A Method for Urban Road Waterlogging Recognition Based on Fundamental Diagram

In many developing cities with impropriate urban planning strategy and poor drainage system always suffer waterlogging problem during heavy rains. Waterlogging, as a well-known urgent problem for urban development, will increase risk of traffic accidents, cause malfunction of transportation system, and seriously threaten people lives. However, those harm can be effectively reduced by giving an early warning to citizens after detecting the water accumulation. Some existing methods based on detective sensor are proved to be expensive and low precision. Other methods try to apply macro simulation model to predict result of waterlogging, but complicated mathematical modeling, as well as getting access to geographic information and related data sources, is hard to implement. Therefore, this paper proposes an effective data-based approach for waterlogging recognition built on fundamental diagram (FD) of traffic flow. The methodology implements a real-time intelligent recognition on waterlogging points by observing shift on FD and calculating the probability of waterlogging. Compared to existing methods, it has the better applicability on entire urban road network, the easier realizability on application implementation, and more independent on data requirement. A study case of Shenzhen (a city in southern China) waterlogging indicates the methodology can effectively provide a real-time recognition on road segments prone to water accumulation during rainstorm, and the accuracy is proved to be better than any other similar approaches.