A Survey of Abnormal Traffic Information Detection and Transmission Mechanisms in VSNs <s> Road Congestion Detection. <s> We have developed an algorithm, referred to as spatio-temporal Markov random field, for traffic images at intersections. This algorithm models a tracking problem by determining the state of each pixel in an image and its transit, and how such states transit along both the x-y image axes as well as the time axes. Our algorithm is sufficiently robust to segment and track occluded vehicles at a high success rate of 93%-96%. This success has led to the development of an extendable robust event recognition system based on the hidden Markov model (HMM). The system learns various event behavior patterns of each vehicle in the HMM chains and then, using the output from the tracking system, identifies current event chains. The current system can recognize bumping, passing, and jamming. However, by including other event patterns in the training set, the system can be extended to recognize those other events, e.g., illegal U-turns or reckless driving. We have implemented this system, evaluated it using the tracking results, and demonstrated its effectiveness. <s> BIB001 </s> A Survey of Abnormal Traffic Information Detection and Transmission Mechanisms in VSNs <s> Road Congestion Detection. <s> Traffic and travel information (TTI) systems in use today are based on a centralized structure. Sensors along the roadside monitor traffic density and transmit the results to a central unit where a situation analysis of the global traffic scenario is performed. The resulting traffic information is made available to drivers via broadcast service or alternatively on demand via cellular phone. Within the FleetNet project, a completely different proposal to establish a powerful traffic information system is developed and analyzed, which is based in inter-vehicle communications (IVC). All vehicles are part of a self-organizing traffic information system (SOTIS): each vehicle monitors the locally observed traffic situation by recurrently receiving data packets with detailed information from other vehicles. A traffic situation analysis if performed in each individual vehicle and the result is transferred via wireless data-link to all surrounding vehicles in the local neighborhood. No sensors along the highways, no central units and no local broadcast stations or cellular networks are needed, which minimizes infrastructure costs. The complete system can be easily avoided completely. Simulation results demonstrate that the proposed SOTIS technique can easily provide detailed information for each vehicle with an individual information range of more than 50 km from the current position with high accuracy and low delay. Even in situations where only a small fraction of all vehicles (e.g. 2%) is equipped with the SOTIS technology, the full functionality of a traffic information system is available. <s> BIB002 </s> A Survey of Abnormal Traffic Information Detection and Transmission Mechanisms in VSNs <s> Road Congestion Detection. <s> In many intelligent transportation system applications, understanding vehicle activity patterns of probe vehicles is becoming increasingly important for determining congestion levels on different roadways. Vehicle activity patterns can be characterized at a microscale level as vehicle velocity trajectories. Many ITS applications now use probe vehicles that are constantly monitoring and recording these velocity data as a component of traffic monitoring and management. Rather than transmitting probe vehicle velocity data to a centralized management center for processing (requiring high bandwidth), it is proposed to perform real-time on-board analysis of velocity trajectories to estimate roadway type and congestion level. These estimates can then be used for a variety of purposes including traffic information systems, intelligent real-time vehicle control systems, and energy consumption/emissions estimation. A method is presented to estimate roadway type and congestion level using a wavelet analysis technique combined with principal components analysis. This technique is applied to 128-second snippets of real-time vehicle velocity trajectories. The complexity of this combined wavelet/PCA approach is o(l3), where l Lt N. Training and recognition has taken place on a small data set of approximately 300 trajectories collected in the Held. Results thus far indicate an approximate 90 % correct estimation rate <s> BIB003 </s> A Survey of Abnormal Traffic Information Detection and Transmission Mechanisms in VSNs <s> Road Congestion Detection. <s> In this study, we demonstrate that traffic congestion areas could be identified by observing cell dwell time, which is the length of time that a mobile device remains registered to a base station until it switches to another base station in a cellular wireless network. Based on routine measurements from Bangkok roads and freeways in Thailand, we have found that large cell dwell times were usually associated with traffic congestion areas. Our additional measurements through GPS devices and video record also confirm such finding <s> BIB004 </s> A Survey of Abnormal Traffic Information Detection and Transmission Mechanisms in VSNs <s> Road Congestion Detection. <s> Automobile traffic is a major problem in developed societies. We collectively waste huge amounts of time and resources traveling through traffic congestion. Drivers choose the route that they believe will be the fastest; however traffic congestion can significantly change the duration of a trip. Significant savings of fuel and time could be achieved if traffic congestion patterns could be effectively discovered and disseminated to drivers. We propose a system that uses a standard GPS driving aid, augmented with peer-to-peer wireless communication. The proposed system uses a combination of clustering and epidemic communication to find and disseminate dynamic traffic patterns. <s> BIB005 </s> A Survey of Abnormal Traffic Information Detection and Transmission Mechanisms in VSNs <s> Road Congestion Detection. <s> On-board video analysis has attracted a lot of interest over the two last decades, mainly for safety improvement (through e.g. obstacles detection or drivers assistance). In this context, our study aims at providing a video-based real-time understanding of the urban road traffic. Considering a video camera fixed on the front of a public bus, we propose a cost-effective approach to estimate the speed of the vehicles on the adjacent lanes when the bus operates on its reserved lane. We propose to work on 1-D segments drawn in the image space, aligned with the road lanes. The relative speed of the vehicles is computed by detecting and tracking features along each of these segments, while the absolute speed of vehicles is estimated from the relative one thanks to odometer and/or GPS data. Using pre-defined speed thresholds, the traffic can be classified in real-time into different categories such as "fluid", "congestion"... As demonstrated in the evaluation stage, the proposed solution offers both good performances and low computing complexity, and is also compatible with cheap video cameras, which allows its adoption by city traffic management authorities. <s> BIB006 </s> A Survey of Abnormal Traffic Information Detection and Transmission Mechanisms in VSNs <s> Road Congestion Detection. <s> Detection of traffic congestion is an important issue both for the transportation research community and everyday life of the motorists. A new type of information is needed for a more efficient use of road networks. Remote sensing sensors installed on aircrafts or satellites enable data collection for various traffic applications over large areas, especially areas not covered with other, e.g. terrestrial sensors, or difficult accessible areas. Synthetic aperture radar (SAR) systems seem to be very promising due to their all-weather capability. We approach the traffic congestion detection problem with a two-channel SAR sensor flying in along-track (ATI) the motorway by combining various techniques: look processing, channel balancing, coherent change detection, e.g. displaced phase center array (DPCA), image processing and incorporation of a priori information such as traffic model and road network. The potential of the proposed method is demonstrated with airborne E-SAR data collected during several campaigns over highways in Germany. Method application for future tight formation of space satellites is discussed. <s> BIB007 </s> A Survey of Abnormal Traffic Information Detection and Transmission Mechanisms in VSNs <s> Road Congestion Detection. <s> Knowledge about traffic conditions on the road play an important role in route planning and avoiding traffic jams. With recent developments in technology, it is possible for vehicles to be equipped with communication and GPS systems. Equipped vehicles on the road can act as nodes to form a vehicular ad hoc network. These nodes can collect information regarding traffic conditions such as position, speed, and direction from other participating nodes. Depending on the number of participating nodes in the ad hoc network, this collected information can provide useful information on driving conditions to the node collecting this information. With proper analysis this information can be used in detecting and/or predicting traffic jam conditions on freeways. In this article the traffic information gathered by a node in an ad hoc network is viewed as a snapshot in time of the current traffic conditions on the road segment. This snapshot is considered as a pattern in time of the current traffic conditions. The pattern is analyzed using pattern recognition techniques. A weight-of-evidence-based classification algorithm is presented to identify different road traffic conditions. The algorithm is tested using data generated by microscopic modeling of traffic flow for simulation of vehicle or node mobility in ad hoc networks. Test results are presented depicting different percentage levels of vehicles equipped with communication capability. <s> BIB008 </s> A Survey of Abnormal Traffic Information Detection and Transmission Mechanisms in VSNs <s> Road Congestion Detection. <s> Information aggregation is used to merge correlated data items from different nodes before redistributing them. Thus, using aggregation the number of transmissions and the communication overhead can be reduced significantly. Especially for applications which require periodic dissemination of information into a large region, aggregation is a prerequisite. Consider for example a traffic information system where each vehicle periodically disseminates information about road conditions. The aim is to provide drivers with accurate information on traffic conditions for a large road section, so drivers can be informed in time and can take alternate routes in case of traffic congestion, for example. Many aggregation approaches for VANETs use a fixed or structured segmentation of roads for these applications. Based on such segments the aggregation function is applied to fuse data. We argue that such fixed bounds contradict the real situation on roads. Thus either the segments are too large, resulting in loss of accuracy or the segments are too small, resulting in high communication load. Therefore, we propose in this work a system for completely structure-free aggregation. We employ fuzzy reasoning to allow for aggregation decisions to be based on a flexible and extensible set of criteria. These criteria can be application specific and enable a dynamic fragmentation of the road according to the applications' requirements. <s> BIB009 </s> A Survey of Abnormal Traffic Information Detection and Transmission Mechanisms in VSNs <s> Road Congestion Detection. <s> Management of traffic incident is an essential part of emergency management. Impact analysis of incident becomes more and more important in the operation of Intelligent Traffic System. This paper uses flow data collected from loop detectors to analyze the impact of incident in urban traffic system. A complex network model using correlation coefficient as the edge weight is constructed. Then, the random matrix theory is used to evaluate the impact level of incident on network-wide traffic status. At last, cluster analysis is also applied to obtain the largest cluster in the network which indicates the scale and location of critical congestion caused by the incident. <s> BIB010 </s> A Survey of Abnormal Traffic Information Detection and Transmission Mechanisms in VSNs <s> Road Congestion Detection. <s> This paper proposes a method for application in automatic traffic congestion identification method (ACI) using gain amplifier theory. The road segment dividing method is given in this paper, and the ACI method of road lean, road cross-section and natural road segment are proposed to identify the traffic state of expressway. The performance of ACI method is evaluated using real and simulation data. The experimental results indicate that the proposed ACI method of lean produced satisfactory DR with deemed acceptable FAR using traffic data of three time scales. The ACI method of natural road segment is suit for estimating the traffic state of natural road segment. <s> BIB011 </s> A Survey of Abnormal Traffic Information Detection and Transmission Mechanisms in VSNs <s> Road Congestion Detection. <s> Traffic congestion has been a long-held social problem because of the increasing number of vehicles. In this paper, we apply a pheromone model to a traffic signal control in order to alleviate the traffic congestion. The pheromone model is a tool to communicate among insects (e.g., ants) for their crowd action. In our target problem, the pheromone is strewed by vehicles in a trail across the road instead of insects, and the amount of pheromone corresponds to the degree of traffic congestion. Moreover, the factors of traffic signals (i.e., cycle, split, and offset) are controlled by the pheromone to reduce the idling time in front of traffic signals. We performed multi-agent simulations to compare our traffic control method with other control methods. The results show that our proposal method has good effects to the traffic congestion problem. <s> BIB012 </s> A Survey of Abnormal Traffic Information Detection and Transmission Mechanisms in VSNs <s> Road Congestion Detection. <s> Cooperative vehicular systems are being developed to improve traffic safety and management. Reducing road traffic congestion can be achieved through effective management strategies. In this sense, mechanisms for its rapid and accurate detection that allow evaluating the road network performance are crucial. Current Intelligent Transportation Systems (ITS) require the deployment of infrastructure sensors to monitor traffic conditions. Their installation is often expensive and their capability to provide accurate traffic information is limited. In this context, this paper proposes CoTEC (COperative Traffic congestion detECtion), a novel cooperative technique based on Vehicle-to-Vehicle (V2V) communications and fuzzy logic to detect road traffic congestion without the need to deploy infrastructure sensors. The proposed technique is also capable to accurately detect the traffic congestion intensity and length. <s> BIB013 </s> A Survey of Abnormal Traffic Information Detection and Transmission Mechanisms in VSNs <s> Road Congestion Detection. <s> In VANETs, message aggregation is crucial to reduce information redundancy and communication overhead. In addition, it can effectively decrease the information inaccuracy from the on-board sensors. In this paper, we propose a novel scheme, event driven structure-less message aggregation (SLMA), for safety-relevant VANET applications. The key motive of SLMA is to reliably and efficiently improve information accuracy and reduce communication overhead with limited delay at the same time. To our best knowledge, this is the first paper to address these goals of message aggregation simultaneously in VANETs. Using multiple level parallel fusion model to provide high reliability, the structure-less aggregation framework eliminates packet exchanges for aggregation structure formation and maintenance. Bayesian fusion algorithm is adopted to effectively achieve precise event detection on the road. Simulation results show that SLMA scheme efficiently reduces the communication overhead with reasonable delay. Meanwhile, it can greatly improve information accuracy. <s> BIB014 </s> A Survey of Abnormal Traffic Information Detection and Transmission Mechanisms in VSNs <s> Road Congestion Detection. <s> Road congestion is a common problem all over the world. In many developed countries, automated congestion detection techniques have been deployed, that are used in road travel assisting applications. But these techniques are mostly inapplicable in many developing regions due to high cost and their assumptions of orderly traffic. Efforts in developing regions have been few. In this paper, we present RoadSoundSense, an acoustic sensing based technique, for near real time congestion monitoring on chaotic roads, at a moderate cost. We present the detailed design of an acoustic sensing hardware prototype, which has to be deployed by the side of the road to be monitored. This unit samples and processes road noise to compute various metrics like amount of vehicular honks and vehicle speed distribution, with speeds calculated from honks using differential Doppler shift. The metrics are sent to a remote server over GPRS every alternate minute. Based on the metric values, the server can decide the traffic condition on the road. Data from deployment of this prototype in six different Mumbai roads, validated against manually observed ground truth, shows feasibility of per minute congestion monitoring from a remote server. K-means clustering gives on average 90% accuracy to group unlabeled data on a new road into two clusters of congested and free-flow. Deployment data from one road for six days shows the temporal variation in traffic state for that road. Though we test our technique in Mumbai, we believe that most of our claims and experimental results can be extended to city roads of other developing regions as well. <s> BIB015
The traditional road congestion detection mechanisms mainly rely on traffic flows observation and auto congestion identification (ACI) algorithms. To be specific, large-scale infrastructure-based sensors are deployed at cross-roads to gather traffic flow data, such as inductive loop detector BIB001 , video camera, and radio frequency identification receiver. Traffic control centers (TCC) or RSUs play the role of sink nodes and are responsible for detecting congestion information by running ACI algorithms. In BIB010 , based on the correlation coefficient of stationary coil sensor data collected from different crossroads, random variable matrix and complex network model are created. Through observing the eigenvalue changes of random matrix, the fluctuations of traffic flows on some road segments are detected. Combined with the cluster analysis method, the position and scale of traffic congestion can be obtained. In BIB006 , based on the recorded live video streams or pictures, the proposed MORYNE system evaluates the surrounding traffic information by adopting video graphical analysis (VGA) method. After processing this traffic information, the road congestion information is extracted by TCC. Other typical information preprocessing techniques also include: congestion identification based on gain amplification BIB011 , congestion identification based on wavelet analysis BIB003 , congestion prediction based on pheromone model BIB012 , and congestion detection based on pattern recognition BIB008 . Obviously, those centralized TCC-based detecting methods have high detection overhead and long response delay. In order to gather real time and dynamic traffic flow data, some scholars proposed the distributed congestion detection mechanism based on VSNs. For instance, Bauza et al. raise the cooperative traffic congestion detection (CoTEC) system in BIB013 . It uses onboard sensors to transmit and receive CAM packets to estimate the surrounding traffic density. By applying six LOS road service levels supported by Skycomp platform and the fuzzy theory, the vehicle nodes can implement the local road congestion detection by the cooperative intervehicle communication technique. At the same time, the information fusion technique has also been applied into road congestion detection based on VSNs. Generally, it is used to extract the congestion feature information or generate the final result of congestion detection. For instance, the traffic view system utilizes the threshold-based message aggregation method to remove redundancy atomic information and extract the congestion feature information. Due to the fixed threshold values constraints, the number of aggregation message pairs is limited. So, the information fusion efficiency is low. In BIB002 , the SOTIS system proposes the road segmentbased message aggregation method. All the messages with the same road segment ID can be aggregated together. Since the length of road segment is fixed, it is hard to achieve a good tradeoff between the information fusion efficiency and the information detection accuracy. In BIB005 , the Street Smart system proposes the cluster structure-based message aggregation method. It requires that the network topology to be completely connected. Because of the dynamic changeable cluster structures, the information maintenance cost is quite large in VSNs. In BIB009 , Dietzel et al. propose a structureless message aggregation method based on the fuzzy logic algorithm. Although it can reduce the maintenance cost of dynamic structure, the computation overhead is still considerable. In BIB014 , Liu and Chigan design a parallel-style SLMA method based on decision-level information fusion. And each vehicle node has an opportunity to become the decision maker. Therefore, it can avoid the interference of uncertain and inaccurate sensor data. Additionally, with the help of the audio receiver units mounted on street lamp post BIB015 , the GPS system embedded in drivers' smartphone BIB004 , or the E-SAR airborne radar sensors BIB007 , the road congestion information detection also can be realized.