbacks of the manual techniques can be amended by integrating a network traffic model with a computation procedure capable of estimating turning movements from a set of link traffic counts and intersection turning movement counts. This study proposes using the path flow estimator, originally used to estimate path flows (hence origin-destination flows), to derive not only complete link flows, but also turning movements for the whole road network given some counts at selected roads and intersections. Two case studies using actual traffic counts are used to demonstrate the proposed intersection turning movement estimation procedure. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Path finding problems have many real-world applications in various fields, such as operations research, computer science, telecommunication, transportation, etc. In this paper, we examine three definitions of optimality for finding the optimal path under an uncertain environment. These three stochastic path finding models are formulated as the expected value model, dependent-chance model, and chance-constrained model using different criteria to hedge against the travel time uncertainty. A simulation-based genetic algorithm procedure is developed to solve these path finding models under uncertainties. Numerical results are also presented to demonstrate the features of these stochastic path finding models. [ABSTRACT FROM AUTHOR]