Modeling and solving discrete network design problem with stochastic user equilibrium

In this paper, we address the discrete network design problem, which determines the addition of new roads to existing transportation network to optimize the transportation system performance. Road users are assumed to follow the traffic assignment principle of stochastic user equilibrium. A mixed‐integer nonlinear nonconvex problem is developed to model this discrete network design problem with stochastic user equilibrium. The original problem is relaxed into a convex mixed‐integer nonlinear program, whose solution provides a lower bound of the original problem. The relaxed problem is then embedded into two proposed global optimization solution algorithms to obtain the global optimal solution of the problem. Copyright © 2016 John Wiley & Sons, Ltd.     

Revisiting the application of simultaneous perturbation stochastic approximation towards signal timing optimization

Simultaneous Perturbation Stochastic Approximation (SPSA) has gained favor as an efficient optimization method for calibrating computationally intensive, “black box” traffic flow simulations. Few recent studies have investigated the efficiency of SPSA for traffic signal timing optimization. It is important for this to be investigated, because significant room for improvement exists in the area of signal optimization. Some signal timing methods and products perform optimization very quickly, but deliver mediocre solutions. Other methods and products deliver high-quality solutions, but at a very slow rate. When using commercialized desktop signal timing products, engineers are often forced to choose between speed and solution quality. Real-time adaptive control products, which must optimize timings within seconds on a cycle-by-cycle basis, have limited time to reach a high-quality solution. The existing literature indicates that SPSA provides the potential for upgrading both off-line and on-line solutions alike, by delivering high-quality solutions within seconds. This article describes an extensive set of optimization tests involving SPSA and genetic algorithms (GAs). The final results suggest that GA was slightly more efficient than SPSA. Moreover, the results suggest today's signal timing solutions could be improved significantly by incorporating GA, SPSA, and “playbooks” of preoptimized starting points. However, it may take another 5–10 years before our computers become fast enough to simultaneously optimize coordination settings (i.e., cycle length, phasing sequence, and offsets) at numerous intersections, using the most powerful heuristic methods, at speeds that are compatible with real-time adaptive solutions.     

A practical concern about the relevance of alternative-specific constants for new alternatives in simple logit models

When a “new” alternative is introduced, post-estimation, into a logit model, analysis initially exclude an ASC or occasionally assume a correspondence with an existing alternative. Using a recent data set for mode and route choice, the paper highlights the forecasting implications of ignoring the ASC in the utility expression of a “new” alternative, and how sensitive the market shares are to the inclusion/ exclusion of the ASC.     

Refocusing the Modelling of Freight Distribution: Development of an Economic-Based Framework to Evaluate Supply Chain Behaviour in Response to Congestion Charging

The distribution of freight is a major contributor to the levels of traffic congestion in cities. However it is much neglected in the research and planning activities of government, where the focus is disproportionately on passenger vehicle movements. Despite the recent recognition of the contribution of freight transportation to the performance of urban areas under the rubric of city logistics, we see a void in the study of how the stakeholders in the supply chain might cooperate through participation in distribution networks, to reduce the costs associated with traffic congestion. Given that transport costs are typically over 45 of all distribution costs, with congestion a major contributor in the urban setting, the importance of establishing ways in which supply chain partnerships might cooperate to reduce levels of freight vehicle movements has much merit. This paper sets out a framework to investigate how agents in a retail supply chain might interact more effectively to reduce the costs of urban freight distribution. We propose an interactive agency choice method as a way of formalising a framework for studying the preferences of participants in the supply chain to support specific policy initiatives. Such a framework is a powerful way of investigating the behavioural response of each agent to many policies, including congestion pricing, as a way of improving the efficient flow of traffic in cities.