Leveraging SUMO for Real-World Traffic Optimization: A Comprehensive Approach

Olga Dobrilko, Alon Bublil
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Abstract

This paper illuminates the utilization of SUMO as a powerful tool for addressing real-world traffic management issues. There is a gap in testing and validating solutions to in-field conditions due to the high cost and complexity of urban and suburban road networks. The validation step is often skipped, which can lead to a higher risk in implementing sophisticated solutions that exist in our multimodal transportation environment. This challenge is addressed by introducing simulations as a crucial preliminary step before real-world application. Accurate simulations require detailed data on intersection geometries, vehicle distribution, and driver behavior to accurately mirror real-world conditions. To meet these criteria, detailed sensor data on trajectories, types of road users, and their locations are extensively employed. This data forms the foundation for calibrated traffic simulations by NoTraffic™ . In conclusion, an in-depth demonstration of the method used to address a real-world traffic problem with SUMO is provided, emphasizing SUMO’s effectiveness in building confidence for deploying solutions in the field.
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利用 SUMO 实现真实世界的流量优化:综合方法
本文阐明了如何利用 SUMO 作为解决实际交通管理问题的有力工具。由于城市和郊区道路网络成本高且复杂,在测试和验证现场条件下的解决方案方面存在差距。验证步骤往往被省略,这可能导致在我们的多式联运环境中实施复杂解决方案的风险更高。为了应对这一挑战,我们在实际应用前引入了模拟这一关键的初步步骤。精确的模拟需要交叉路口几何形状、车辆分布和驾驶员行为等方面的详细数据,以准确反映现实条件。为了满足这些标准,我们广泛采用了有关轨迹、道路使用者类型及其位置的详细传感器数据。这些数据构成了 NoTraffic™ 校准交通模拟的基础。最后,我们深入展示了使用 SUMO 解决真实世界交通问题的方法,强调了 SUMO 在建立实地部署解决方案的信心方面的有效性。
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