Research And Implementation Of Traffic Flow Prediction Technology Based On Cellular Automata

Because of the lag of urban transport infrastructure upgrading and public transport development,and the rapid increase in vehicle ownership,urban road traffic congestion in China is increasing.Intelligent Transportation System is a new generation of transportation system developed to improve the operation efficiency of transportation infrastructure,solve traffic congestion,avoid traffic accidents and improve energy and environment closely related to transportation.Prediction of urban road flow is an important basis of ITS.It can provide effective help for individual travel services,joint control of traffic signals between different regions,formulation of specific travel plans,and even analysis of urban road construction planning and management.Therefore,the research and implementation of traffic flow forecasting technology based on cellular automata has important academic significance and application prospects.The main research work of this paper is as follows:(1)Through a lot of research,the theory of traffic flow forecasting and cellular automata are deeply studied.By consulting relevant data,we can analyze the different characteristics of traffic flow under different weather conditions,and classify different weather levels.At the same time,the impact of weather on traffic flow parameters is studied,including acceleration,deceleration,minimum safe distance and so on.(2)Aiming at the technical development demand of traffic flow forecasting,this paper studies traffic flow forecasting based on cellular automata traffic forecasting model and grey theory traffic flow forecasting model respectively.Because traffic flow is affected by many factors,the prediction effect of single prediction model is limited.This paper combines the two prediction models to improve the accuracy of traffic flow prediction.(3)According to the influence of different meteorological conditions on traffic flow prediction,including vehicle status,speed and traffic density,this paper presents a traffic flow prediction model based on cellular automata with meteorological conditions.Firstly,in view of the influence of meteorological conditions,the main parameters of cellular automata are optimized,and then the improved cellular automata prediction model and grey theory GM(1,1)prediction model are combined,and the results show that the combined model has better prediction effect,which can provide more accurate urban traffic flow prediction for users.(4)According to the functional requirements of traffic flow forecasting system,a prototype system of traffic flow forecasting is designed,which includes five functional modules: data collection module,data communication module,data management and processing module,data publishing module and traffic flow statistics and prediction module,the functions of five modules are analyzed,and relevant data tables are designed.The traffic flow forecasting system is preliminarily realized.This paper mainly focuses on traffic flow forecasting.It studies four aspects: traffic flow forecasting based on CA,traffic flow forecasting based on GM(1,1),traffic flow forecasting based on combination of CA and GM(1,1),and traffic flow forecasting based on combination of improved CA and GM(1,1)relied on meteorological conditions.And through a lot of experiments,it is proved that the improved model has better prediction effect.Finally,the model is applied to the design and implementation of the system to provide users with more accurate urban traffic flow prediction.