Abstract
Vehicle-to-everything (V2X) technology will significantly enhance the information perception ability of drivers and assist them in optimizing car-following behavior. Utilizing V2X technology, drivers could obtain motion state information of the front vehicle, non-neighboring front vehicle, and front vehicles in the adjacent lanes (these vehicles are collectively referred to as generalized preceding vehicles in this research). However, understanding of the impact exerted by the above information on car-following behavior and traffic flow is limited. In this paper, a car-following model considering the average velocity of generalized preceding vehicles (GPV) is proposed to explore the impact and then calibrated with the next generation simulation (NGSIM) data utilizing the genetic algorithm. The neutral stability condition of the model is derived via linear stability analysis. Numerical simulation on the starting, braking and disturbance propagation process is implemented to further study features of the established model and traffic flow stability. Research results suggest that the fitting accuracy of the GPV model is 40.497% higher than the full velocity difference (FVD) model. Good agreement between the theoretical analysis and the numerical simulation reveals that motion state information of GPV can stabilize traffic flow of following vehicles and thus alleviate traffic congestion.
Highlights
With the development of urbanization and motorization, the number of vehicles continues to grow, and congestion has become one of the main problems existing in cities around the world
And 5, one can obtain that stable region of the generalized preceding vehicles (GPV) model is larger than that of the full velocity difference (FVD) model. This is because motion state of GPV considered in our model can assist driver with better grasping traffic condition ahead and taking measures in advance to maintain stable state as much as possible, and enhance the stability of traffic flow, which suggests that motion state such as average velocity of GPV plays an important role in enhancing the stability of traffic flow
We believe that in the V2X environment, information of GPV motion state can assist drivers in optimizing car-following behavior and, enhance the stability of traffic flow, which infer that traffic efficiency will be improved and energy consumption will be reduced with V2X technology in intelligent transportation system (ITS)
Summary
With the development of urbanization and motorization, the number of vehicles continues to grow, and congestion has become one of the main problems existing in cities around the world. Li et al [14] proposed an extended car-following model to concurrently study headway, relative velocity and acceleration information of an arbitrary number of vehicles ahead in the current lane. It is noteworthy that the fitting acceleration curve of the FVD model has bigger curvature in several places, and there is a certain delay in acceleration calculation results of the FVD model, especially in the deceleration phase The causation of this phenomenon is that drivers adjust car-following behavior only according to the motion state of their front vehicle in the FVD model and cannot grasp the traffic situation further ahead, which will guide them to take measures in advance and reduce reaction delay. The above results suggest that GPV’s motion state, such as average velocity, plays an important role in improving the performance of the car-following model in fitting data measured in the field
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