Abstract
This paper proposes an improved intelligent driver model (IDM) by considering the information of multiple front and rear vehicles to describe the car-following behaviour of CAVs (Connected and autonomous vehicles). The model involves the velocity and acceleration of multiple front and rear vehicles as well as the velocity difference and headway between the host vehicle and its surrounding vehicles. By introducing location-related parameters, the model quantitatively expresses the change in influence degree of a surrounding vehicle with its location to the host vehicle. To maximize traffic stability, we obtain the optimal value of the parameters in the model and the effect of specific time delays on the stability of traffic flow with numerical simulation. The results indicate that for a single vehicle control, the proposed model provides a much quicker and smoother acceleration and deceleration process to the desired speed than the IDM and multi-front IDM. And for fleet control, the proposed multi-front and rear IDM is superior to the other two models in decreasing the starting and braking time and increasing the stability of speed and acceleration. With effective car-following behaviour control, it is helpful to improve the operation efficiency of CAVs and enhance the stability of traffic flow. In addition to the car-following behaviour control, the model can be utilized for fleet control in the case of CAVs’ homogeneous flow. This model can also serve as an effective tool to simulate car-following behaviour, which is beneficial for road traffic management and infrastructure layout in connected environments.
Highlights
Car-following behaviour is a common micro-driving behaviour and describes the interaction between two adjacent vehicles in a single lane with limited overtaking [1]
The results show that when τf= 0.26,τr= 0.24, Qf = 12 and Qr = 2, the multi-front and rear intelligent driver model (IDM) has the best stability of traffic flow among these three curves, which indicates the optimal value of the corresponding parameters in the model
By adding weights λlf and λlr for the front and rear vehicles, respectively, the model quantitatively expresses the change of their influence degree on the host vehicle with its distance
Summary
Car-following behaviour is a common micro-driving behaviour and describes the interaction between two adjacent vehicles in a single lane with limited overtaking [1]. We will depict the critical stable curves for different models and analyse the influence degree of the front and rear vehicles on the host vehicle and the effect of the number of front and rear vehicles on the stability of the traffic flow under the model. When the considered number of front and rear vehicles increases, the effect of the time delays on the traffic flow stability will gradually accumulate. The results show that when τf= 0.26,τr= 0.24, Qf = 12 and Qr = 2, the multi-front and rear IDM has the best stability of traffic flow among these three curves, which indicates the optimal value of the corresponding parameters in the model.
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call