# Efficient Traffic Flow Guidance Feedback Strategy Considering Drivers' Disobedience in ITS
## 2 Related Literature  
In this section, we summarize the studies related to our work as follows: The first part is dedicated to the contributions considering driving behaviors; the second one focuses on the weighted vehicle density feedback strategy proposed in [12].  
1. Driving behavior Some studies claim that culture and people's traditions have a major impact on how drivers control their cars and driving safety. Studies on Chinese drivers' behavior have shown that aggressive driving contributes significantly to traffic accidents, i.e., around 84.6% of car accidents are caused by traffic violations [1]. This latter study proved that bad driving habits, a meager understanding of other road users, and a lack of respect for traffic regulations are reasons that contribute to traffic accidents.  
Some studies pointed out that a system considered useful in one country can be seen as almost worthless (or even harmful) in another, and system settings feasible in one part of the world may not be suitable on the other side of the world [6].  
Although some works dealing with driver behavior were published recently, there has been little attempt to understand and take into account driver behaviors and goals when designing products for a global ITS. We strongly believe that the key to an effective design is an understanding of how needs/requirements vary among several drivers. This may be particularly appropriate for ADAS (advanced driving assistance systems) [6] since not only the rules of the road but also social situations, norms, and driver behavior may differ considerably from state to state in terms of the behaviors of drivers (various cultures of driving). The authors in [13] conducted an experiment to determine anxiety status and driving behavior based on heart rate and concluded that anxiety is accompanied by increased heart rate and cautious driving.  
* Traffic Flow Guidance Feedback Strategy WVDFS  
In this sub-section, we survey the weighted vehicle density feedback strategy proposed in [12]. At each time, vehicles on the road upload their positions to the traffic control center. Based on the uploaded information, the traffic control center could compute the weighted vehicle density of each route and display it on the board at the entrance of the route. The newly arrived dynamic vehicle would choose the route with a smaller weighted vehicle density. The weighted vehicle density is defined as:  
\[\rho_{w}=\sum_{i=1}^{N}\frac{\left(k\times\frac{n_{i}}{L}+b\right)}{L} \tag{1}\]  
where \(L\) is the length of each road, \(N\) is the total number of vehicles on the route at that moment, \(n_{i}\) is the position of \(i\)th vehicle, \(k\) is the weight factor and \(b\) is a constant, where \(k=-2.0\) and \(b=2.0\).  
Our paper only considers the stationary density of a vehicle on the route; it does not reflect the real congestion situation on the routes. For example, take a scenario on the expressway where vehicles are crowded and are close to each other, but they are all moving at a high speed. In this scenario, this route cannot be defined as a route with severe congestion (as a driver drives more carefully when there are many vehicles around her/him). Applying the WVDFS strategy in this context may result in severe congestion. This might lead to a misunderstanding of the system and the actual situation in the driving environment.