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In a multi-lane area, the increasing randomness of lane changes contributes to traffic insecurity and local traffic flow instability. A study on safe lane shifting activity that focuses on threat assessment under real-time knowledge is necessary to enhance smooth vehicle flow. This paper proposed a more comprehensive lane changing guidance rule to investigate the status of surrounding vehicles to accommodate future vehicle-on-road collaborative environments based on these parameters 1) lane change demand and 2) treat assessment function. The collaborative relationships between vehicles are analyzed using a cellular automata model based on their location, velocity, and acceleration. We analyze and examine the relationship between the number of lanes and traffic flow when the road capacity is heavily mined via intelligent lane changing. Our analysis can further provide theoretical guidance for the selection of road expansion mode. Our proposed STCA-L is compared based on the average speed, average flow, lane changing frequency, spatial and temporal pattern of STCA, STCA-I, and STCA-S, and STCA-M under different vehicle densities. The numerical simulation results show that our proposed STCA-L provides the most flexible lane changing guidance in the multi-lanes road. Moreover, the simulated results show that the exponential growth of physical space cannot provide the corresponding increase in the average flow of vehicles.

Improved STCA Model for Multi-Lane Using Driving Guidance under CVIS