Modeling And Analysis Of The Car-following Behavior Considering The Following Vehicle

With the development of economy and the reduction of the manufacturing cost, the number of vehicles increases very fast. However, the increase of roadways cannot catch up the increase of vehicles, that is to say, there is an imbalance of roadway supply and demand. This problem is much more serious in the developing countries like China. In addition, a large number of vehicles also cause a lot of environmental pollution problems and traffic accidents. To solve these problems, a new emerging technology, ITS (Intelligent Transportation System), has attracted much attention and been developing rapidly in the past decades. As a critical technology in ITS, CVT (Connected Vehicle Technology) is gradually changing driving environment in the whole world. In CVT environment, the communications among vehicles, between vehicles and traffic facilities, and among traffic facilities are greatly enhanced, which reveals that drivers can receive much more external stimulation from other vehicles and facilities, and the drivers’ behavior will be influenced hugely.This paper focuses on the influence of the new vehicle connected technology on the car-following behavior and tries to prepare the car-following theory for the coming change of the traffic environment. It is intuitive that drivers can receive much more information from the following vehicle in the new traffic environment, and drivers’behavior will be definitely impacted by this change. Thus, this paper attempts to model the car-following behavior considering the following vehicle and analyzes the characteristics of the car-following behavior considering the following vehicle based on the proposed model. On the other hand, along with the application of the automatic control theory in transportation field, especially the ACC (Adaptive Cruise Control) technology, a lot of scholars have been searching for the more reasonable vehicle control mechanism, and the new proposed car-following model may provide ACC a possible better control mechanism.In study on the car-following model, it is found that the existing car-following model calibration method has some problems, such as producing impractical traffic phenomenon and high prediction error. Therefore, the existing calibration method needs to be improved before applied to calibrate the car-following model considering the following vehicle. Two methods are introduced to improve the existing calibration method in this paper. The first one is the punishment method, aiming to punish the abnormal traffic phenomena (including excessive acceleration and deceleration, exceeding the speed limit, and frequent crashes) in car-following simulation. The second one is the error weighted method, which is mainly to against the problem that the simulation error accumulates with the increase of the simulation iteration number. These two methods are evaluated using the five the car-following models, GM, Bando, Gipps, FRESIM and IDM, based on NGSIM (Next Generation Simulation) data. This paper proposes a framework the car-following model considering the following vehicle; the most existing car-following models can be fit into this framework. On the basis of the modeling framework, Chandler, GM, Bando, Jiang, Gipps and NETSIM models are transformed from the existing form to a form including the following vehicle. The six new models are calibrated using the improved calibration method based on NGSIM data and analyzed for the different contributions of the forward control in the car-following behavior considering the following vehicle. Stability as a critical property of the car-following model is also explored using the theoretical analysis method and the pseudo ring-road simulation method. The stability of Gipps model is studied using the theoretical analysis method, while the stability of RV (Relative Velocity) and OV (Optimal Velocity) models are examined using the pseudo ring-road simulation method.The following conclusions are drawn:1. the two improvement methods of the car-following model calibration are verified to be effective by simulations;2. Different car-following models may have different optimal objective functions in calibration; however, when the optimal objective function of a model is not clear, the weighted MAE (Mean Absolute Error) with the position as the variable will be a better objective function choice which can obtain relatively high performance for most car-following models. The weighted Theil’s U function with the velocity as the variable will be the objective function that can effectively control the simulation error accumulation in most models;3. Taking into account the following vehicle in car-following models can better describe the car-following behavior in reality;4. In the existing traffic environment, the forward control contribution is generally more than80%in the entire control task, and the backward control generally has very small proportion. Most models display the characteristic that drivers are often forced to accelerate in the car-following behavior considering the following vehicle;5. With the increase of the backward control contribution, the forward and backward acceleration distributions of some proposed models becomes unreasonable, revealing that the high backward control contribution will fail car-following behavior, so the backward control contribution should be bound in a reasonable range in the vehicle control system;6. The general stability condition of the car-following model considering the following vehicle is verified to be correct, but it is only applicable to the models in which the decision variable is acceleration and the reaction time is not included, and the formula is not very complicated;7. The Gipps car-following model considering the following vehicle displays that the backward control has three kinds of effect on traffic flow:stabilizing, destabilizing, producing non-physical phenomena, which enriches the existing results of the stability of the car-following model considering the following vehicle;8. The pseudo ring-road simulation results show that free flow can evolve into a severe traffic congestion in the stability testing, and the congested traffic flow may also evolve into a free flow accompanied by a minor traffic fluctuation;9. Generally speaking, it is not necessary to consider the influence of the following vehicle on the car-following behavior in free flow, and the backward control contribution in the total decision may need to be limited in a reasonable range to avoid the failure of the traffic cooperation and control.