Refining Urban Microscopic Traffic Simulations Accuracy Using a Customized Random Walk Model for Dynamic Stochastic Route Choice

This study introduces a novel simulation-based approach for addressing the stochastic Dynamic Traffic Assignment (DTA) problem, specifically targeting large, congested networks under dynamic conditions which is a characteristic of urban mobility environment. The proposed methodology leverages an underlying random walk model for route selection, drawing inspiration from the concept of equivalent impedance in electrical networks. This alternative route choice model iteratively condenses non-overlapping subnetworks into virtual links, allowing for the dynamic estimation of equivalent time-dependent virtual travel costs. Consequently, the downstream link choice probabilities for all destinations are computed, and by employing a random walk model, the route choice decision-making process is shifted to nodes. This approach closely aligns with travelers' real-life behavior, supporting a finer temporal segmentation of evolving traffic conditions and improving the precision of performance assessments. Furthermore, the route choice model addresses the limitations of other Markovian route choice models in handling overlapping routes and scaling issues. The Directed Acyclic Graphs (DAGs) structure is utilized to efficiently find all routes between two nodes to prevent the need for route enumeration, which is intractable in general networks. As a result, all available routes within the network can be chosen with a non-zero probability, thus avoiding the biases linked with limited route sets. The effectiveness of the proposed method is evaluated through experiments on two synthetic network models under congested demand scenarios, utilizing the Simulation of Urban MObility (SUMO) platform. The results demonstrate the method's robustness, faster convergence, and more even trip distribution compared to traditional route assignment methods, making it a viable proposal for real-time or resource-intensive applications such as microscopic demand calibration.