AbstractAs a typical type of adverse weather, heavy rainfall can result in congestion and productivity loss in the urban transportation system, which has been a severe problem for cities and attracted much attention from scholars. However, little attention has been paid to the effects of the combination of rainfall and rain-induced waterlogging on urban road traffic. In this paper, first, an experiment based on a high-fidelity driving simulator is conducted to collect the drivers’ behavior data in the scenario of rainfall and waterlogging where 30 groups of scenarios with different levels of rainfall intensity and water depth are included. Second, different car-following models are calibrated in the light of the drivers’ behavior database from the experiment, and the best one is chosen to simulate the traffic flow on urban road sections. Lastly, different traffic flow models that depict the relationship between flow, velocity, and density are calibrated based on the simulation results. The results show that the increase of both the rainfall intensity and water depth can result in decreased free flow speed and maximum flow. The results also show that the depth of water is more critical as an influencing factor on the characteristics of traffic flow compared with the rainfall intensity. The results from this study can facilitate a deeper understanding of the influence of rainfall and waterlogging on traffic flow and improve the response capacity of traffic management departments when faced with rainfall and waterlogging.