This article presents a new multi-objective mathematical programming framework to model interactions between public and private sector in constructing and maintaining highway networks using the build, operate, and transfer scheme. In this study, private companies are assumed to have a degree of control over highway sections on which they perform maintenance and rehabilitation and capacity expansion activities. The private investors recover the cost of construction by levying tolls. The public agency is assumed to maintain the rest of the network with the objective of minimizing total system generalized cost. The bi-directional impact of roadway utilization on deterioration and deterioration on utilization is modeled in this study. The model accounts for route choice of users and all users are assumed to choose routes that have equal and minimal experienced generalized cost. The nonconvex and discontinuous multi-objective mathematical program is solved using nondominant sorting genetic algorithm-II and the pareto-optimal trade-off surface between the profit of the private company and the total system cost is generated. Computational runs are conducted to demonstrate the suitability and flexibility of the developed framework in modeling various policy decisions such as the presence of noncompete clauses.

One of the critical elements in considering any real-time traffic management strategy requires assessing network traffic dynamics. Traffic is inherently dynamic, since it features congestion patterns that evolve over time and queues that form and dissipate over a planning horizon. Dynamic traffic assignment (DTA) is therefore gaining wider acceptance among agencies and practitioners as a more realistic representation of traffic phenomena than static traffic assignment. Though it is imperative to calibrate the DTA model such that it can accurately reproduce field observations and avoid erroneous flow predictions when evaluating traffic management strategies, DTA calibration is an onerous task due to the large number of variables that can be modified and the intensive computational resources required. To compliment other research on behavioral and trip table issues, this work focuses on DTA capacity calibration and presents an efficient Dantzig-Wolfe decompositionbased heuristic that decomposes the problem into a restricted master problem and a series of pricing prob- * lems. The restricted master problem is a capacity manipulation problem, which can be solved by a linear programming solver. The pricing problem is the user optimal DTA which can be optimally solved by an existing combinatorial algorithm. In addition, the proposed set of dual variable approximation techniques is one of a very limited number of approaches that can be used to estimate network-wide dual information in facilitating algorithmic designs while maintaining scalability. Two networks of various sizes are empirically tested to demonstrate the efficiency and efficacy of the proposed heuristic. Based on the results, the proposed heuristic can calibrate the network capacity and match the counts within a 1% optimality gap.

Given the vast amounts of data automatically collected by traffic detectors, identifying erroneous data is an important and challenging issue. In this paper, we develop a fuzzy logic approach for quantifying the reliability of data obtained from traffic detectors. Previous researchers have proposed multiple criteria for determining erroneous data; broadly speaking, these approaches either consider fundamental consistency (is the data physically plausible?), network consistency (is the data consistent with observations at nearby detectors?), and historical consistency (is the data plausible given past observations at this location?). This paper proposes a classifier incorporating all of these criteria, applying fuzzy logic to integrate these three separate assessments. An example application is given, utilizing data collected in the Dallas, TX, region.

Stochastic shipper carrier model, L shaped method, Regularized decomposition, Capacity shifting heuristic,