CELLULAR AUTOMATA MODEL FOR TRAFFIC FLOW WITH OPTIMISED STOCHASTIC NOISE PARAMETER

Based on the existing safe distance cellular automata model, an improved cellular automata model based on realistic human reactions is proposed in this paper, which aims to reproduce the characteristics of congested traffic flow. In the proposed model, the stochastic noise parameter is optimised by considering driving behavioural difference. The relative speed, gap and acceleration of the front vehicle are introduced into the optimised stochastic noise parameter oriented to describing the asymmetric acceleration behaviour of drivers in congestion. The simulation results show that an uneven distribution of acceleration trajectories of vehicles experiencing congestion exhibited on the spatial-temporal diagram of the proposed model is reproduced. Based on the analysis of the NGSIM, compared with the model with traditional stochastic noise parameter, the vehicles that move ac-cording to the proposed model can be followed more easily and more realistically. Then the actual gap of vehicles can be better reflected by the proposed model and the change of vehicle speed is more stable. Additionally, the traffic efficiency from two aspects of flow and speed shows that the proposed model can significantly improve the traffic efficiency in the medium high density region.