# A New Driver Model Based on Driver Response
## 1 Introduction
The use of traffic physics is more appropriate for microscopic traffic characterization. In addition, the distances between vehicles are too small leading to accident conditions. [PERSON] [19] improved the [PERSON] et al. model using regression techniques. However, this resulted in a very complex traffic characterization with 11 variables in the model. [PERSON] and [PERSON] [20] developed a model that incorporated reaction to differences in velocity. Thus, it can accurately model velocity and time headway in congestion, but acceleration and deceleration occur over a very short time. This behaviour is typical of an aggressive driver, so slow and average driver behaviour is ignored. [PERSON] [21] proposed a model with realistic acceleration and deceleration and driver behaviour. However, it is not accurate for a wide range of parameters [15]. Several models are described in Table 1.  
The Intelligent Driver (ID) model developed by [PERSON], [PERSON], and [PERSON] [15] is based on driver response. This model considers the velocity and distance headway of preceding vehicles and incorporates practical traffic parameters [22; 23; 24]. However, the acceleration exponent in this model is a constant and so is not based on traffic physics, which leads to unrealistic behaviour. Traffic flow with the ID model is typically estimated at a maximum speed of 30 m/s and a jamming distance headway at standstill of 7 m. The maximum acceleration is 1 m/s\({}^{2}\) while the maximum deceleration is 1.5 m/s\({}^{2}\)[16]. The acceleration exponent is commonly set to 4 [15] and is the same for all traffic conditions. The ID model has been improved by incorporating driver response based on deceleration at intersections [25]. However, the distances between vehicles are small even with large velocities. Traffic alignment is the adjustment in velocity when a forward change in traffic occurs. Adaptive cruise control is designed to maintain the required distance headway for safe alignment to forward conditions [26], i.e., changes in velocity are small and within a maximum and minimum range. It has been shown that using the ID model in an adaptive cruise control system can result in accidents [27]. Several traffic prediction software programs are available, such as Simulation of Urban Mobility (SUMO) and PTV VISSIM. The SUMO program is a free package that is commonly used for research [28]. It can be combined with the ID model for traffic prediction, but driver behaviour is not included [29]. The PTV VISSIM program is a commercial package that can be used to predict microscopic traffic behaviour [30]. A revised ID model is used which incorporates driver response based on the distance headway between vehicles. However, changes in flow only occur with a significant change in this response [29]. Traffic capacity is the maximum flow for the conditions at a given location [31]. Connected vehicles communicate bidirectionally with the outside world. The prime goal of connectivity is to improve public safety [32]. The PTV VISSIM program has been used to predict freeway capacity and increase the proportion of connected and autonomous vehicles [33]. Traffic signal algorithms have been developed to mitigate traffic congestion [34]. However, the interaction between autonomous and human operated vehicles is difficult to characterize [35]. The advanced interactive microscopic simulator for urban and non-urban networks (AIMSUN) is a modelling package used to simulate traffic models [36]. It encompasses a collection of dynamic modelling tools and includes microscopic and mesoscopic simulators and dynamic traffic assignment models. AIMSUN is employed for offline traffic engineering and real time traffic management decision support. It provides solutions to traffic planning and operational problems [37].