Proposing a Simulation-Based Dynamic System Optimal Traffic Assignment Algorithm for SUMO: An Approximation of Marginal Travel Time

Mehrabani, Behzad Bamdad; Erdmann, Jakob; Sgambi, Luca; Snelder, Maaike · 2022 · Crossref

DOI: 10.52825/scp.v3i.119

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Summary

This paper investigates the comparative performance of System Optimal (SO) and User Equilibrium (UE) dynamic traffic assignments to evaluate the potential benefits of Advanced Traveler Information Systems (ATIS). The research addresses whether normative route guidance, which minimizes total system travel time, offers significant advantages over descriptive information strategies that lead to user equilibrium, where individuals minimize their own travel times. The study aims to quantify these differences across varying levels of network congestion and to examine macroscopic network-level traffic flow relationships, such as the identity between flow, concentration, and speed, in a dynamic context. The researchers employed a simulation-based heuristic algorithm using the DYNASMART (DYnamic Network Assignment Simulation Model for Advanced Road Telematics) model. Experiments were conducted on a test network comprising 50 nodes and 163 links, including a freeway and parallel street networks with signal-controlled intersections. The algorithm iteratively assigns vehicles to paths based on either marginal travel times (for SO) or average travel times (for UE), using simulation outputs to update path costs and check for convergence. The study analyzed network performance under loading factors ranging from 0.6 (low congestion) to 2.4 (extreme congestion), corresponding to vehicle counts between 11,616 and 46,674 over a 35-minute peak period. Performance metrics included average trip times, total system travel time, average network speed, and network concentration. Results indicate that SO and UE solutions are nearly identical under low congestion levels (loading factors 0.6–0.8), with SO offering negligible improvements of 0.3% to 0.5%. However, as congestion increases, the benefits of SO assignment become pronounced. At moderate congestion (loading factors 1.4–1.6), SO reduced average travel times by approximately 10–11% compared to UE. Under heavy congestion (loading factors 1.8–2.0), SO achieved substantial gains of 15.1% and 19.0%, respectively. These benefits peaked at moderate-to-high congestion levels and diminished at extreme loading levels (2.1–2.2) as the network approached gridlock, where opportunities for route optimization were limited. The study also confirmed that macroscopic relationships, such as the inverse correlation between network speed and concentration, hold at the network level in dynamic conditions, paralleling single-roadway theories. The findings suggest that ATIS strategies employing SO-based route guidance can significantly outperform descriptive, non-cooperative information strategies, particularly during moderate to high congestion periods. This implies that central controllers providing normative guidance could yield considerable system-wide efficiency gains. Furthermore, the study validates the use of network-level traffic descriptors for analyzing dynamic traffic performance and highlights the time-dependent nature of SO benefits, which accrue primarily during peak congestion intervals. These insights support the development of more effective ATIS operations by quantifying the upper bounds of benefits attainable through coordinated traffic management.

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• mental model of traffic