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With increased vehicular traffic on roadways, traffic jams and accidents are common, leading to substantial social and financial losses. Early detection of roadway accidents is beneficial as help can be directed to the accident site quickly, saving the lives of the injured. With this goal, an intelligent accident detection system is proposed in this paper using a Vehicular Adhoc Network model. Each vehicle has an on-board unit with multiple sensors in the proposed work. Outputs of these sensors, which monitor the condition of the driver and the vehicle, are input to a fuzzy logic controller for quick detection of an accident. The proposed system is validated by designing and implementing a hardware prototype. When an accident is detected, the proposed hardware uses a low-range radio (LoRa) transceiver network to send messages to a central server. After receiving the message from a vehicle, the server can inform a nearby ambulance. It can also identify the shortest path to a nearby hospital using Dijkstra's algorithm and inform the ambulance. This would facilitate timely treatment of the accident victim(s). [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]

With advancing vehicular technology, there are challenges related to the computing capabilities of the deployed infrastructure. In a dense vehicular network, the system performance quickly degrades due to the scarcity of computing capacity and the heavy workload on the coordinating nodes. In situations of a road accident or slow-moving traffic, many trigger messages are generated to enable situational awareness. Aforesaid events may lead to network congestion across the vehicular environment resulting in higher packet loss. Moreover, emergency messages incur increased service delays rather than getting preference for servicing. In this work, we propose a clustering-based re-routing framework for network traffic congestion avoidance on urban vehicular roads. We use queue optimization to categorize and execute tasks based on their priority level at each fog node. Moreover, threshold-based congestion detection is used to determine congested nodes, which, alongside clustering-based suitable node selection, is used for workload sharing. Furthermore, a re-routing mechanism is implemented to decrease packet loss, improving the delivery rate. The simulation results show that the proposed technique is reliable, effective, and robust in terms of packet loss, throughput, and delivery of emergency messages. [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]

Road transportation is among the global grand challenges affecting human lives, health, society, and economy, caused due to road accidents, traffic congestion, and other transportation deficiencies. Autonomous vehicles (AVs) are set to address major transportation challenges including safety, efficiency, reliability, sustainability, and personalization. The foremost challenge for AVs is to perceive their environments in real-time with the highest possible certainty. Relatedly, connected vehicles (CVs) have been another major driver of innovation in transportation. In this paper, we bring autonomous and connected vehicles together and propose TAAWUN, a novel approach based on the fusion of data from multiple vehicles. The aim herein is to share the information between multiple vehicles about their environments, enhance the information available to the vehicles, and make better decisions regarding the perception of their environments. TAWUN shares, among the vehicles, visual data acquired from cameras installed on individual vehicles, as well as the perceived information about the driving environments. The environment is perceived using deep learning, random forest (RF), and C5.0 classifiers. A key aspect of the TAAWUN approach is that it uses problem specific feature sets to enhance the prediction accuracy in challenging environments such as problematic shadows, extreme sunlight, and mirage. TAAWUN has been evaluated using multiple metrics, accuracy, sensitivity, specificity, and area-under-the-curve (AUC). It performs consistently better than the base schemes. Directions for future work to extend the tool are provided. This is the first work where visual information and decision fusion are used in CAVs to enhance environment perception for autonomous driving. [ABSTRACT FROM AUTHOR]

In today's era, thinking of Vehicular Ad-hoc Network as a midrib for the leaf of academic, social, corporate, and economic activities will not be erroneous. To avoid any panic situations like road accidents, heavy traffic jams, etc., the timely availability of correct information is obligatory. The presence of malicious nodes within the network will ruin the dream of establishing a safe, secure, and accident-free vehicular network. This objective can be fulfilled only when malicious nodes within the network are identified correctly, and respective actions are taken at the right time. Therefore, there is a great requirement for efficient and intelligent misbehavior detection techniques to deal with such situations. Vehicular networks are very prone to numerous attacks, such as Sybil attacks, unauthorized access, etc. due to their dynamic nature. The main goal of this study is to discuss and bundle various available misbehavior detection schemes and respective solutions to cope with harmful attackers in the network. We have categorized different misbehavior detections on the criteria of architecture, approach, node-centric, and data-centric. The subcategorization is also given within the paper. One section of this paper focuses on the role of machine learning techniques in misbehavior detection as an emerging foot strap for further enhancement. A comparative analysis of various misbehavior detection schemes is also conducted based on performance measures like accuracy, False Positive Rate, Recall, Precision and F-measurement. Finally, the paper concluded by discussing open issues and various research challenges associated with misbehavior detection in the Vehicular Ad-hoc Network. [ABSTRACT FROM AUTHOR]

Predicting traffic accident duration is necessary for ensuring traffic safety. Several attempts have been made to achieve high prediction accuracy, but researchers have not considered traffic accident text data in much detail. The limited text data of the first report on an incident describes the characteristics of an accident that are initially available. This paper uses text data fusing and ensemble learning algorithms to build a model to predict an accident's duration, and a preprocessing scheme of accident duration text data is established. Next, the random forest (RF) algorithm is applied to select feature variables of text data related to the traffic incident duration. Last, a text feature vector is introduced to models such as decision tree, k nearest neighbor, support vector regression, random forest, Gradient Boosting Decision Tree, and Xtreme Gradient Boosting. Our results show that the improved RF model has good prediction accuracy with RMSE, MAPE and R2. From this, the textual factors important to determining the duration of the accident are identified. Further, we investigated that the cumulative importance of 60% is sufficient for traffic accident prediction using text data. These results provide insights into minimizing traffic congestion related to accidents and contribute to the input optimization in text prediction. [ABSTRACT FROM AUTHOR]

Urban areas are more prone to accidents and traffic congestions due to ever-increasing vehicles and poor traffic management. The increase in the emission of harmful gases is another important issue associated with vehicular traffic. Attaining a level of QOS is often challenging as it has to meet the eco-friendly factors along with reliable and safe transportation. Smart and accurate congestion management systems in VANET can significantly reduce the risk of accidents and health issues. To fulfil the requirements of QOS the congestion control methods should consider the properties such as fairness, decentralization, network characteristics, and application demands in VANET. We proposed an Adaptive Congestion Aware Routing Protocol (ACARP) for VANET using the dynamic artificial intelligence (AI) technique. The ACARP presents the adaptive congestion detection algorithm using the type-2 fuzzy logic AI technique. The fuzzy model detects the congestion around each vehicle using three fuzzy inputs viz. bandwidth occupancy, link quality, and moving speed. This is followed by inference model to estimate congestion probability for each vehicle. Finally, defuzzification determines status of congestion detection using the pre-defined threshold value for each vehicle. The status of congestion and its probability values were utilized to establish safe and reliable routes for data transmission. It also saves significant communication overhead and hence congestions in the network. The simulation results provide the evidence that the proposed protocol improves the QOS and assist in reduction of traffic congestions significantly. [ABSTRACT FROM AUTHOR]

Stagnant water on roads has always been a major cause of traffic jams and accidents. Traditional urban waterlogging monitoring and warning system is mainly based on a large amount of historical data and predictive network, which has low accuracy and weak generalization ability. Considering the deep neural network algorithms have demonstrated strong capabilities in computer vision tasks such as object detection, we aim to apply them to road stagnant water detection. In this paper, a novel automatic stagnant water localization method under weak supervision based on visual image is proposed. First, the template matching method is applied to extract road information from the traffic image. Then, due to the complexity of data annotation, we locate stagnant water in image based on Class Activation Maps (CAM) mechanism, which is a weakly supervised method. The detection model consists of the ResNet-18 and the Grad-CAM++ mechanism. Finally, based on the heat map and template, we set a suitable threshold to segment stagnant water area in image. In the experiments, the precision and recall for road stagnant water classification by the proposed model are 99.39% and 99.60%, while the Intersection over Union (IoU) for stagnant water area segmentation is up to 63%. These show that our method is effective for road stagnant water localization. [ABSTRACT FROM AUTHOR]

Traffic congestion and accidents are more likely to occur on gently undulating roads called sags, because drivers are less likely to recognize a change in velocity. Therefore, to induce appropriate judgement and operation, we have proposed to bias the perception of velocity by tactile stimulation. Our previous study showed that the frequency of vibrotactile stimulation based on an engine is related to the perception of velocity and that an increase or decrease in frequency makes the driver feel they are traveling faster or slower than the actual velocity. In this study, to define the control initiation condition that biases the perception of velocity, we investigated a boundary model that can notice a change in velocity based on the velocity and the acceleration. Additionally, we investigated the effect of noticing a change in velocity and the driver's response by biasing the perception of velocity. Psychophysical experiments were conducted using a driving simulator that simulates the vibration of an actual vehicle. The results showed that the change in velocity required to notice an increase or decrease in velocity was 4 km/h when the accelerations were 0.02 and 0.04 m/s 2 , − 6 km/h when the acceleration was − 0.02 m/s 2 , and − 4 km/h when the accelerations were − 0.04 and − 0.06 m/s 2 . Furthermore, by biasing the perception of velocity, we demonstrated that the participants recognized a change in velocity on a gently undulating road and appropriately operated the acceleration pedal. [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]

In recent years, Internet of Vehicles has attracted increasing research attention, especially from the viewpoint of establishing effective information transmission methods to aid drivers and road users. Drivers can currently receive numerous types of assisted information. However, too much and cluttered information may affect their driving performance. Thus, effective guidance and notification services should be provided to drivers according to time, location, and events. For this purpose, we propose a Message Queue Telemetry Transport-based adaptive guide and notification service system called VIPER to provide driving assistance information. VIPER adaptively provides information to drivers and road users based on five conditions: Vehicle, points of Interest, People, Environment, and Roads. First, we establish a hierarchical grid architecture that is used to provide location-based services. Second, we collect information from the vehicles, roads, and environmental sensors to produce a weighted road network. Then, guide and notification services are provided based on this network. Thus, we can provide real-time driving assistance and help drivers to increase their safety and avoid traffic jams. We also analyze historical traffic data collected from vehicle detectors and accident data to estimate the safety and accident risk degrees of roads. To verify the feasibility of the proposed system, a system prototype is implemented to provide guidance and notification services. The experimental results show that our system can effectively assist drivers and road users and that it has a low system response time. [ABSTRACT FROM AUTHOR]

The transport of developing countries faces lots of problems including heavy traffic jams, the uncertainty of waiting time, increased fuel consumption and the risk of road accidents due to unmanaged traffic flow. These problems are being solved by using Intelligent Transportation System (ITS). In this paper, In this paper, we propose the tracking of vehicles which uses crowdsourced positioning data obtained from GPS of smartphones. Traditional transit tracking is costly and resources limited, thus instead installing a tracking device in each vehicle, the location of each vehicle is tracked by user's smartphone. The positioning data from GPS of smartphone is used as an input. We have developed an algorithm, that will detect and recognize the specific vehicle based on crowdsourced data. Using this the users waiting for their desired vehicle at various routes can track vehicle's current location and estimated the time the vehicle will take to reach their location. In this research paper, we have analyzed the usability and reliability of crowdsourcing tracking data in terms of usage of battery charge, power consumption, CPU utilization and response time. It can be seen in the results that proposed work consumes power in between the range of 412 mW to 502 mW only. Whereas, maximum CPU utilization goes up to 0.3%. In the end, the battery life of the proposed system is compared with existing work and it shows proposed system have more battery life than existing systems. As a case study, we have applied our algorithm for tracking of university buses. [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]

Intelligent transportation systems are becoming more and more important nowadays. In these systems, vehicles and possibly the infrastructure communicate with each other by vehicular ad hoc networks (VANETs). VANETs are being deployed and widely used in urban as well as in highway applications. Several standard use cases have been identified over the last decade (i.e., alert messages, car following support, data exchange between vehicles). In this paper, we focus on the alert message propagation on the highway. We derive the stationary and the transient solution of the message propagation distance by constant vehicle speed. Since these messages frequently indicate an accident on the road leading to a traffic jam, we extend the model to take the queueing system due to the traffic jam also into consideration. Our analytical results are compared with SUMO-/Veins-based simulations. [ABSTRACT FROM AUTHOR]

Purpose: Road traffic incidents cause delay, affect public safety and the environment. The CEDR PRIMA project aims to extend practical guidance for traffic managers in pro-active Traffic Incident Management (TIM) techniques to reduce the impacts and associated costs of incidents. Methods: The paper describes modelling methods used in the project for assessing the effect of different management techniques on incident duration and travel delay under various scenarios, including collision, adverse weather, heavy vehicle breakdown and other obstruction, assuming various management strategies and generic impacts of novel technologies. Macroscopic simulations of 178 variations of 13 basic scenarios have been performed using a flexible and computationally efficient macroscopic queue model, results being verified by simulation using a velocity-based Cell Transmission Model (CTM-v). Results: The results of the two modelling methods are broadly consistent. While delays estimated by the two methods can differ by up to 20%, this is small compared to the factor of 30 range of modelled delays caused by incidents, depending on their nature and circumstances, and is not sufficient to affect general conclusions. Under the peak traffic conditions assumed, the most important factor affecting delay is whether running lanes can be kept open, but quick clearance of carriageway is not always feasible. Conclusions: Comparison of two very different modelling methods confirms their consistency within the context of highly scenario-dependent results, giving confidence in the results. Future research and data needs include further validation of the models, potential application to traffic flow and conflict prediction and incident prevention, and more complete and consistent recording of incident timelines and impacts. [ABSTRACT FROM AUTHOR]

Currently, almost every family has at least one car; thus, vehicle density is increasing annually. However, road capacity is finite; consequently, traffic accident frequency may increase due to increasing vehicle density. Typically, car accidents result in traffic congestion because vehicles behind the accident are not aware of the event and continue to follow the front queue. To address this problem, some emergency services, such as emergency message broadcasting, have been proposed. However, not all drivers want to receive such messages because they intend to exit the route prior to the accident scene, which means that communication resources may be wasted. In this paper, we propose a prediction model to forecast vehicles behavior based on a Markov chain and identify which vehicles require the emergency message. In addition, the proposed model includes an efficient policy based on the shortest path for police cars and ambulances such that they can attend the accident scene quickly and relieve traffic congestion. Simulation results show that the proposed method reduces unnecessary message transmission and increases road utilization efficiently. [ABSTRACT FROM AUTHOR]

Cognitive radio spectrum sensing is an important issue in today's emerging communication techniques. Similarly, vehicular networks are vital considering the increasing traffic on roads and fatal accidents. Efficient networks can reduce the road accidents and enhance the suitability of communication between vehicles and with fixed infrastructure. Different techniques like independent spectrum sensing and various forms of cooperative techniques have been proposed in the near past. We propose a sensing technique which prepares a database for small road segments, time slots for the hours of the day, and different frequencies of the spectrum based on the sensing of vehicles throughout the day. Based on this database, the future utilization of the spectrum is proposed. Simulations and results clearly indicate the success and usefulness of our proposed technique. [ABSTRACT FROM AUTHOR]