Abstract
In intelligent transportation systems, vehicles can obtain more information, and the interactivity between vehicles can be improved. Therefore, it is necessary to study car-following behavior during the introduction of intelligent traffic information technology. To study the impacts of drivers’ characteristics on the dynamic characteristics of car-following behavior in a vehicle-to-vehicle (V2V) communication environment, we first analyzed the relationship between drivers’ characteristics and the following car’s optimal velocity using vehicle trajectory data via the grey relational analysis method and then presented a new optimal velocity function (OVF). The boundary conditions of the new OVF were analyzed theoretically, and the results showed that the new OVF can better describe drivers’ characteristics than the traditional OVF. Subsequently, we proposed an extended car-following model by combining V2V communication based on the new OVF and previous car-following models. Finally, numerical simulations were carried out to explore the effect of drivers’ characteristics on car-following behavior and fuel economy of vehicles, and the results indicated that the proposed model can improve vehicles’ mobility, safety, fuel consumption, and emissions in different traffic scenarios. In conclusion, the performance of traffic flow was improved by taking drivers’ characteristics into account under the V2V communication situation for car-following theory.
Highlights
In recent years, the rapid promotion of the modern urban motorization process has led to traffic problems including traffic congestion, traffic accidents, air pollution, and energy consumption
The space headway from the immediately preceding vehicle and the average speed of the preceding vehicle were taken into account in the car-following model, and the results showed that these models could improve the stability of the traffic flow [28,29]
To explore the braking effect of the reinforcement car-following (RCF) model compared to the full velocity difference (FVD) model in consideration of drivers’ characteristics in the V2V communication environment, we supposed that 11 vehicles were running with the same space headway of 15 m and that each vehicle’s velocity was the optimal velocity (V(15) = 4.67 m/s, as can be computed by Equation (1))
Summary
The rapid promotion of the modern urban motorization process has led to traffic problems including traffic congestion, traffic accidents, air pollution, and energy consumption. To explore and understand emerging traffic problems, scholars have proposed different traffic flow models from macro and micro perspectives. The car-following model can describe complex microscopic traffic flow phenomena such as traffic congestion, stop-and-go waves, and traffic phase transitions. By reviewing the literature on the optimal velocity car-following model, it can be found that drivers’ characteristics are rarely considered in the optimal velocity function (OVF), that is, the optimal velocity depends on the space headway and on the driver’s response to the speed of the preceding vehicle. This paper proposed an extended car-following model that considered drivers’ characteristics in a vehicle-to-vehicle (V2V) communication environment.
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