Simplifying traffic simulation - from Euclidean distances to agent-based models

Abstract

Urban settings require a thorough understanding of traffic patterns to best manage traffic, be prepared for emergency scenarios and to guide future infrastructure investments. In addition to analyzing collected traffic data, traffic modeling is an important tool that often requires detailed simulations that can be computationally intensive and time-consuming. A well-known comprehensive simulation framework is MATSim. On the other hand, simpler shortest-path routing systems that compute trips on an individual basis promise faster computations. The primary focus of this study is to assess the viability of a fast shortest path routing system as a method of traffic simulation. This study compares the MATSim with the Graphhopper routing system. Key metrics include travel time accuracy, congestion levels, route similarity, vehicle miles traveled, and average travel time. By analyzing these metrics, this study shows that a shortest-path routing system can serve as an effective and expedient approximation of more resource intensive simulation frameworks. This has significant implications for authorities and planners, as it offers a quick and efficient tool for traffic management and decision-making during critical events, enhancing their ability to respond quickly and effectively to dynamic traffic conditions.