Traffic Flow Guidance Feedback And Early Warning Modeling In Internet Of Vehicles

The introduction of Internet of Vehicles technology will bring new changes to driving conditions of the road traffic.By obtaining the real-time information of driver,vehicle,road and environment state,it provides traffic applications such as prediction for vehicles running track,dynamic route guidance and early warning for traffic safety.Road traffic is a nonlinear system with complex,dynamic and stochastic characteristics.Due to unpredictable disturbance and highly variable physical and human elements,traffic flow time series present strong nonlinear,non-stationary and self-similar characteristics,so it is easy to cause extreme traffic accidents.Considering improving road capacity,easing traffic congestion and ensuring traffic safety,this thesis spreads out exposition on three parts: the complexity and the correlation of traffic flow,guidance feedback strategy of traffic flow,early warning of traffic flow.So the thesis mainly focuses on solving key issues as follows:(1)how to accurately describe and parse coupling complexity and correlation of traffic flow,and to complete the sharp variation detection of the road traffic.(2)how to ensure the accuracy and real-time performance of dynamic route guidance,and to effectively solve the phenomenon of traffic jam.(3)how to effectively carry out active safety warning for vehicles and reduce traffic accidents.Therefore,it is of great significance to improve the scientific management of traffic system and intelligent control,improve the road capacity and ensure traffic safety on theoretical and practical significance.The main purpose of this thesis is for traffic flow guidance feedback and early warning modeling in Internet of Vehicles.First,the complexity and the correlation of traffic flow time series of different density are studied to explore the tendency and volatility of the traffic flow state,and further complete the traffic mutation detection.Second,with speed bottleneck,the symmetric double channel and asymmetric double channel cellular automata traffic flow model are established.A weighted mean space gap feedback strategy(WMSGFS)is also proposed.Finally,a Visual-based Asymmetric Two-lane Cellular Automata model with Abandoned Object(V-ATCA-AO)and an Internet of Vehicles-based Asymmetric Two-lane Cellular Automata model with Abandoned Object(IoV-ATCA-AO)are both proposed and compared.Two types of traffic accidents are analyzed.Accident rates of two models are discussed.It provides references for early warning for lane changing in the Internet of vehicles.Main contributions of this thesis include:(1)The complexity and the correlation of three-phase traffic flow time series of different density.Micro time headway and velocity taken as the research object,multivariate multiscale entropy(MMSE)and multifractal detrended cross-correlation analysis(MF-DCCA)are both used.MMSE is used to reflect the coupling complexity of traffic flow time series of evolution.MMSE results show that MMSE trend of different density is similar.The complexity has increased under low scales meanwhile the complexity has reduced under high scales.The conclusion reflects that it is hard for traffic flow prediction in the short-term,and easy for traffic flow prediction in the long-term.Meanwhile,MF-DCCA is used to reflect the tendency of the traffic flow state and fluctuations,from the global and local cross-correlation.Considering the Hurst exponent changing with different scale,MF-DCCA can not only analyze the local cross-correlation of different time intervals,but also effectively distinguish the influence of local cross-correlation and multifractal characteristics between multiscale intervals of different density.And further through differences between the global generalized auto-correlation Hurst exponent and global generalized cross-correlation Hurst exponent,the reinforcing or weakening change has been analyzed between connected behavior and auto-correlation behavior.Experimental results verify the validity and feasibility of the methods.Finally,MF-DCCA is used to identify the fractal scale-less range to complete the mutation detection of the road traffic.(2)Guidance feedback strategy of traffic flow in the Internet of Vehicles is studied.The purpose is to improve road traffic capacity,to help drivers choose rational route,to realize dynamic balance of the traffic flow and finally to effectively solve the phenomenon of traffic jam.Considering the highway maintenance section and the speed limit range,with speed bottleneck,the symmetric double channel and asymmetric double channel cellular automata traffic flow model are established.A weighted mean space gap feedback strategy is also proposed.The influence of different proportion of aggressive driving,different road length ratio and different proportion of dynamic car are also studied.In Internet of Vehicles,WMSGFS proposed not only considers all the vehicle's dynamic effect of average gap on every channel at each time step,but also adjusts the corresponding weights to reflect the road congestion level,according to different vehicle distance.Results show that compared with other strategies,the weighted mean space gap feedback strategy can effectively improve the traffic conditions with a good stability and balance,and promote the average flow across the road.(3)An early warning modeling of an abandoned object on the highway to effectively reduce the occurrence of traffic accidents.Considering the influence of an abandoned object on driving behavior,a Visual-based Asymmetric Two-lane Cellular Automata model with Abandoned Object(V-ATCA-AO)and an Internet of Vehicles-based Asymmetric Two-lane Cellular Automata model with Abandoned Object(IoV-ATCA-AO)are both proposed.Based on these two models,two types of traffic accidents caused by an abandoned object are analyzed: rear-end collision caused by the abandoned object ahead(type I)and collision of the vehicle with the abandoned object(type II).Through spatiotemporal characteristics of traffic flow by simulation,accident rates of two models are discussed.Finally,the effect of Internet of Vehicles-based warning threshold with abandoned object on the accident rate is analyzed,and the appropriate warning threshold is also given in different densities to provide the model support for early warning for lane changing in the Internet of vehicles.Further,the relationship between fractal characteristics of traffic flow and the probability of traffic accidents is also explored.Therefore,it improves the fractal self-similarity study of traffic accident rates in the Internet of vehicles.