Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> DETECTION <s> In this paper we present a comparative study of two approaches for road traffic density estimation. The first approach uses the microscopic parameters which are extracted using both motion detection and tracking methods from a video sequence, and the second approach uses the macroscopic parameters which are directly estimated by analyzing the global motion in the video scene. The extracted parameters are applied to three classifiers, the K Nearest Neighbor (KNN) classifier, the LVQ classifier and the SVM classifier, in order to classify the road traffic in three categories: light, medium and heavy. The methods are compared based on their robustness to the classification of different road traffic states. The goal of this study is to propose an algorithm for road traffic density estimation with a high precision. <s> BIB001 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> DETECTION <s> Increased traffic and hence congestion is a major problem in cities or urban areas. To mitigate this problem of congestion a real-time traffic density estimation model is essential. This research proposes one such model for estimating the traffic congestion level with the help of Vehicular Ad-hoc Network (VANET) and Cloud Computing. In this work, a novel architecture and algorithm has been proposed to estimate the density of vehicles on the road and the average speed. The fuzzy algorithm is then used to get the level of congestion. The algorithms are simulated using Network Simulator 3 (NS3) and Simulation of Urban Mobility (SUMO) to estimate the congestion level in an area of a city. The model proposed is deployed on Cloud and can be made available as Software as a Service (SaaS) in future. <s> BIB002 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> DETECTION <s> The paper presents an idea for a simulation-based traffic management system, which may be especially successful in the era of connected and autonomous vehicles (CAVs). The most important aspect of the system is its ability to evaluate traffic conditions for different traffic control strategies (e.g., different traffic signal settings, different route assignments) using fast traffic simulations and neural networks. It also employs metaheuristics (e.g., genetic algorithms) to find (sub)optimal traffic control strategies. Results of initial experiments show that building such traffic management system might be technically feasible and it may be especially successful in the era of CAVs, for which it may be possible to collect required traffic data and make accurate traffic predictions. <s> BIB003
Vehicular congestion is identified by observing the average speed of the traffic stream and the traffic density metric, which is the number of vehicles per unit area of length such as vehicles/cell and vehicle/km/lane  . The average speed and traffic density related information is used by classifiers to group traffic into various fuzzy sets and is also used to identify congestion levels ranging from free flow, slight to severe BIB002 . Classifiers used to identify congestion levels include Naive Bayes, K-nearest neighbor, learning vector quantization (LVQ) and support vector machine (SWM)  , BIB001 ,  . Driving patterns of motorists are highly unpredictable which depends on multiple parameters and varies for every situation and traffic condition BIB003 . This makes neural networks based machine learning tools ineffective for congestion detection and handling. This is however not true for autonomous vehicles as reaction to external events can be predicted accurately and thus machine learning techniques can be used for scenarios with autonomous vehicles BIB003 . Traffic density is estimated in connected vehicle technology by applying algorithms in the RSU on the preprocessed sensory data collected from the vehicular sensory probes by using the communication channels of the vehicular network in the IoV system. The techniques used in the algorithms to identify congestion by using the traffic density metric include vehicle kinetics analysis, motion detection and tracking, average traffic velocity estimation, road occupancy rate estimation and traffic flow rate estimation  , BIB001 . In this section, various traffic density estimation techniques using connected vehicle technology are studied for identification of vehicular congestion.