Modeling differential car-following behavior under normal and rainy conditions: a memory-based deep learning method with an attention mechanism


 In order to analyze and learn the difference car-following behavior on normal and rainy days, we first collected car-following trajectory data of an urban elevated road on normal and rainy days by microwave radar and analyzed data's differences in speed, space headway and time headway through statistics. Secondly, due to the time-series characteristics of car-following data, we use the LSTM (Long Short-term Memory) neural network optimized by attention mechanism (AM) and sparrow search algorithm (SSA) to learn the different car-following behaviors under different weather conditions and build corresponding models (ASL-Normal, ASL-Rain), respectively. Finally, the simulation test shows that the MSE (Mean Square Error) and RTTC (Reciprocal of Time-to-Collision) of the ASL model are better than that of LSTM and IDM (Intelligent Diver Model), which is closer to the real data. The ASL model can better learn different driving behaviors on normal and rainy days. However, it has a higher sensitivity to weather conditions from cross test on normal and rainy data-sets requiring classification training or sample diversification processing. In the car-following platoon simulation, the stability performance of two models is excellent, which can describe the basic characteristics of traffic flow on normal and rainy days. Compared with ASL-Rain model, the convergence time of ASL-Normal is shorter, reflecting that cautious driving behavior on rainy days will reduce traffic efficiency to a certain extent. However, ASL-Normal model produced a more severe and frequent traffic oscillation within a shorter period because of aggressive driving behavior on normal days.