Abstract

Recently, an open-source light-weight dynamic traffic assignment (DTA) package, namely DTALite, has been developed to allow a rapid utilization of advanced dynamic traffic analysis capabilities. Aiming to bridge the modeling gaps between multi-agent simulation and optimization in a multimodal environment, we further design and develop DTALite-S to simplify the traffic flow dynamic representation details in DTALite for future extensions. We hope to offer a unified modeling framework with inherently consistent space–time network representations for both optimization formulation and simulation process. This paper includes three major modeling components: (1) mathematic formulations to describe traffic and public transportation simulation problem on a space–time network; (2) transportation transition dynamics involving multiple agents in the optimization process; (3) an alternating direction method of multipliers (ADMM)-based modeling structure to link different features between multi-agent simulation and optimization used in transportation. This unified framework can be embedded in a Lagrangian relaxation method and a time-oriented sequential simulation procedure to handle many general applications. We carried out a case study by using this unified framework to simulate the passenger traveling process in Beijing subway network which contains 18 urban rail transit lines, 343 stations, and 52 transfer stations. Via the ADMM-based solution approach, queue lengths at platforms, in-vehicle congestion levels and absolute deviation of travel times are obtained within 1560 seconds.The case study indicate that the open-source DTALite-S integrates simulation and optimization procedure for complex dynamic transportation systems and can efficiently generate comprehensive space-time traveling status.

Highlights

  • To understand and analyze future emerging mobility scenarios, planers and engineers need to utilize many different simulation tools to generate corresponding modeling results

  • Via the alternating direction method of multipliers (ADMM)-based solution approach, queue lengths at platforms, in-vehicle congestion levels and absolute deviation of travel times are obtained within 1560 seconds.The case study indicate that the open-source DTALite-S integrates simulation and optimization procedure for complex dynamic transportation systems and can efficiently generate comprehensive space-time traveling status

  • This paper considers the vehicle route scheduling optimization in the broader framework of public transportation and develops a unified space–time framework to improve public transportation mobility

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Summary

Introduction

To understand and analyze future emerging mobility scenarios, planers and engineers need to utilize many different simulation tools to generate corresponding modeling results. Optimization, on the other hand, needs to search a solution in the dynamic (possibly complex) system subject to a number of constraints. Transportation planners and engineers utilize simulation tools to evaluate and further optimize a subset of system’s parameters, but there is a critical modeling gap between simulation and optimization for complex dynamic transportation systems. To bridge such a gap in a multimodal environment, this research focuses on how to offer a theoretically sound and practically useful modeling framework with a simplified traffic flow dynamic

Literature Review
Paper Structure
Motivation
Overall Modeling Framework
Station2
Simulation Flowchart Based on Simple Data Structure
Case Study
Conclusions
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