Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> A. Estimation using Statistical Methods <s> In urban areas, traffic congestion has become a common phenomenon resulting in a great waste of time and fuel. Thus, the real-time detection for road condition becomes a significant challenge for both scientists and engineers. Recently, the technology of vehicular ad-hoc networks (VANETs) has been utilized to alleviate this problem, and becomes an important component of the intelligent transportation system (ITS). In this paper, we propose a novel detection scheme for urban traffic condition only by volunteer probes. The travel time for detected road segment is used to evaluate the traffic condition. To reduce the influence of individuals, a T Window Algorithm (TWA) is designed for calculating the average travel time of probes. For performance optimization, three kinds of Adaptive T Window Algorithm (ATWA) are put forward to resize window according to the probe density. Besides, on analysis of travel time composition, a congestion line is built to identify the traffic status of road segment. Finally, all the approaches are validated in a realistic scenario. The simulation results show that our scheme can detect urban traffic condition effectively. <s> BIB001 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> A. Estimation using Statistical Methods <s> Over the last few years, vehicular ad hoc networks (VANETs) have gained popularity for their interesting applications. To make efficient routing decisions, VANET routing protocols require road traffic density information for which they use density estimation schemes. This paper presents a distributed mechanism for road vehicular density estimation that considers multiple road factors, such as road length and junctions. Extensive simulations are carried out to analyze the effectiveness of the proposed technique. Simulation results suggested that, the proposed technique is more accurate compared to the existing technique. Moreover, it facilitate VANET routing protocols to increase packet delivery ratio and reduce end-to-end delay. <s> BIB002 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> A. Estimation using Statistical Methods <s> Wireless technologies are making the development of new applications and services in vehicular environments possible since they enable mobile communication between vehicles (V2V), as well as communication between vehicles and infrastructure nodes (V2I). Usually, V2V communications are dedicated to the transmission of small messages mainly focused on improving traffic safety. Instead, V2I communications allow users to access the Internet and benefit from higher level applications. The combination of both V2V and V2I, known as V2X communications, can increase the benefits even further, thereby making intelligent transportation systems (ITS) a reality. In this paper, we introduce V2X-d, a novel architecture specially designed to estimate traffic density on the road. In particular, V2X-d exploits the combination of V2V and V2I communications. Our approach is based on the information gathered by sensors (i.e., vehicles and road side units (RSUs)) and the characteristics of the roadmap topology to accurately make an estimation of the instant vehicle density. The combination of both mechanisms improves the accuracy and coverage area of the data gathered, while increasing the robustness and fault tolerance of the overall approach, e.g., using the information offered by V2V communications to provide additional density information in areas where RSUs are scarce or malfunctioning. By using our collaborative sensing scheme, future ITS solutions will be able to establish adequate dissemination protocols or to apply more efficient traffic congestion reduction policies, since they will be aware of the instantaneous density of vehicles. <s> BIB003 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> A. Estimation using Statistical Methods <s> Detecting congestion in urban areas is critical and creates a myriad of complications. Intelligent Transportation Systems (ITS), which are trending in recent years, are used by researchers to engage problems related to congestion and transportation. However, due to the open access in urban area structures, it is less feasible to handle rife data that is generated from vehicles and infrastructure. On the grounds, ITS demands a reliable methodology that uses the data's effectively to detect the congestion. In this paper, we present a novel congestion estimation model for urban areas that leads to predict the congestion propagation. It uses a histogram-based model on a window time basis to make the data transfer substantially minimum and keep the system robust. Due to its simplicity, it can be practically implemented in real time for any nature of roadways. Simulation results, with different scenarios, show that the proposed model is detecting the congestion estimation effectively and leads to predict the congestion propagation for the near future. <s> BIB004 </s> Fog Computing for Detecting Vehicular Congestion, An Internet of Vehicles based Approach: A review <s> A. Estimation using Statistical Methods <s> In urban areas, traffic congestion is a challenging problem which creates many complications to travelers. Unattended congestion in roadways will cause long waiting times and affects individual economy. It also creates environmental issues and affects personal health. In recent years, the development of Vehicular Ad-hoc Network (VANET) provides a promising solutions for handling the congestion in the roadways. In this paper, we adapt a VANET networking model and propose a novel congestion detection and avoidance scheme for urban areas. A light weight histogram model is utilized to compute the congestion for every lane using an infrastructure based system. By computing the probability density function for every lane, the proposed model predicts congestion in advance and the re-routing strategy is initiated on time. For optimizing the re-routing strategy, two different kinds of congestion avoidance scheme are proposed based on static and dynamic modelling. Various scenarios are created in the microscopic simulation environment and the effectiveness of the proposed algorithm is analyzed by measuring the average travel time. The simulation results show that the proposed model detects congestion in priori and initiates reroute strategy effectively. <s> BIB005
Statistical methods based traffic density is performed by selecting a volunteer probe vehicle BIB001 , BIB005 ,  . Statistical distributions based on the number of vehicles in the neighborhood are applied on the data collected by the probe and is used to determine the maximum likely-hood estimate  , BIB003 . The global traffic density estimate can be determined by using the local traffic density estimate BIB003 . A cluster of vehicles with direct communication links can be used as the probes to improve the estimation. Statistical method based estimation is best suited for scenarios where the inter-vehicle distance follows an exponential distribution BIB003 . In BIB004 , a window time based histogram model approach is used to develop an unique traffic density estimation technique for urban areas. The technique aims to reduce the amount of sensory data collected to make the estimation. The histogram based approach is adopted to reduce the data set required to be analyzed to observe characteristics that are dynamic in nature of individual lanes. Sensory systems are used to collect density patterns for an observation period segmented into sampling events. The histogram used in this approach is a probability distribution function representing the relation between number of vehicles in an observation window and its corresponding probability. Knuth's rule, an optimized version of the Bayesian fitness function is used in this approach to model. A similar lightweight histogram based approach is also developed in BIB005 , to predict periodically, the level of congestion per lane prior to its occurrence. In  , statistical methods are applied on GPS data collected from sensory nodes to detect traffic congestion. The collected GPS data is processed to identify on and off road traffic and average speed is computed for the nodes in the data set by using the Haversine formula. The K-Means algorithm is used to classify the identified clusters based on the centroid of the cluster group. The classified clusters groups are processed using an algorithm based on the Naive Bayes method to identify congestion levels. The performance of statistical methods based traffic density estimation improves with increase in traffic density. This makes statistical methods based traffic density estimation poor for scenarios with low traffic densities and ideal for urban areas which experience high traffic volumes  , BIB002 . The performance of statistical methods based congestion identification can be improved by increasing the sampling size and frequency along with increasing the monitoring area covered in a cluster group. This increases the overhead on the communication apparatus and directly impacts the effectiveness of the CVT based technique, especially in scenarios with extremely high traffic volumes  .