Adaptive signal timing optimizations can improve the efficiency of road networks and reduce the emissions of pollutants, but most of the current studies still rely on simplified analytical methods to depict complex road transport systems and focus on optimizing traffic signals at an isolated intersection. A framework that integrates computer vision and traffic modeling is proposed to link the real-world transport systems and operable virtual traffic models for the signal timing optimization at multiple intersections. The integrative framework consists of six main steps, including configuring real-time video sources, conducting transfer-learning to develop the vehicle detector, comparing and selecting vehicle trackers, collecting traffic parameters by referring to the CV-TM ontology, establishing and running the traffic model, and operating simulation-based optimizations. The proposed integrative framework is demonstrated through a case study of the signal timing optimization at multi-intersections in a real-world road network. Three critical information items including the traffic volumes, vehicle compositions, and vehicles’ turning ratios are derived from real-time surveillance videos, and the extracted information is then automatically incorporated into TM to optimize the signal timings of interconnected intersections in a near-real-time manner. In comparison with the original signal scheme, the optimized one can reduce 14.2 % of average vehicle delays, 18.9 % of vehicle stops, 9.1 % of average travel time, and 2.3 % of pollutant emissions in this specific case. The results indicate that synchronously optimizing signal timings at multiple intersections increase not only the transportation efficiency but also the environmental friendliness of road transport systems. The proposed CV-TM integration framework is demonstrated to be a promising way for conducting near-real-time signal timing optimizations in intricate traffic scenes instead of at isolated intersections, helping decision-makers to promptly respond to the time-varying traffic conditions during various real-world events, and facilitating the transportation systems and cities to achieve sustainable development goals.

自适应信号定时优化可以提高道路网络的效率并减少污染物的排放，但是当前的大多数研究仍然依靠简化的分析方法来描绘复杂的道路运输系统，并着重于在孤立的交叉路口优化交通信号。提出了一个集成计算机视觉和交通模型的框架，以链接现实世界的交通系统和可操作的虚拟交通模型，以实现多个交叉路口的信号定时优化。集成框架包括六个主要步骤，包括配置实时视频源，进行传递学习以开发车辆检测器，比较和选择车辆跟踪器，通过参考CV-TM本体收集交通参数，建立和运行交通模型，以及基于操作仿真的优化。通过对现实世界道路网络中多个交叉口的信号时序优化进行案例研究，证明了所提出的集成框架。从实时监控视频中获得了三个关键信息项，包括交通量，车辆组成和车辆转弯率，然后将提取的信息自动合并到TM中，以优化近乎实时的互连交叉路口的信号定时。时间的方式。与原始信号方案相比，经过优化的方案可以减少这种情况下平均车辆延迟的14.2％，车辆停止的18.9％，平均行驶时间的9.1％和污染物排放的2.3％。结果表明，在多个路口同步优化信号定时不仅可以提高运输效率，而且可以提高道路运输系统的环境友好性。事实证明，提议的CV-TM集成框架是一种在复杂交通场景中而不是在孤立的交叉路口进行近实时信号定时优化的有前途的方法，有助于决策者在各种情况下迅速响应时变的交通情况现实世界的事件，并为交通系统和城市提供便利，以实现可持续发展目标。