SUMO Simulation-Based Urban Traffic Optimization in Egypt: A Case Study of Mansoura

This article focuses on Mansoura, Egypt, using SUMO microscopic traffic simulation software. Through strategies such as adjusting signal timing, lane configuration, and intersection design, congestion and emissions were effectively reduced. The study takes Umm Kulthum Square as a typical node, providing a reusable methodological framework for urban traffic management in developing countries, which has both scientific value and practical significance.

Accelerated urbanization in Egypt has led to increased traffic congestion in medium-sized cities like Mansoura. The surge in motor vehicle ownership does not match infrastructure and management systems, causing problems such as low traffic efficiency and heavy pollution. Traditional solutions rely on experience and lack quantitative evaluation. This study targets Umm Kulthum Square (an urban hub with peak-hour queues of hundreds of meters and average delays exceeding 100 seconds) to explore simulation technology as a scientific basis for decision-making.

The SUMO open-source simulation platform was used to build the model. This tool can accurately simulate vehicle behavior and is suitable for evaluating the effectiveness of control strategies. For model construction, satellite images were used to obtain road geometric parameters, and field surveys were conducted to collect data such as traffic flow, vehicle types, and turning ratios. Calibration was completed by comparing simulation and actual observed indicators (delay and queue length) and adjusting parameters until the error was within an acceptable range.

A comparison was designed between the baseline scenario (current situation) and optimization scenarios (signal timing adjustment, lane function division, additional turning lanes, phase optimization, etc.). Evaluation indicators take into account both efficiency (average travel time, delay, queue length, number of stops) and environment (CO/CO₂/HC/NOx/PMx emissions, fuel consumption, noise), avoiding the pursuit of efficiency alone while ignoring environmental costs.

The optimization plan achieved significant results: In terms of efficiency, the average delay decreased from over 100 seconds to 30-40 seconds (a reduction of >60%), and queue lengths were significantly shortened. Environmentally, pollutant emissions, fuel consumption, and noise all decreased significantly. Optimization of turning traffic flow (dedicated lanes + phase allocation) alleviated bottleneck issues, making traffic flow smoother.

Methodologically, it proves the applicability of SUMO in developing countries and establishes a complete operational process from data collection to effect evaluation. Practically, it provides decision support for Mansoura. The optimization plan does not require large-scale civil engineering; it is implemented through signal upgrades and marking adjustments, with small investment and quick results, suitable for cities with limited budgets.

This case has reference value for cities in developing countries: traffic can be improved through refined management under limited resources. The study reveals the synergy between traffic management and environmental protection (reducing congestion means reducing emissions). Limitations: single intersection, static data, simplified non-motorized modeling. Future research can expand to network-level, introduce real-time data, strengthen multi-mode simulation, and conduct post-implementation evaluations.