Narrow Results

In this paper, the car following model is investigated by considering the vehicle dynamics in a cyber physical view. In fact, that driving is a typical cyber physical process which couples the cyber aspect of the vehicles' information and driving decision tightly with the dynamics and physics of the vehicles and traffic environment. However, the influence from the physical (vehicle) view was been ignored in the previous car following models. In order to describe the car following behavior more reasonably in real traffic, a new car following model by considering vehicle dynamics (for short, D-CFM) is proposed. In this paper, we take the full velocity difference (FVD) car following model as a case. The stability condition is given on the base of the control theory. The analytical method and numerical simulation results show that the new models can describe the evolution of traffic congestion. The simulations also show vehicles with a more actual acceleration of starting process than early models.

The paper proposes a car following model from the perspective of visual imaging (VIM), where the visual imaging size of the preceding vehicle on a driver's retina is considered as the stimuli and determines the driving behaviors. NGSIM trajectory data are applied to calibrate and validate the VIM under two scenarios, i.e. following the car and following the truck, whose fitting performance outperforms that of visual angle car following model (VAM). Through linear stability analyses for VIM, it can be drawn that the asymmetry in traffic flow is preserved; the larger vehicle width, vehicle length and vehicle apparent size all benefit enlarging the traffic flow stable region; the traffic flow unstable region when following the car tends to fall in the relatively small distance headway range compared with that when following the truck. After that, numerical experiments demonstrate that the visual imaging information applied in VIM is more contributive to the traffic flow stability than the visual angle information in VAM when following the truck in the relatively large distance headway or involving the driver's perception threshold, i.e. Weber ratio; introducing Weber ratio would break the originally stable traffic flow or deteriorate the traffic fluctuation, which however can be alleviated by increasing drivers' sensitivity, e.g., decreasing Weber ratio. Finally, VIM is verified to be able to satisfy the consistency criteria well from the theoretical aspect.

Based on velocity-difference-separation model, the mixed traffic flow on two-lane road is investigated. For a fixed road length, the influence of bus and bus stops on traffic flow is studied with the increasing traffic density. Compared with the result without bus stops given by Li et al., a new traffic state is found, which is valuable for studying the impacts of public transport on urban traffic flow.