Research on Freeway Traffic Flow Evolution and Cloud Control System Based on Safe Following Distance and Cellular Automata

Abstract

With the increasing traffic demand, the trade-off between freeway traffic flow and driving safety has become increasingly prominent. Traditional right-driving rules often fail to balance driving safety and road network efficiency under varying traffic densities. This paper aims to deeply explore the impact mechanism of freeway driving rules on the spatio-temporal evolution of traffic flow. First, by introducing the concept of safe following distance, a vehicle anti-collision model under different driving states and overtaking behaviors is constructed to quantify the safety risks in microscopic driving behaviors. Second, a Cellular Automaton (CA) traffic flow simulation model is established to dynamically simulate and reveal the traffic flow evolution patterns and their critical density characteristics under different speed limits and mixed vehicle compositions. The research indicates that under heavy traffic conditions, a single right-driving rule significantly restricts the overall traffic efficiency of the road, while implementing a scientific lane-based speed limit strategy can effectively delay the spread of congestion. To further break the limitations of existing static rules, this paper proposes an intelligent traffic management and control system framework integrating cloud computing and big data. This system utilizes Kalman filtering and Wavelet Neural Network (WNN) for accurate short-term traffic flow prediction, and combines the information interaction between the vehicle private cloud and the platform public cloud to achieve dynamic coordination of individual vehicle behavior and optimal control of global traffic flow. The research results of this paper provide a solid theoretical basis and decision support for future dynamic lane design, speed limit strategy optimization, and the development of intelligent transportation systems on freeways.