Abstract

The application of vehicle-to-everything (V2X) technology has resulted in the traffic environment being different from how it was in the past. In the V2X environment, the information perception ability of the driver–vehicle unit is greatly enhanced. With V2X technology, the driver–vehicle unit can obtain a massive amount of traffic information and is able to form a connection and interaction relationship between multiple vehicles and themselves. In the traditional car-following models, only the dual-vehicle interaction relationship between the object vehicle and its preceding vehicle was considered, making these models unable to be employed to describe the car-following behavior in the V2X environment. As one of the core components of traffic flow theory, research on car-following behavior needs to be further developed. First, the development process of the traditional car-following models is briefly reviewed. Second, previous research on the impacts of V2X technology, car-following models in the V2X environment, and the applications of these models, such as the calibration of the model parameters, the analysis of traffic flow characteristics, and the methods that are used to estimate a vehicle’s energy consumption and emissions, are comprehensively reviewed. Finally, the achievements and shortcomings of these studies along with trends that require further exploration are discussed. The results that were determined here can provide a reference for the further development of traffic flow theory, personalized advanced driving assistance systems, and anthropopathic autonomous-driving vehicles.

Highlights

  • Traffic accidents and congestion are common problems for both those who manage and use transportation systems

  • Like Yang’s approach, Zeng et al further introduced the relative velocity into the BLFVD model and formed a new extended model [60]. They discussed the impacts of the following vehicle information on traffic flow, and the results show that the headway information or the relative velocity information along with the headway can effectively enhance the stability of traffic flow

  • The results reveal that the characteristics of car-following behavior are affected by various sources of information and consider the information applying characteristics, and can provide an important reference for further updating traffic flow theory, the planning/designing/constructing/managing of the transportation system, and developing next-generation vehicles equipped with personalized Advanced Driving Assistance System (ADAS) or quasi-human automatic controller

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Summary

Introduction

Traffic accidents and congestion are common problems for both those who manage and use transportation systems. Further developments in technology will be able to effectively improve the poor state of the present situation. Relevant intelligent transportation system (ITS) technologies that are represented by the V2X have been developing rapidly in the last few years. V2X technologies enable a vehicle to exchange information with the other elements that are involved in the system, providing the basis for intellectualization. These technologies have been regarded as being an effective way to solve problems such as traffic accidents, congestion and pollution. The informatization level of transportation systems has been greatly improved with the application of

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