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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]

As a freeway operational management strategy, High-Occupancy Toll (HOT) lanes have been deployed to manage the demand for High-Occupancy Vehicle (HOV) lanes by adjusting the tolls. By doing so, the capacity of freeways with such lanes can be used more efficiently. Periodically, setting the right amount of toll in accordance with the time-varying demand is a key to successful operation of HOT lanes; however, this is often difficult because travellers have heterogeneous willingness to pay for the toll and traffic conditions vary as the demand changes due to the imposition of tolls. This paper proposed an algorithm to determine the optimal level of toll for minimizing the total delay collectively spent by both HOVs and low-occupancy vehicles. Based on real-world traffic and survey data obtained from Gyungbu expressway in South Korea, a case study is presented to verify the applicability of the developed algorithm. The results from the case study show that the proactive dynamic pricing scheme can use the underutilized capacity of HOT lane efficiently and, thereby, shorten total travel time by 22% and generate revenue of more than $8600. Some limitations and future research agendas are also discussed. [ABSTRACT FROM AUTHOR]

This study develops a predictive continuum dynamic user-optimal model for the simultaneous departure time and route choice problem through a variational inequality (VI) approach. A polycentric urban city with multiple central business districts (CBDs) is considered, and travelers are classified into different classes according to their destinations (i.e., CBDs). The road network within the modeling city is assumed to be sufficiently dense and can be viewed as a continuum. A predictive dynamic user-optimal (PDUO) model has been previously used to model traffic flow with a given traffic demand distribution, in which travelers choose the routes that minimize the actual travel cost to the CBD. In this work, we combine the departure time choice with the PDUO model to study the simultaneous departure time and route choice problem. The user-optimal departure time principle is satisfied, which states that for each origin–destination pair, the total costs incurred by travelers departing at any time are equal and minimized. We then present an equivalent VI and solve it using the projection method after discretization based on unstructured meshes. A numerical experiment for an urban city with two CBDs is presented to demonstrate the effectiveness of the numerical algorithm. [ABSTRACT FROM AUTHOR]

This paper presents the development of a macroscopic dynamic traffic assignment model for continuum transportation systems with elastic demand. A reactive dynamic user equilibrium model is extended to simulate network equilibrium problems for an urban area with multiple central business districts (CBDs). Each copy of traffic flow reactively makes route choice decisions to minimize the total travel cost from origin to destination, based on instantaneous traffic information from a radio broadcasting service or route guidance system. The elastic demand function for each copy of flow is associated with its total instantaneous travel cost. The model is solved by a cell-centered finite volume method for conservation law equations and a fast sweeping method for Eikonal-type equations on unstructured grids. Numerical experiments for an urban traffic network with two CBDs are presented to verify the rationality of the model and the validity of the numerical method. The numerical results indicate that the model captures some macroscopic characteristics of two copies of flow interacting in time-varying urban transportation systems, e.g., the spatial distributions of the flow density and flux and the path choice behavior in response to elastic demand, and can describe traffic equilibrium phenomena, such as self-organization and traffic congestion build-up and dissipation. [ABSTRACT FROM AUTHOR]