# Efficient Traffic Flow Guidance Feedback Strategy Considering Drivers' Disobedience in ITS
## 1 Introduction  
Intelligent transportation systems (ITS) aim to provide efficient and safe services relating to different modes of transport and traffic management and to enable users to be better informed. Moreover, the integration of existing technologies can create new services. Driving behavior is an important factor in ITS. Studies have shown that the cultural and educational backgrounds of people can have a major impact on driving behavior and can play an important role in driving safety in ITS. For instance, there are studies on Chinese drivers' behavior showing that aggressive driving makes a significant contribution to traffic accidents, and statistics show that 84.6% of traffic accidents are caused by driving rules violations [1]. Usually, the driving behavior in ITS is also impacted by the traffic control strategy. To alleviate traffic control issues (e.g., congestion) and enhance the road capacity of existing infrastructure, researchers and practitioners are attracted by vehicular traffic flow problems; many theories have been proposed. For example, a mean velocity route guidance strategy was proposed by [PERSON] et al. [2], in which the largest mean velocity as route preference is considered. Another strategy, the congestion coefficient route guidance strategy, was proposed by [PERSON] et al. [3]. This latter strategy proved to be better than the travel time route guidance strategy and mean velocity route guidance strategy used in [1]. The congestion coefficient route guidance strategy was then popularly employed in [3; 4; 5; 6]. Other strategies were designed, which one can cite, such as the pace flow route guidance strategy proposed by [PERSON] et al. [7] and the vacancy length route guidance strategy, which offers easy operational applications. This last strategy was also suggestedby [PERSON] et al. and is shown to be better than other strategies (e.g., travel time route guidance strategy, congestion coefficient route guidance strategy, space flow route guidance strategy, and time flow route guidance strategy [6]). A strategy based on the number of vehicles was introduced by [PERSON] et al. [8], and its performance was assessed in terms of network efficiency and cost. Only a few studies have focused on studying drivers' behaviors. For instance, some studies, such as [9; 10; 11], have shown that there is no difference in driving behavior between males and females; however, some disparities persist in certain aspects. The differences involve traffic accidents and offenses, although driving times, attitudes, education, and other background factors are controlled. In real-life conditions, the drivers, unlike machines that follow the instructions strictly, have their own wills; it is possible that they will disobey or sometimes do things completely opposite to the instructions given by the central vehicle system. Moreover, the uncertainty of drivers' obedience not only hinders the efficiency of the feedback strategy but also leads to a misjudgment of the system. Thus, the originality of our work resides in the integration of the human factor, which most previous works did not consider. This factor is the obedience rate of drivers. For this purpose, we propose the use of a pre-filtering procedure before giving instructions to drivers. That is to say, the system classifies the drivers into two categories: those who tend to follow the instructions and those who tend not to, and then chooses to give instructions to the drivers who are more likely to obey the system. The contributions of this study are listed hereafter:  
1. A procedure based on machine learning algorithms, namely the \(k\)-means and SVM methods, is used to classify drivers according to their behavior. 2. A new traffic flow guidance algorithm, the dynamic weighted vehicle density feedback strategy (DWVDFS), is proposed. This latter is based on WVDFS [12] suggested by [PERSON] and [PERSON]. In WVDFS, the vehicle velocity is not taken into account when calculating the vehicle density.