Improving SUMO's Signal Control Programs by Introducing Route Information

Flötteröd, Yun-Pang; Behrisch, Michael · 2018 · Crossref

DOI: 10.29007/d1xg

archive: archived pipeline: cataloged verified

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Summary

This paper addresses the challenge of data insufficiency in microscopic traffic simulations, specifically regarding the lack of accurate traffic signal timing plans. In the Simulation of Urban Mobility (SUMO) software, intersections without explicit timing data are assigned default pre-timed signals with a fixed 90-second cycle length. These defaults often fail to account for actual traffic demand, leading to unrealistic congestion and poor simulation accuracy. The authors propose a method to optimize these signal programs by integrating available route information with Webster’s delay model, thereby improving cycle lengths and green time allocations to better reflect real-world traffic conditions. The proposed approach utilizes route data—derived from sensors, traffic counts, or simulation outputs—to calculate hourly traffic volumes in Passenger Car Equivalents (PCE). The method first identifies critical flow groups for each signal phase and eliminates redundant flows caused by signal sharing among adjacent intersections. It then applies Webster’s formula to determine the optimal cycle length that minimizes vehicle delay, subject to constraints such as minimum green times and maximum cycle lengths (capped at 120 seconds for oversaturated conditions). Effective green times are subsequently allocated proportionally to the flow ratios of critical lane groups. The implementation is conducted via a Python script that processes SUMO network and route files. The effectiveness of the approach was evaluated through two case studies: a single intersection in Brunswick, Germany, and a signalized area in Berlin’s WISTA Science and Technology Park. For the single intersection, the authors redesigned signal phases to reduce unnecessary complexity before applying the optimization. Results showed significant improvements: vehicular waiting time decreased by 52%, average trip duration dropped by 22%, and time loss reduced by 40%. Pedestrian trip duration also decreased by 11%. In the area-wide study involving 13 intersections, the optimized signals reduced average travel duration by 16%, waiting time by 47%, time loss by 35%, and departure delay by 54% compared to SUMO’s default algorithm. The study concludes that incorporating route information into signal optimization significantly enhances traffic efficiency and simulation realism. The results highlight the importance of appropriate signal phase design, as improper phasing can negate optimization benefits by increasing lost time. The authors suggest future work should address lane allocation strategies, consider exclusive lanes for bicycles and motorcycles, and incorporate overlap phasing to further refine the model. This approach provides a practical solution for improving simulation accuracy in scenarios where detailed signal timing data is unavailable.

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• signaling behavior