## 1 Introduction  
Due to recent fast urbanization, many large cities around the world are suffering from an unprecedented rise in traffic congestion. According to a recent urban transportation report, the economic loss caused by traffic congestion in the U.S. in terms of both fuel consumption and travel time delay was estimated at $121 billion in 2011 and is expected to reach $199 billion in 2020 [1]. Not to mention highly populated countries like China and India. Without a doubt, traffic congestion is one of the most prominent problems that scientific and industrial communities are trying to solve today. In the literature of Intelligent Transportation Systems (ITS), the problem of finding the optimal path for a given vehicle is still under investigation, many congestion avoidance and traffic optimization systems have been proposed but most of them suffer from several drawbacks. Google Maps, Baidu Maps, and TomTom are the leading industrial companies in real-time traffic optimization. They provide real-time traffic conditions and route planning services, based on Global Positioning System (GPS), they measure the vehicle's speed, and eventually detect the congestion if a group of adjacent vehicles has very low speed at a specific road. This approach suffers from two drawbacks, firstly, it cannot prevent the congestion before it happens. Secondly, it cannot make a quick response to emergency cases such as traffic accidents. The current industrial solutions for GPS navigation are divided into two groups, either dedicated navigators (e.g., TomTom [2] and uNAVI [3]) or smartphone based navigator (e.g., Waze [4] and Skobbler [5]). Most of them are using the shortest travel delay path algorithm based on road traffic statistics or real-time road traffic measurement. There are two major drawbacks of using such systems, (1) the routes that they provide are individually optimized paths for each vehicle. That is, they do not consider the collaboration among vehicles in congested road areas for better navigation; (2) the congestion is detected based on vehicles' speed in different road segments. A road segment is considered as congested if the mean speed of the passing vehicles has been reduced to an abnormal level. In other words, these systems are detecting the congestion rather than preventing it from happening in the first place. Alternatively, the emerging Vehicular Ad-hoc Networks (VANET), that support both, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) wireless communication, can be used in many traffic related applications. Such as traffic congestion estimation [6], autonomous car parking system [7], traffic flow prediction [8], CO\({}_{2}\) emissions reduction [9], emergency situations management [10; 11] and route discovery [12]. However, deploying a new infrastructure, such as Roadside Units (RSU), in a big city like Beijing for instance, could be a quite costly task. In addition to the high cost, in case of infrastructure failure due to natural disaster, say an earthquake for instance, the vehicles must be able to coordinate the evacuation of critical areas in a fast and orderly way, this requires the ability to effectively communicate with each other without the need to rely on any external infrastructure. Motivated by the above-mentioned reasons, in this paper we proposed a new distributed infrastructure-less congestion prediction and traffic optimization system for VANET in urban environment named DIFTOS. The city map is divided into a hierarchy of servers. The vehicles that are located in the busy road intersections form a virtual vehicular server (VVS), thus DIFTOS does not rely on any centralized server and does not need cellular network connectivity, RSU or any kind of infrastructure. The contributions of this work are summarized as follows:  
* DIFTOS is completely infrastructure-less and does not rely on any other network (cellular network, RSU), and it is totally distributed and does not rely on a centralized server.