No abstract

In a passenger railroad system, the stopping pattern optimization problem determines the train stopping strategy, taking into consideration multiple train classes, station types, and customer origin‐destination (OD) demand, to maximize the profit made by a rail company. The stopping pattern is traditionally decided by rule of thumb, an approach that leaves much room for improvement. In this article, we propose an integer program for this problem and provide a systematic approach to determining the optimal train stopping pattern for a rail company. Commonly used commercial optimization packages cannot solve this complex problem efficiently, especially when problems of realistic size need to be solved. Therefore, we develop two genetic algorithms, namely binary‐coded genetic algorithm (BGA) and integer‐coded genetic algorithm (IGA). In many of the past evolutionary programming studies, the chromosome was coded using the binary alphabet as BGA. The encoding and genetic operators of BGA are straightforward and relatively easy to implement. However, we show that it is difficult for the BGA to converge to feasible solutions for the stopping pattern optimization problem due to the complex solution space. Therefore, we propose an IGA with new encoding mechanism and genetic operators. Numerical results show that the proposed IGA can solve real‐world problems that are beyond the reach of commonly used optimization packages.

Periodic infrastructure maintenance is crucial for a safe and efficient transportation system. Numerous decision models for the maintenance planning problem have been proposed in the literature. However, to the best of our knowledge, no model exists that simultaneously accounts for traffic dynamics and is intended for long‐term planning purposes. This article addresses this gap in the literature. A mixed‐integer bi‐level program is introduced that minimizes the long‐term maintenance cost as well as the total system travel time. For the solution approach we utilize a genetic algorithm in conjunction with mesoscopic traffic simulation. The model is illustrated via a numerical example.

The traditional trip based approach to transportation modeling has been employed for the past thirty years. However, due to the limitations of traditional planning for short-term policy analysis, researchers have explored alternative paradigms for incorporating more behavioral realism in planning methodologies. On the demand side, activity-based approaches have evolved as an alternative to traditional trip-based transportation demand forecasting. On the supply side, dynamic traffic assignment models have been developed as an alternative to static assignment procedures. Unfortunately, much of the research efforts in activity-based approaches (the demand side) and dynamic traffic assignment techniques (the supply side) have been undertaken relatively independently. To maximize benefits from these advanced methodologies, it is essential to combine them via a unified framework. The objective of the current paper is to develop a conceptual framework and explore practical integration issues for combining the two streams of research. Technical, computational and practical issues involved in this demand-supply integration problem are discussed. While the framework is general in nature, specific technical details related to the integration are explored by employing CEMDAP for activity-based modeling and VISTA for the dynamic traffic assignment modeling. Solution convergence properties of the integrated system, specifically examining different criteria for convergence, different methods of accommodating time of day and the influence of step size on the convergence are studied. Further, the integrated system developed is empirically applied to two sample networks selected from the Dallas Fort Worth network.

The occurrence of natural disasters in the coastal regions and numerous potential events within urban regions has drawn considerable attention among transportation stakeholders. Federal, state and local officials need to be effectively prepared to address the challenges raised by an evacuation. The focus of this research effort is to develop a tool to study the repercussions of evacuation of an entire regional transportation network recognizing the human behavior element. Neglecting these seemingly chaotic traffic flow patterns would lead to inaccurate system assessment and predictions. We study the influences of evacuees' locations in the urban region at the moment of emergency alert. In addition, we identify the locations of all the members of the household and explicitly consider household member interactions. Further, we study the accurate times the individuals enter the network to evacuate the study region, which can vary based on where the other household members are located at that time and the travel time on the network to reach these locations. To accomplish the goals, we employ the integration framework of activitybased modeling and dynamic traffic assignment to study the evacuation traffic flow patterns at the time of evacuation. Specifically, the paper formulates the evacuation problem and discusses the utility of deploying the integrated module of activity-based modeling and dynamic traffic assignment for evacuation planning and outlines the challenges in integrating these two tools.