# A Microscopic Traffic Flow Model Characterization for Weather Conditions
## 2 Related Work  
[PERSON] [19] developed a microscopic model that characterizes vehicle behavior in dense traffic. In this model, velocity is based on the distance headway, thus a larger headway results in lower density and higher velocity. However, this relationship between velocity and density results in unrealistic acceleration [20]. [PERSON] et al. [21] modified the [PERSON] model by ignoring velocity differences, but this results in unstable traffic flow behavior. Furthermore, deviations from equilibrium velocity lead to the same acceleration, which is not realistic. [PERSON] and [PERSON] [22] proposed a model that considers reaction to velocity differences. This model can accurately characterize traffic during congestion, but the acceleration is unrealistic. [PERSON] [23] developed a model to better characterize acceleration and driver behavior, but it is accurate for only a small range of parameters [20].  
[PERSON], [PERSON], and [PERSON] [20] proposed the Intelligent Driver (ID) model, which is based on driver response. This model characterizes driver behavior by considering the velocity and distance headway of forward vehicles and incorporating traffic parameters [24; 25; 26]. However, the acceleration exponent in the ID model is a constant and not based on traffic dynamics, which results in unrealistic traffic behavior. The ID model was revised in [27] to consider driver intent at junctions. However, at high velocities, the safe distance between vehicles is small, which can lead to accidents in adaptive cruise control systems. An improved safe distance was obtained in [28] using velocity-based parameters.  
[PERSON] et al. [29] employed machine learning to predict the severity of injuries due to traffic accidents. The weather, road surface condition, lighting, vehicle type, and damage were considered as features. [PERSON] et al. [30] identified the factors that affect motorbike accident injury severity using the random parameters logit model. The results obtained indicate that weather is a primary cause of these accidents in developing countries. Weather also influences commuting decisions [31; 32]. Rain, snow, wind, and temperature often result in a switch from open to sheltered modes of transport [33].  
Traffic signal parameters were modified in [PERSON] et al. [34] to improve traffic efficiency and road safety during adverse weather. [PERSON] et al. [35] employed a multi-layer logistic regression classification tree model to characterize the relationship between weather, road surface conditions, and traffic speed. The effect of weather on road safety was also examined in [36; 37]. The influence of weather on traffic flow was examined in [13] using a combination of driving simulation and traffic simulation. A convolutional neural network was employed in [38] using data from a traffic monitoring camera to alert road users of adverse weather conditions.  
In this paper, a microscopic traffic model is proposed that characterizes traffic flow according to the weather conditions. The road surface condition is determined using the weather severity index. This index is low for normal and high for adverse weather conditions. The behavior of the ID and proposed models is evaluated over a circular road of length 800 m. A circular road is employed so that traffic is conserved and does not move off the road. This can be considered a worst-case condition to assess the performance of traffic models. Results are presented that indicate the proposed model realistically characterizes traffic behavior during adverse weather.  
The remainder of this paper is organized as follows. Section 3 presents the ID and proposed models. In Section 4, the stability of these models is evaluated. The performance of both models is evaluated and compared in Section 5, and some concluding remarks are given in Section 6.