The research on car-following behavior cannot ignore the specific environment in which the object vehicle is located. There are differences in the car-following behavior when the vehicle is in various kinds of environments. In the traditional car-following models, the environmental factors are assumed to be ideal. To be specific, the road and weather conditions are assumed to be consistently good, and slope, curvature, or snow do not exist. These unrealistic assumptions lead to those models showing poor performance when used to describe the car-following behavior in realistic, complex traffic scenarios.
2.3.1. Road
Road condition
Different from the traffic conditions that indicate traffic congestion on the road, road conditions are the technical conditions of the main body, surface, structure, and accessories of the road. In traffic flow theory, a good road condition is regarded as the normal road condition. According to driving experiences, when the road condition deviates from the normal condition, the car-following behavior will be affected and show different characteristics. Therefore, in the research on car-following behavior, the road condition refers to the damage to road surface or other components.
There are impacts of road condition on traffic flow at both the micro and macro levels.
- (1)
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Micro level. The vehicle’s acceleration/deceleration/velocity/headway/energy consumption/exhaust emissions in the starting, driving, and braking process are all affected by the road conditions. Specifically, the lasting time will enlarge, and the velocity along with acceleration/deceleration will decline in the starting and braking process. There will be a disturbance in the velocity and headway in the driving process, which will cause an increase of energy consumption and exhaust emissions.
- (2)
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Macro level. The stability of traffic flow will be enhanced, and the shock wave will be alleviated when the road condition is good. It is noteworthy that there are negative impacts of good road condition on stability when the traffic flow is evaluated for the stop-and-go state.
Slope
On a road with slope, there will be a tendency for the vehicle to move towards a lower position due to gravity, and the driver will take measures to counteract this tendency to maintain a safe and desired driving state. To be specific, the vehicle needs to output more power to reach the same acceleration when going uphill than on a flat road, and the vehicle needs to output more brake force to reach the same deceleration when going downhill. These impacts of gravity will also make the driver correspondingly adjust the headway in the car-following process. Li et al.
[30] first analyzed the maximum velocity and safety headway when car-following on roads with different slopes. In this work, Li et al. summarized a general expression of the optimal velocity function to describe the relationship between the optimal velocity function and position, slope, and safety headway. Based on this, Li et al.
[31] proposed an extended OV model and analyzed the traffic flow utilizing numerical simulation. Different from the approaches that Li used to form the general expression of the optimal velocity function by analyzing the driver’s behavior characteristics, Komada et al.
[32] proposed an extended OV model based on the force analysis of vehicles on roads with slope.
The slope OV model has a similar structure to the basic OV model and two optimal velocity functions, which are suitable for the uphill and downhill. Based on this slope OV model, Komada et al. analyzed traffic flow with the help of numerical simulation and detected the congestion position on various slopes by adjusting the traffic flow density. However, theoretical analysis of the traffic flow on roads with slope is still absent. Aiming at this, Zhu and Yu
[33] derived the neutral stability condition and the nonlinear characteristics near the critical point of the traffic flow based on Komada’s model. During the same period, Zhu and Yu
[34] derived the Korteweg-de-Vries (KdV) equation and the solitary solutions in the metastable region based on Komada’s model. Soon after, Zhu
[35] combined Komada’s model and the energy consumption and exhaust emission model proposed by Li et al.
[31] to construct an energy consumption and exhaust emission estimation model for vehicles on roads with slope. Based on
[33] and the energy consumption model for electric vehicles
[36], Yang et al.
[37] proposed an improved energy consumption model with consideration of the impacts of slopes and the kinetic energy recovery system. Two nondimensional parameters were introduced, which represent the impacts of fog on a driver’s misjudgment of the headway and the corresponding reduction of velocity, by Tan et al. into Komada’s model to form an extended model and to analyze car-following behavior as affected by the fog and slope
[38]. Based on
[33], Zhang et al.
[39] further considered the two relative velocities (forward and backward), constructed an extended slope OV model, and derived the corresponding macro flow model.
Curve
The curve refers to the section with a curvature on the road. When the vehicle is driving on a curve, on the one hand, the driver needs to adjust the direction to control the vehicle along the road curve; on the other hand, the velocity cannot be high due to the limitation of centrifugal force. The above-mentioned two points lead to the fact that the driving characteristics of vehicles on curves are different from those on straight roads.
Gyroidal road
The gyroidal road is a section with both slope and curvature. The curve and slope of roads in the actual traffic system are not independent of each other, and quite a number of roads are both curved and sloped. A typical gyroidal road is a ramp to elevated roads. However, there is no consideration of the gyroidal road, that is, the curve and slope are not considered at the same time. To address this, Zhu et al.
[40] introduced the maximum angular velocity of the gyroidal road, velocity correction due to gradient, and the safety headway affected by slope to modify the optimal velocity function and, based on this, proposed an extended gyroidal OV model. The impacts of the gyroidal road were incorporated into the FVD model by Meng et al.
[41], and they derived the stability conditions of traffic flow utilizing control theory. Considering that the
H∞ norm can describe the traffic congestion with open boundary conditions and the OV model
[42][43], Zhai et al.
[44] proposed a delay feedback control method based on the extended gyroidal OV model constructed in
[40] and discussed the impacts of controller gain coefficient and delay time on traffic flow on gyroidal roads under the Hulwitz criterion.
2.3.2. Weather
In addition to the road conditions, there are significant impacts of weather on car-following behavior. Good weather is generally regarded as normal weather in the research on car-following behavior. When the weather gets worse, it will increasingly affect the car-following behavior. The impacts of bad weather on driving behavior are significant and widely acknowledged. Because of this, traffic managers around the world will send alerts to drivers when they detect bad weather. The previous norm organized weather according to type, such as rain, snow, and fog. In fact, no matter what type of weather, its impacts on driving behavior can be divided into two aspects: visibility and adhesion. Compared with good weather, the presence of liquid and solid particles in the air in the rain, snow, fog, and other weather will lead to the decline of visibility, which will affect the driver’s perception of traffic conditions and then affect his/her car-following and other driving behaviors.
3. Conclusions
There are differences in the car-following behavior when the vehicle is in various driver-vehicle-environment aggregations, which suggests that it is difficult to use one model to comprehensively and precisely describe the car-following behavior of a vehicle with enhanced information perception ability. Generally speaking, (i) the reality that the car-following behavior is comprehensively affected by various driver-vehicle-environment factors has not been adequately considered, and (ii) the processing approaches of impacts of driver, vehicle, or environment on car-following behaviors were relatively simple in previous studies. Therefore, the comprehensive consideration of driver, vehicle, and environmental factors from a global perspective, fully incorporating the characteristics of various factors’ influence, the evolution of modeling and evaluation methods, and the construction of the new generation datasets are the more urgent needs for future works.