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In the fast modernised world, usage of vehicles increased day by day, leads to vehicle traffic and congestions. Traditional way of monitoring traffic is less efficient and requires man power. Besides, safety of traffic controllers is the major concern in the manual monitoring. For that reason, effective prediction of vehicular traffic and monitoring the level of congestion is significant in VANET (Vehicular Adhoc Network) for mitigating the delays and danger of accidents. In order to make clear prediction of vehicles and the collision free path in the network, numerous existing algorithms have been provided for prediction and classification. However, the conventional techniques have faced complications in satisfying the accuracy while making predictions of path images. The accuracy in prediction along with the faster computational time have been addressed as a drawback which hinders in making appropriate travel path decisions. In order to overcome such challenges, proposed system employed Yolo v5 algorithm, GGA (Greedy based Genetic Algorithm) and Deep CNN (Convolutional Neural Network) with LSTM (Long Short Term Memory) for traffic monitoring and congestion control in VANET. The Yolo v5 algorithm is utilised for the vehicle prediction mechanism, for the capability of detection with high speed and accuracy. GGA is utilised for the feature selection for the capability of handling numerous features in data and to increase the computational speed. The Deep CNN with LSTM is utilised for Classification for the capability of handling larger datasets and to enhance the accuracy. Though, DCNN is the effective algorithm for classification, it has few limitations like slow processing time. To resolve this, DCNN is utilised with LSTM for enhancing the speed in the projected system. Vehicle detection dataset is used in the proposed system. Further, the experimental evaluations and comparative analysis exhibits the efficiency of the system in terms of accuracy, precision, recall and F1 score when compared to several existing algorithms. The proposed model holds the potential in efficient vehicular congestion management with expected accuracy and it is intended to contribute in to the path identification for vehicles in VANET applications. [ABSTRACT FROM AUTHOR]

With the continuous development of socio-economics, the rapid increase in the use of road vehicles has led to increasingly severe issues regarding traffic accidents. Timely and accurate detection of road traffic accidents is crucial for mitigating casualties and alleviating traffic congestion. Consequently, road traffic accident detection has become a focal point of research recently. With the assistance of advanced technologies such as deep learning, researchers have designed more accurate and effective methods for detecting road traffic accidents. However, deep learning models are often constrained by the scale and distribution of their training datasets. Presently, datasets specifically tailored for road traffic accident detection suffer from limitations in scale and diversity. Furthermore, influenced by the recent surge in research on intelligent driver assistance systems, datasets from the surveillance perspective (the third-person viewpoint) are fewer than those from the driver's perspective (the first-person viewpoint). Considering these shortcomings, this paper proposes a new dataset, Traffic Accident Detection from the Perspective of Surveillance (TADS). To the best of our knowledge, we are the first to attempt to detect traffic accident under the surveillance perspective with the aid of eye-gaze data. Leveraging the special data components within this dataset, we design the RF-RG model (input: the RGB and optical flow values of the frames; output: the RGB and gaze values of the predicted frame) for detecting road traffic accidents from a surveillance perspective. Comparative experiments and analyses are conducted with existing major detection methods to validate the efficacy of the proposed dataset and the approach. The TADS dataset has been made available at: https://github.com/cyc-gh/TADS/. [ABSTRACT FROM AUTHOR]

Nowadays Road traffic is a major issue in developing and under-developing countries. With the rampant increase in traffic, society faces major traffic threats including life threats and environmental threats, thus traffic management is a gruesome problem to address. The consequences of poor traffic management include road accidents, jamming traffic, pollution, and many more that can be life-threatening. Living in the twenty-first century with the emergence of technology and the applicability of smart cities provides a perfect solution to curb traffic issues. Keeping in view the deadlock and congestion in traffic, this work will provides solution by indigently detecting and prioritizing vehicles and non-vehicles. The research involves the implementation and comparison of two states of art algorithms Aggregated channel feature and a Point Tracker. Further, the algorithms are enhanced by improving traffic management in terms of identifying the category of transport, prioritizing the traffic which contains vehicles and non-vehicles on basis of the size of vehicle, type of the vehicle, and emergency providing priority to resolve the deadlock. Further, the proposed enhanced point tracker algorithm includes emergency detection in case of an accident and provides an alternative route to neighboring vehicles and non-vehicles. Enhanced ACF has detected a true positive rate of 80%, 89%, has detected true positive rates of 69%, and 79% having non-vehicle detected with assigned priority. Enhanced point tracker has detected true positive rate of 88%, 94%, and 86% having vehicles, non-vehicles, and assigned priority. [ABSTRACT FROM AUTHOR]

In metropolitan regions, the quick increase in vehicles has caused gridlock, pollutants, and problems in the transportation of products. IoT is a breakthrough advancing the development of intelligent management systems and automated processes throughout the universe. For automatic and intelligent cultures, this is a crucial contribution. Effective management and prevention of traffic congestion are reliable strategies that contribute to conserving numerous valuable resources. Autonomous vehicles and smart devices are equipped with an IoT-based ITM system of sensors that can understand, collect, and transfer information. Another method to enhance the transportation system is deep learning (DL). Traffic jams, delays, and a high death rate result from the numerous problems of conventional transportation management systems. An adaptive traffic management system (ATM) built on DL and IoT is designed and implemented in this study's efforts. The suggested system's architecture is built on the automobile, the framework, and the events. To account for all potential problems with the transportation system, the design uses a variety of situations. The proposed methodology uses a Quantum Optical Communication (QOC) system for Traffic monitoring and enhanced data communication. The suggested adaptive traffic management-based QOC model continuously modifies the timing of traffic signals based on traffic volume and anticipated movements from neighbouring junctions. By progressively allowing cars to cross green lights, it considerably reduces the duration of travel. It also reduces congestion in traffic by creating a smoother transition within 38 s, which is less than all other techniques. [ABSTRACT FROM AUTHOR]

In this study, a traffic queue model is employed to simulate traffic parameters for optimization purposes. The primary objective is to enhance the average queue length, cumulative delay, and cumulative reward for varying numbers of vehicles generated using an innovative approach. The proposed methodology integrates deep Reinforcement Q-Learning techniques implemented through a realistic simulator, Simulation of Urban MObility, based on OpenStreetMap. This simulator is used to balance the flow of traffic by monitoring traffic conditions and adjusting signals (Red/Yellow/Green) accordingly. The research extensively evaluates simulation outcomes across different scenarios involving 50, 100, 500, and 1000 vehicles, with episodes 10, 50 and 100. The results provide valuable insights into training times, simulation durations, and total rewards obtained, indicating improvements in mitigating traffic congestion. The introduction of a traffic control strategy enhances model effectiveness through the utilization of four-layer deep neural network architectures with 400 neurons in hidden layers. However, the study also highlights persistent challenges related to real-time implementation and complexities in state-action-agent relationships. The progress highlights improvements in reducing congestion and enhancing efficiency at junctions by dynamically adjusting signal phases while leveraging historical data. The results highlight the potential of dynamic traffic management strategies in effectively addressing urban traffic challenges. [ABSTRACT FROM AUTHOR]

Many highways are acquiring smart transportation systems to improve traffic efficiency, safety and management. Intelligent transportation systems can monitor traffic congestion by providing estimated time of arrival or suggesting a diversion route. However, due to the chaotic complexity of the highway traffic road network and the short-term mobility of the population, traffic flow prediction is affected by many complex factors, and the development of an effective traffic flow forecasting system is very challenging. This paper establishes a novel framework to enhance the prediction of highway traffic flow based on the integration of Complementary Ensemble Empirical Mode Decomposition and Gaussian Mixture Model (CEEMD-GMM). Firstly, the complementary ensemble empirical mode decomposition is used to decompose and reduce the noise of traffic flow data. The empirical modal components are combined by calculating the sample entropy (SE), which makes the time series stable and reduces the time cost of forecasting. Secondly, the histogram of oriented gradient (HoG) is computed to extract the distinct features of various traffic flows and enhance the traffic flow prediction. Furthermore, the highway traffic data is classified using Gaussian mixture model to realize the feature extraction of different types of highway traffics. The performance of the model is evaluated in terms of mean absolute error (MAE), root mean square error (RMSE), mean absolute percent error (MAPE), and average correlation. The model is extensively verified using traffic flow data under individual weather conditions and different traffic flow datasets. Compared with the traditional support vector machine (SVM), artificial neural network (ANN), random forest (RF) and Adaboost algorithms, the proposed model shows 2.90% MAE, 6.07% RMSE, 0.38% MAPE and average correlation coefficient of 0.74 for traffic flow detection. It is concluded that the model can predict the highways traffic flow with high accuracy as compared to other contemporary models. [ABSTRACT FROM AUTHOR]

Accurately estimated highway traffic flow info plays a decisive role in dynamic and real-time road management, planning, and preventing frequent/recurring traffic jams, traffic rule violations, and chain/fatal traffic accidents. Traffic flow information is extracted by processing raw camera images via vehicle detection and tracking algorithms. Object detectors including the Yolo, single-shot detector, and EfficientNet algorithms are used for vehicle detection; however, You only look once version 5 (Yolov5) has a clear advantage in terms of real-time performance. Due to this reason, the pre-trained Yolov5 models were utilized in the vehicle detection part, and in the vehicle tracking module, a novel tracker algorithm was developed using vehicle detection features. The performance of the proposed approach was measured by comparing it to the Kalman filter-based tracker. The evaluation results show that the proposed tracking approach outperformed the Kalman filter-based tracker with 5.82% (Buses), 2.24% (Cars), 36.50% (Trucks), and overall 2.58% better traffic counting accuracy for the 12 nighttime case study videos captured from the highways with different horizontal and vertical angle-of-views. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization in China has contributed to traffic events, such as traffic accidents and delays. It is difficult to detect and resolve highway traffic congestion in a timely manner using traditional methods because they are slow, require a large number of workers, and require the installation of a large amount of monitoring equipment. Therefore, it is imperative to introduce advanced technology to address these challenges. Recently, deep learning technology has made significant breakthroughs and has been widely applied to various fields with satisfactory results. Deep learning is among the most important technologies for the detection and evaluation of traffic congestion, enabling the accurate detection of the state of the expressway network's traffic congestion, the evaluation of traffic congestion, and the prediction of possible traffic congestion. This enables management to formulate traffic dredging strategies in advance to prevent the negative impact of traffic congestion on normal traffic flow. This paper proposes a framework based on deep learning for next-generation highway traffic management. This framework selects traffic congestion indicators to construct an index model, and then constructs a deep learning model based on self-coding. It predicts and classifies highway traffic environment data and excavates sample data based on the characteristics of traffic parameters. As soon as traffic data were classified, a prediction model based on SoftMax was established to detect and predict highway traffic congestion. We conducted a traffic congestion analysis of the Shanghai expressway network based on the speed performance data obtained from the China Traffic Management Bureau. As a result of their research, we developed an index to measure highway traffic congestion. For traffic control and management organizations to function effectively, it is crucial to have an accurate and clear picture of traffic network operations. We evaluated the proposed framework using data gathered from highway monitoring scenes, and the results indicated that 98.6% of the data could be correctly detected. Using the prediction model based on SoftMax for expressway vehicles during peak hours, the accuracy was 92%, and the misjudgment rate was 8%. This study demonstrates that detecting and evaluating the state of highway traffic environments using deep learning has high accuracy, can be applied to actual highway traffic systems, and is extremely useful for detecting highway congestion. This framework is a promising solution for next-generation highway traffic management and provides accurate and timely traffic congestion detection and evaluation. [ABSTRACT FROM AUTHOR]

Intelligent transportation system (ITS) is widely employed in dynamic traffic management to alleviate roadside congestion and increase traffic efficiency for solving the increasingly critical traffic congestion problems. With the advancement in ITS, real-time data collection of vehicles on the road has become a reality. A vast amount of traffic data ensures that the state of the road network may be analysed and predicted in real time for vehicle detection. Decision support system plays a significant role in the early decision making based on some defined criteria again available options. If the decision is made in the right and precise way, then eventually, it lead to success. This research investigates and designs a vehicle recognition algorithm and the road environment discrimination algorithm, which greatly increase the accuracy of highway vehicle detection, using a deep learning framework. In this work, we collect the highway video surveillance's images in various environments, create an original database, build a deep learning model of environment discrimination, and train the classification model to achieve real-time highway environment as the basic condition of vehicle recognition and traffic event discrimination. The proposed work uses a decision support system which is feed with basic information for vehicle detection and selection. Labeling the vehicle target and sample preparation of various environments are carried out to improve the accuracy of detecting the vehicles on a highway. The vehicle recognition algorithm is investigated in this context, and a vehicle detection technique based on weather environment recognition and a rapid RCNN model is presented. The performance of the vehicle recognition algorithm developed in this study is then verified by comparing detection accuracy with the existing state-of-the-art approaches. The comparison with these approaches in terms of accuracy, sensitivity, and F-score shows that our algorithm outperforms these approaches for detecting and classifying vehicles on the highway. [ABSTRACT FROM AUTHOR]

Car congestion is a pressing issue for everyone on the planet. Car congestion can be caused by accidents, traffic lights, rapid accelerations, deceleration, and hesitation of drivers, as well as a small low-carrying capacity road without bridges. Increasing road width and constructing roundabouts and bridges are solutions to car congestion, but the cost is significant. TLR (traffic light recognition) reduces accidents and traffic congestion caused by traffic lights (TLs). Image processing with convolutional neural network (CNN) lakes dealing with harsh weather. A semi-automatic annotation for traffic light detection employs a global navigation satellite system, raising the cost of automobiles. Data was not collected in harsh conditions, and tracking was not supported. Integrated channel feature tracking (ICFT) combines detection and tracking, but it does not support sharing information with neighbors. This study used vehicular ad-hoc networks (VANETs) for VANET traffic light recognition (VTLR). Information exchange as well as monitoring of the TL status, time remaining before a change, and recommended speeds are supported. Based on testing, it has been determined that VTLR performs better than semi-automatic annotation, image processing with CNN, and ICFT in terms of delay, success ratio, and the number of detections per second. [ABSTRACT FROM AUTHOR]

With the rapid development of internet technology and mobile devices, massive streaming data of spatiotemporal information is available for real-time data mining. Outlier detection is playing as one of the most important analysis tasks for trajectory stream processing, such as traffic control and urban planning. Existing work about this issue can be divided into two main categories: individual outlier detection (IOD) and group outlier detection (GOD). IOD focuses on detecting an individual trajectory outlier generated by a single moving object (e.g., an over-speeding car), while GOD aims to detect a group outlier generated by various moving objects (e.g., a group of cars influenced by a traffic jam). However, existing studies only support one of them and cannot comprehensively understand the traffic conditions by combining both of them. To this end, we propose a novel framework for real-time urban traffic outlier detection (RUTOD) in this paper. RUTOD contains IOD based on current traffic conditions and GOD according to historical data. In addition, we adopt a street-based trajectory division to accelerate investigation. Experimental results show that our proposal is not only effective but also efficient for detecting both individual outliers and group outliers in real-time scenario. [ABSTRACT FROM AUTHOR]

Traffic management continues to be one of the most critical challenges facing smart cities. Timely detection of incidents plays an important role in reducing fatality rates, avoiding congestion and improving traffic conditions. Currently, traditional traffic event detection approaches often rely on one source of data, such as road sensor readings or social media posts. However, there is a need for new approaches that can combine these channels and benefit from the diversity of the collected data for better event detection performance. This paper presents a new framework for near real-time event detection based on the fusion of sensor readings and social media data. The shapelets technique, used for sensor readings, generates sub-sequences of the time series representing distinctive patterns. Each pattern is called a shapelet and is selected based on the maximal differentiation achieved between the different classes of a time series set. In traffic events, shapelets can represent patterns of incidents/congestion as well as normal traffic situations that the framework utilizes to detect the occurrence of events. Similarly, social media posts can be featured as shapelets to enable the combination of both media channels creating a multi-modal solution. In the proposed framework, two pipelines are defined : sensor readings detection pipeline and social media detection pipeline. In addition, two multi-modal fusion techniques based on Shapelet Transform are suggested and compared, namely early fusion and late fusion. They help in combining the two pipelines either at the data level or at the decision level. Validation using the M25 London Circular road data shows that early fusion of both sources has better detection rate and better performance over late fusion. [ABSTRACT FROM AUTHOR]

Traffic jams are a significant problem in urban cities that cause pollution and waste fuel, money, and time. Therefore, there is an urgent need to build tools that enable authorities to monitor and understand traffic dynamics and their causes. However, exploring these large complex data presents a challenge to domain experts. This paper proposes JamVis, a web-based visual analytics framework that leverages Waze's multi-modal spatio-temporal data to this end. JamVis comprises two main components designed based on requirements elicited from domain experts. The first one supports the exploration of Waze's traffic jam information through multiple linked views. The second component allows identifying events through alerts reported by Waze users about different problems (e.g., potholes, floods, or heavy traffic). A new algorithm called TST-clustering is introduced to perform event detection, which is an adaptation of the DB-Scan algorithm that allows clustering alerts by space, time, and type. Furthermore, to provide an overview of this algorithm's spatio-temporal results, we introduce a novel visualization called ST-Heatmap. JamVis is validated through three usage scenarios analyzing different events in Rio de Janeiro. [ABSTRACT FROM AUTHOR]

Congested roads and daily traffic jams cause traffic disturbances. A traffic monitoring system using closed-circuit television (CCTV) has been implemented, but the information gathered is still limited for public use. This research focuses on utilizing Twitter data to monitor traffic and road conditions. Traffic-related information is extracted from social media using text mining approach. The methods include Tweet classification for filtering relevant data, location information extraction, and geocoding in order to convert text-based location into coordinate information that can be deployed into Geographic Information System. We test several supervised classification algorithms in this study, i.e., Naïve Bayes, Random Forest, Logistic Regression, and Support Vector Machine. We experiment with Bag Of Words (BOW) and Term Frequency - Inverse Document Frequency (TF-IDF) as the feature representation. The location information is extracted using Named Entity Recognition (NER) and Part-Of-Speech (POS) Tagger. The geocoding is implemented using the ArcPy library. The best model for Tweet relevance classification is the Logistic Regression classifier with the feature combination of unigram and char n-gram, achieving an F1-score of 93%. The NER-based location extractor obtains an F1-score of 54% with a precision of 96%. The geocoding success rate for extracting the location information is 68%. In addition, a web-based visualization is also implemented in order to display traffic information using the spatial interface. [ABSTRACT FROM AUTHOR]

Traffic incidents endanger the smooth running of vehicles. Congestion caused by traffic incidents has caused a waste of time and fuel and seriously affected transportation efficiency. At present, most methods use manual judgment or image features to detect traffic incidents, but these methods lack timeliness, leading to secondary incidents. For dangerous road sections such as ramp-free and long downhills, this paper proposes an algorithm to quickly detect traffic incidents based on a spatiotemporal map of vehicle trajectories. First, a vehicle dataset from the monitoring perspective is constructed, and an improved YOLOv4 detection algorithm is used to detect images organized as batches. Based on the detection result, the multi-object tracking method of vehicle speed prediction in key frames is used to obtain the vehicle trajectory. Then according to the vehicle trajectory obtained in a single scene, the vehicle trajectory is reidentified and associated in the continuous monitoring scene to construct a long-distance vehicle trajectory spatiotemporal map. Finally, according to the distribution and generation status of the trajectory in the spatiotemporal map, traffic incidents such as vehicle parking, vehicle speeding, and vehicle congestion are analyzed. Experimental results show that the proposed method greatly increases the speed of vehicle detection and tracking and obtains high mAP, MOTA, and MOTP indicators. The global spatiotemporal map constructed by trajectory reidentification can achieve high detection rates for traffic incidents, reduce the average elapsed time, and avoid the problems of the inaccuracy of analyzing image features. [ABSTRACT FROM AUTHOR]

Increased number of the vehicles on the streets around the world has led to several problems including traffic congestion, emissions, and huge fuel consumption in many regions. With advances in wireless and traffic technologies, the Intelligent Transportation System (ITS) has been introduced as a viable solution for solving these problems by implementing more efficient use of the current infrastructures. In this paper, the possibility of using cellular-based Low-Power Wide-Area Network (LPWAN) communications, LTE-M and NB-IoT, for ITS applications has been investigated. LTE-M and NB-IoT are designed to provide long range, low power and low cost communication infrastructures and can be a promising option which has the potential to be employed immediately in real systems. In this paper, we have proposed an architecture to employ the LPWAN as a backhaul infrastructure for ITS and to understand the feasibility of the proposed model, two applications with low and high delay requirements have been examined: road traffic monitoring and emergency vehicle management. Then, the performance of using LTE-M and NB-IoT for providing backhaul communication infrastructure has been evaluated in a realistic simulation environment and compared for these two scenarios in terms of end-to-end latency per user. Simulation of Urban MObility has been used for realistic traffic generation and a Python-based program has been developed for evaluation of the communication system. The simulation results demonstrate the feasibility of using LPWAN for ITS backhaul infrastructure mostly in favor of the LTE-M over NB-IoT. [ABSTRACT FROM AUTHOR]

As modern cities expand and develop, the resultant increase in population density gives rise to the need for smart solutions to cope with the demands applied to the infrastructure of the city. In this paper, we investigate the shortcomings of traffic policing and emergency response handling systems; propose an intelligent, autonomous UAV-enabled solution; and describe the system in a simulated environment. Several scenarios of traffic monitoring and policing system are considered in the simulation: traffic light violations and accident detection, mobile speeding traps and automated notification, congestion detection and traffic rerouting, flagged stolen vehicles/pending arrest warrants and vehicle tracking using UAVs, and autonomous emergency response handling systems. Furthermore, smart city infrastructure enable intelligent handling of emergencies by providing traffic light prioritization for ground emergency response units to reduce delay for patient care, automated physical bollard on routes with congested points due to accidents or hazards, first responder support UAV units—medical supplies UAV, fire fighting UAV to combat or control small fires, and numerous other benefits. Lastly, we present the results of the simulated system and discuss our findings. [ABSTRACT FROM AUTHOR]

Traffic rules violations and accidents on road are major issues now-a-days. Identification of vehicles violating traffic rules and manual monitoring of vehicles is difficult, due to traffic congestion on freeways. A novel mathematical model is proposed to generalize detection of a number of traffic violations on highways. The model, using image processing techniques translates lanes on the road as equation of lines. A tracking algorithm generates a vehicle trace which is modelled as equations. A piecewise linearity is used for the modelling and ease of computation of traffic violation. The model then solves a number of equations for finding intersection of traces with the traffic lanes to identify the violations. This novel modelling approach can help machine based identification of a number of traffic violations and proposed system need not to be installed in vehicles and all along road for violation detection. To cover larger length of the road, camera handoff algorithm is also designed. This technique keeps track of all vehicles along with their traces on Google maps. [ABSTRACT FROM AUTHOR]

Twitter has become one of the most popular social media platforms, evidently stirred by a very popular trend of event detection with many applications, including delay detection and traffic congestion on the public transport network. In this paper, we propose a Twitter-based railway delay detection method based on topic propagation analysis of geo-tagged tweets between railway stations. In particular, we aim to discover delay events and to predict train delays due to traffic accidents by analyzing topic propagation using railway network topology of real space. To realize this, first, we construct the topology of the railway network (the physical space) as a graph in which nodes are railway stations and edges are represented as routes between them. Then, we extract the topology of the social network that is mapped on the railway network, based on topic propagation analysis of accident delays between stations and by analyzing geo-tagged tweets of each station with a neural network. This allows us to observe the influence of delays on railway stations even if there are a few tweets on them and to predict stations affected by delays with the tweets which contain indirect topics about delays such as "crowded!" and "raining!". Overall, this paper proposes the method which enables us to analyze the topic propagation of geo-tagged tweets in order to predict accident delays by considering the railway topology of real space. In addition, we also evaluate the performance of the proposed method on datasets derived from Twitter with the actual delay information from 488 stations of 62 routes in Tokyo area in Japan. [ABSTRACT FROM AUTHOR]

Vehicle-to-Vehicle (V2 V) communication opens up new possibilities for the traffic surveillance because of its high accuracy and real-time performance on detecting and avoiding traffic congestion. This work presents a traffic congestion aware V2 V communication framework based on Voronoi diagram and information granularity. Voronoi diagram is used to divide the map into different regions that provide the basis for the proposed V2 V communication model. Then the generation and propagation mechanisms of information about traffic congestion including the intersection congestion and the incident congestion are described. In the proposed V2 V communication, the combination of carry-and-forward strategy and reverse relay strategy is adopted to optimize the information propagation. After analyzing the influence of PoV, which is the ratio of vehicles with the ability of V2 V communication to all vehicles, congestion detection based on information granularity and congestion avoidance based on PoV are also proposed separately. Finally, the performance of such framework is validated by comparison with existing routing protocols and the common approaches adopted in existing maps. This novel V2 V communication framework used in the field of traffic surveillance can effectively increase the ability of detecting and avoiding traffic congestion. [ABSTRACT FROM AUTHOR]

Vehicular ad-hoc network (VANET) is characterized as a highly dynamic wireless network due to the dynamic connectivity of the network nodes. To achieve better connectivity under such dynamic conditions, an optimal transmission strategy is required to direct the information flow between the nodes. Earlier studies on VANET's overlook the characteristics of heterogeneity in vehicle types, traffic structure, flow for density estimation, and connectivity observation. In this paper, we have proposed a heterogeneous traffic flow based dual ring connectivity model to enhance both the message disseminations and network connectivity. In our proposed model the availability of different types of vehicles on the road, such as, cars, buses, etc., are introduced in an attempt to propose a new communication structure for moving vehicles in VANETl under cooperative transmission in heterogeneous traffic flow. The model is based on the dual-ring structure that forms the primary and secondary rings of vehicular communication. During message disseminations, Slow speed vehicles (buses) on the secondary ring provide a backup path of communication for high speed vehicles (cars) moving on the primary ring. The Slow speed vehicles act as the intermediate nodes in the aforementioned connectivity model that helps improve the network coverage and end-to-end data delivery. For the evaluation and the implementation of dual-ring model a clustering routing scheme warning energy aware cluster-head is adopted that also caters for the energy optimization. The implemented dual-ring message delivery scheme under the cluster-head based routing technique does show improved network coverage and connectivity dynamics even under the multi-hop communication system. [ABSTRACT FROM AUTHOR]

Connectivity in vehicular traffic environment has witnessed significant attention due to the direct impact on the performance of most of the traffic safety applications of intelligent transport system. Various parameters such as density, speed, direction, link quality and inter vehicle distance (IVD) have been utilized for measuring connectivity. IVD has greater impact on connectivity and controls the impact of other parameters. Usage of real time IVD for measuring connectivity has not received sufficient attention in VANETs. This paper proposes IVD based connectivity aware routing (Ivd-CAR) for enhancing connectivity aware data dissemination. IVD calculation is robust and can effectively handle instantaneous GPS failure. Two localization techniques; namely, cooperative localization and Geometry based Localization are developed. Standard deviation of real time IVDs of a forwarding path is derived. Distribution of IVDs of a forwarding path is employed for estimating connectivity. Segment vehicle based next hop vehicle selection is utilized for incorporating network load, link quality and direction into consideration while selecting forwarding path. Simulations are carried out in ns2 to evaluate the performance of Ivd-CAR in realistic traffic environment. Comparative analysis of simulation results attests the superiority of Ivd-CAR to the state-of-the-art techniques: CSR and A-CAR. [ABSTRACT FROM AUTHOR]

Wireless network sensors and their use in traffic monitoring, traffic density determination or vehicle speed detection and classification have recently been the focus of interest for researchers. This article describes how a new sensor circuit was designed to deliver instantaneous, real-time and novel solutions as a vehicle detection system, which is more powerful than the nodes used in other studies, and gives results with smaller error margins due to its serial communication qualification. With the proposed logic algorithm, it was possible to categorise the instantaneous traffic status of a road in four levels: no traffic, mild traffic, heavy traffic and very heavy traffic. Additionally, with the nodes placed at the beginning and the end of the road, the number of vehicles per hour for a day was determined and traffic was analysed. Then, vehicles passing by were classified with a proposed classification algorithm and magnetic signature length (MSL) parameter as cars, minibuses, buses and trucks, and an accuracy rate of 95% was obtained. As the last application, the direction of motion of the vehicle on the x-axis as well as left-to-right or right-to-left directions was determined, and the result was 94% accurate. The simplicity of the proposed algorithms, the absence of any complex mathematical calculations, the low cost of the sensor node and circuit and the low power consumption of the communication system demonstrate the superiority of this system in comparison with other studies. [ABSTRACT FROM AUTHOR]