Traffic Congestion Monitoring Research Articles

Next track point prediction using a flexible strategy of subgraph learning on road networks

Accurately predicting the next track point of vehicle travel is crucial for various Intelligent Transportation System (ITS) applications, such as travel behavior studies, traffic control, and traffic congestion monitoring. Recent works on trajectory prediction follow a paradigm that first represents the raw trajectory and subsequently makes predictions based on that representation. Currently, trajectory representation methods tend to project trajectory points to road networks by map matching and represent trajectories based on the representation of matched roads. However, precisely matching trajectories to roads is a challenge in ITS, as the matching precision is greatly affected by the quality of the trajectory. Meanwhile, since it is difficult to discern whether trajectory matching results are accurate or confounded, how to effectively utilize this type of uncertain geographic context information is also a challenge, which is defined as the Uncertain Geographic Context Problem (UGCoP) in geographic information science. Therefore, we propose a flexible strategy of subgraph learning, referred to as SLM, for predicting the next track point of vehicles. Specifically, a subgraph generation module is first proposed to extract topology contextual information of the roads around historical trajectory points. Secondly, a subgraph learning module is designed to learn rich spatial and temporal features from generated subgraphs. Finally, the extracted spatiotemporal features will be fed into a prediction module to predict the next track points of vehicles on road networks. Our model enables the effective utilization of uncertain geographic context information of trajectories on road networks while avoiding the error brought by map matching. Extensive experiments based on trajectory datasets in two different cities confirm the effectiveness of our approach.

OpenCV Based Automatic Detection of Pedestrian Crossing Platform Using Congestion Monitoring - A Survey

Road traffic congestion and pedestrian accidents are one of the major issues being faced worldwide. The reasons behind these accidents are mainly due to the risk when crossing or walking on road in urban and rural areas under heavy traffic. In order to avoid such circumstances a new idea is proposed- “Automatic uplifting of pedestrian crossing platform using traffic congestion monitoring”. The pedestrian and traffic congestion is constantly monitored using an IR (Infrared) sensor module. At the time when pedestrian density is more, the traffic signal turns red for the vehicles and let the pedestrian walk through uplifted crossing platform. The proposed system consists of motorized platform placed on the zebra crossing line which automatically uplifts when the infrared sensor senses and count the crowded pedestrians at the signal point. This method ensures safety in pedestrian crossing and will also not let drivers to break the rules, which may lead to accidents

An Improved Traffic Congestion Monitoring System Based on Federated Learning

This study introduces a software-based traffic congestion monitoring system. The transportation system controls the traffic between cities all over the world. Traffic congestion happens not only in cities, but also on highways and other places. The current transportation system is not satisfactory in the area without monitoring. In order to improve the limitations of the current traffic system in obtaining road data and expand its visual range, the system uses remote sensing data as the data source for judging congestion. Since some remote sensing data needs to be kept confidential, this is a problem to be solved to effectively protect the safety of remote sensing data during the deep learning training process. Compared with the general deep learning training method, this study provides a federated learning method to identify vehicle targets in remote sensing images to solve the problem of data privacy in the training process of remote sensing data. The experiment takes the remote sensing image data sets of Los Angeles Road and Washington Road as samples for training, and the training results can achieve an accuracy of about 85%, and the estimated processing time of each image can be as low as 0.047 s. In the final experimental results, the system can automatically identify the vehicle targets in the remote sensing images to achieve the purpose of detecting congestion.

Traffic congestion monitoring using an improved kNN strategy

A systematic approach for monitoring road traffic congestion is developed to improve safety and traffic management. To achieve this purpose, an improved observer merging the benefits of a piecewise switched linear traffic (PWSL) modeling approach and Kalman filter (KF) is introduced. The PWSL-KF observer is utilized as a virtual sensor to emulate the traffic evolution in free-flow mode. In the proposed approach, residuals from the PWSL-KF model are used as the input to k-nearest neighbors (kNN) schemes for congestion detection. Here, kNN-based Shewhart and exponential smoothing schemes are designed for discovering the traffic congestions. The proposed detectors merge the desirable properties of kNN to appropriately separating normal from abnormal features and the capability of the monitoring schemes to better identify traffic congestions. In addition, kernel density estimation has been utilized to set nonparametric control limits of the proposed detectors and compared them with their parametric counterparts. Tests on traffic measurements from the four-lane State Route 60 in California freeways show the effectiveness of the PWSL-KF-based kNN methods in supervising traffic congestions.

Machine Learning Approach on Traffic Congestion Monitoring System in Internet of Vehicles

Abstract Traffic congestion is a major issue in urban cities leading to aggregated traffic. With the advancement in intelligent internet of vehicles, new technologies and protocols have been developed to predict the traffic congestion and utilize this traffic-related knowledge for congestion prediction and identification. This paper highlight the ML approach to identify traffic congestion based on multiple parameters such as hard delay constraints, the speed available through GSP vehicle trajectory. Here, we have used the Gaussian process in ML for prediction of traffic speed which uses 3 datasets i.e. training set, prediction set, and road sector data frame. ML can provide live traffic prediction in real-time, future traffic prediction and short-term traffic prediction on recent observation and historical data. In this paper using the data set, we have identified three different time slots for vehicle traffic congestion monitoring and evaluated the average speed of vehicles on the road sector during respective time slots.

Traffic Congestion Monitoring and Management by using IOT

In this day and age, congested driving conditions during surge periods are one type of the real apprehensions. During flood days, emergency vehicles are trucks slow down out in jams. Along these positions, the emergency automobiles are not equipped to complete their objectives on time, it will leads into lost human lives. In this paper proposed to avoid such type of issues, in this paper presented on a self-ruling 2-level framework which will help in the recognizable proof of crisis vehicles or some other wanted vehicle. Here designed the IOT based structure, it will monitor and managing the traffic situation continuously

Smart road stud based two-lane traffic surveillance

This paper develops the two-lane traffic surveillance function of a smart road stud (SRS), which has the capabilities of high-resolution traffic data collection and transmission, and a light-based traffic guidance. The SRS addresses the limitations of the current systems with the potential for high performance and usage for a variety of applications. SRS nodes are mounted on the road surface along the lane separation lines instead of the intrusive middle of the lane detection sensors. The key innovation in SRS design is its ability to detect vehicles in two adjacent lanes in real time by employing a three-axis geomagnetic sensor mounting on the lane line, which is the basis of advanced applications such as traffic surveillance, driving guidance, and congestion monitoring. Multiple vehicles are recognized based on the superposition of the disturbance of the geomagnetic fields. Magnetic field changes along the axes are simulated and analyzed with vehicles modeled as magnet dipoles to determine the criteria for the detection of vehicles in adjacent lanes, which are verified by actual waveforms. Extended dual-window and two-lane vehicle detection algorithms are developed, enabling to distinguish five detection scenarios and count vehicles in two lanes. Field test results show that one SRS can detect traffic in adjacent lanes with a correct vehicle detection rate of over 98%, the same level achievable by intrusive middle of the lane detectors. Based on the enhanced features, high performance, cost-effectiveness, and value-added services, the SRS is a significant improvement on the conventional studs for intelligent transport system applications.

IOT based Online Traffic Congestion Monitoring and Management System

IOT based Online Traffic Congestion Monitoring and Management System

An Efficient Traffic Congestion Monitoring System on Internet of Vehicles

Existing intelligent transport systems (ITS) do not fully consider and resolve accuracy, instantaneity, and compatibility challenges while resolving traffic congestion in Internet of Vehicles (IoV) environments. This paper proposes a traffic congestion monitoring system, which includes data collection, segmented structure establishment, traffic‐flow modelling, local segment traffic congestion prediction, and origin‐destination traffic congestion service for drivers. Macroscopic model‐based traffic‐flow factors were formalized on the basis of the analysis results. Fuzzy rules‐based local segment traffic congestion prediction was performed to determine the traffic congestion state. To enhance prediction efficiency, this paper presents a verification process for minimizing false predictions which is based on the Rankine‐Hugoniot condition and an origin‐destination traffic congestion service is also provided. To verify the feasibility of the proposed system, a prototype was implemented. The experimental results demonstrate that the proposed scheme can effectively monitor traffic congestion in terms of accuracy and system response time.

Pseudonym-based Privacy Protection Scheme for Participatory Sensing with Incentives

Participatory sensing applications rely on recruiting appropriate participants to share their surrounding conditions with others, and have been widely used in many areas like environmental monitoring, health care, and traffic congestion monitoring, etc. In such applications, how to ensure the privacy of a participant is important, since incentive mechanisms are used to maintain their enthusiasm for sustainable participation by offering certain amount of reward. In this paper, we propose a pseudonym-based privacy protection scheme, that takes both privacy protection and user incentives into consideration. The proposed scheme uses the pseudonym mechanism and one-way hash function to achieve user incentives, while protecting their identity. We also show extensive analysis of the proposed scheme to demonstrate that it can meet the security and performance the requirement of a participatory sensing application.

Towards Improving the Quality of Present MAC Protocols for LECIM Systems

Wireless networking system is quickly growing in the field of communication technology due to its usefulness and huge applications. To make the system more effective to the users its lower energy consumption, security, reliability and lower cost issues must be considered under any circumstances. Low energy wireless is exceedingly required because the sensors are frequently located where mains power and network infrastructure are not reliably available. The recent development of Low Energy Critical Infrastructure Monitoring (LECIM) has vast applications including: Water leak detection, Bridge/structural integrity monitoring, Oil & gas pipeline monitoring, electric plant monitoring, public transport tracking, Cargo container monitoring, Railroad condition monitoring, Traffic congestion monitoring, Border surveillance, Medical alert for at-risk populations and many more. This proposal Low Energy Critical Infrastructure Monitoring (LECIM) is proposed by the Task Group 4k under IEEE P802.15 WPAN. Although many issues related to its quality are involved, but several Media Access Control (MAC) protocols with different objectives were proposed for LECIM. In this research paper, issues related to energy consumption and wastage in LECIM system, energy savings mechanism, relevant energy conscious MAC protocols have been briefly studied and analyzed. Science Direct, Elsevier, Springer, IEEE Explore, Google Scholar and Wiley digital Library databases were used to search for articles related to the existing MAC protocols well suited for LECIM system. Finally, some ideas have been proposed towards developing energy efficient MAC protocol for LECIM applications in order to fulfill and satisfy the major issues of LECIM quality

Extended MULE Concept for Traffic Congestion Monitoring

Wireless Sensor Networks are being recently studied to monitor real-time traffic conditions on roads and highways. Idea of using vehicles to convey information from sensors placed alongside roads to the dedicated base stations has also been under scrutiny for some time. In this paper, we argue that a sensor placed on a vehicle instead of a fixed location can effectively sense traffic congestion on the road and report it to the already available WLAN Access Points (APs) instead of the dedicated base stations. This way, instead of deploying series of base stations to collect traffic information, congestion information can be sent over the ISM links between the vehicular sensor nodes and the WLAN APs. This paper investigates, as we call it, the Extended MULE concept by using actual experimental data obtained from the test drives across the city. Our results show that adopting this idea is effective in reporting traffic congestion on the roads.

Dynamic Wide-Area Congestion and Incident Monitoring Using Probe Data

Traffic congestion and overall performance monitoring of roadways continues to be a major initiative of departments of transportation and planning boards nationwide. Information from traffic monitoring programs has direct relevance to both analysts and the general public. Traditional data analysis tools and methods in this area fail to connect congestion data with incident and event data and thus make the task of determining the causes of congestion difficult in many cases. As more traffic data are collected, the need for tools that can facilitate effective visualization of this data, both archived and real time, is becoming increasingly apparent. The proposed congestion and incident scanner tool is a web-based application that affords dynamic and interactive analysis of traffic congestion conditions. The tool provides an intuitive, minimalistic interface for generating congestion performance visualizations for specific date ranges and locations. These visualizations improve on existing tools by allowing users to interact with and manipulate the visualizations to better highlight specific areas of interest. The integration of incident and traffic event data with visualization allows users to easily correlate congestion abnormalities with possible causes as well as evaluate the full effects of events, such as roadwork and major incidents, on traffic conditions.