Timely and reliable accident detection provides a foundation for traffic accident management (TIM), which is critical functionality for traffic management agencies. Effective TIM strategies mitigate negative impacts caused by non-recurrent events, improve quality of service and traveler satisfaction, and enhance transportation resilience. Most existing studies focus on traffic accident detection and system mobility. Very few systems attempted to quantify the environmental impacts of accidents. We examine a cloud-based data platform that fuses information from real-world traffic, probe vehicle data, and road weather. Moreover, we developed a data-driven approach to estimate the impacts of accidents using Otsu’s method, morphological operation, time-series prediction, and emissions simulator model, allowing us to quantify the benefits of advanced accident detection. The proposed method was evaluated with a real-world scenario, showing that the studied accident may cause additional energy waste by 38% and CO emissions by 36%.

及时可靠的事故检测为交通事故管理 (TIM) 奠定了基础，这是交通管理机构的关键功能。有效的 TIM 策略可减轻非经常性事件造成的负面影响，提高服务质量和旅客满意度，并增强交通弹性。大多数现有研究都集中在交通事故检测和系统移动性上。很少有系统试图量化事故对环境的影响。我们研究了一个基于云的数据平台，该平台融合了来自现实世界交通、探测车辆数据和道路天气的信息。此外，我们开发了一种数据驱动的方法来使用 Otsu 的方法、形态学操作、时间序列预测和排放模拟器模型来估计事故的影响，使我们能够量化高级事故检测的好处。所提出的方法在真实场景中进行了评估，表明所研究的事故可能导致额外的能源浪费增加 38%，二氧化碳排放量增加 36%。