Urban traffic simulation is a critical tool for infrastructure planning, including the placement of electric vehicle charging stations. However, realistic traffic simulation across many cities is hindered by two fundamental data limitations: detailed real-world traffic measurements are available for only a small fraction of road segments in most cities, and employment distribution data critical for modeling commuter traffic is rarely available at the resolution needed for simulation. This paper presents a genetic algorithm-based framework that directly addresses both limitations, calibrating urban traffic simulations from sparse road observations without requiring detailed job location data. Using the SUMO traffic simulation platform for Greensboro, North Carolina, our approach optimizes job distributions and gate-traffic parameters to align simulated traffic with a small sample of roads with known traffic-flow rates. We demonstrate that this approach produces simulated traffic that correlates well with real-world measurements, generalizes to road segments withheld from training, and produces job distributions that show promising qualitative agreement with census employment data despite never directly training on that employment data. This work demonstrates that realistic urban traffic simulation can be achieved from minimal real-world observations, offering a scalable and data-light approach to simulation calibration that reduces the barrier to deploying traffic models across diverse cities.

翻译：城市交通仿真是基础设施规划的关键工具，涵盖电动汽车充电站选址等领域。然而，跨城市开展逼真的交通仿真受制于两个基本数据局限：多数城市仅有少数路段可获得详细的实测交通数据，且通勤建模所需的关键就业分布数据极少达到仿真所需的分辨率。本文提出一种基于遗传算法的框架，直接应对这两个限制，可在不依赖详细就业地点数据的情况下，通过稀疏路侧观测数据校准城市交通仿真。基于美国北卡罗来纳州格林斯博罗市的SUMO交通仿真平台，我们的方法优化了就业分布与门控交通参数，使仿真流量与少量已知交通流率的路段实测数据吻合。实验表明：本方法生成的仿真交通与真实测量数据高度相关，可泛化至训练中未使用的路段，且产生的就业分布虽从未直接使用就业数据进行训练，却在定性层面与美国人口普查就业数据呈现令人鼓舞的一致性。这项工作证实，仅需极少量真实观测数据即可实现逼真的城市交通仿真，为仿真校准提供了一种可扩展、低数据需求的方法，显著降低了多城市部署交通模型的门槛。