Traffic Congestion Detection Research Articles

Detection of traffic congestion based on twitter using convolutional neural network model

<span lang="EN-US">Microblogging is a form of communication between users to socialize by describing the state of events in real-time. Twitter is a platform for microblogging. Indonesia is one of the countries with the largest Twitter users, people can share information about traffic jams. This research aims to detect traffic jams by extracting tweets in the form of vectors and then inserting them into the Convolution neural network (CNN) model and getting the best model from CNN+Word2Vec, CNN+FastText, and support vector machine (SVM). Data retrieval was conducted using the Rapidminer application. Then, the context of the tweets was checked so that there were 2777 data consisting of 1426 congestion road data and 1351 smooth road data. The data was taken from certain coordinate points in around Jakarta, Indonesia. Then, preprocessing and changes to vector form were carried out using the Word2Vec and FastText methods, then inserted into the CNN model. The results of CNN+Word2Vec and CNN+FastText were compared to the SVM method. The evaluation was done manually using the actual traffic conditions. The highest result obtained using test data by the CNN+FastText method are 86.33% while CNN+Word2Vec is 85.79% and SVM is 67.62%.</span>

Fine Traffic Congestion Detection With Hierarchical Description

Is video congestion detection a visual classification problem? Most studies say yes. They classify road states into 1–3 congestion levels to determine whether congestion occurs. However, in real traffic scenes, congestion is a dynamic process, including generation, evolution, and dissipation. Congestion detection should not only be a classification problem but also the detection of the entire congestion process. This paper proposes a novel method to realize fine congestion detection using road surveillance video. The first step is to analyze road scene and detect vehicle information. The road area is modeled as a grid to construct three region levels: segment, lane, and cell. Vehicle position sequences and speeds are detected by using object detection, multi-object tracking, and sparse optical flow. The second step is to discriminate congestion state. Traffic flow, average speed, and visual impact parameters of each region level are counted. And a three-dimensional model is formed to obtain the short-time congestion state. The third step is to describe the congestion spatiotemporal changes by accumulating multiple short-term congestion states. Experiments are organized from three aspects: whether congestion occurs, continuous congestion degree, and spatiotemporal changes of congestion. The results indicate that the proposed method has higher detection accuracy while reflecting the whole spatiotemporal changes of congestion degree.

FogJam: A Fog Service for Detecting Traffic Congestion in a Continuous Data Stream VANET

In a continuous data stream vehicular network environment, a Traffic Congestion Detection Service (TCDS) receives many periodic information to update and discover road segments with low speeds and high vehicular density. The use of Cloud computing to support the massive amounts of traffic data received from multiple vehicles significantly increases network traffic. Therefore, we propose FogJam, a Fog service to detect traffic congestion directly at the edge of the vehicular network. In the network edge, FogJam leverages sampling and clustering-based methods to reduce the traffic data stream transmitted by all vehicles on the network links to the Cloud. In the Cloud, FogJam is used as a macro-control of all vehicles geographic positions, acquiring traffic flow data to detect traffic congestion. We evaluate FogJam using OMNeT++, Veins and SUMO simulators. The results suggest that FogJam is highly accurate in detecting traffic congestion at a lower cost, even in a high vehicular density scenario. Furthermore, using clustering-based methods, FogJam is able to, on average, reduce the impact on network usage by approximately 70% compared to the sampling methods, while maintaining an acceptable level of congestion detection accuracy.

Vehicle Detection Monitoring System using Internet of Things

The overcapacity of vehicle numbers is one of the significant causes of the traffic congestion problem on Indonesia roadways. The government applies a One-way system (SSA) as one proposed solution to unravel the congestion. However, several congestion points are still found during the SSA implementation. Thus, this research offers an alternative method to detect congestion using IoT technology. The system automatically enumerates the number, classifies the type, and computes the speed averages of vehicles to identify the severity of congestion based on the Indonesian Highway Capacity Manual (IHCM) published by the Ministry of Public Works 2014. We utilize ultrasonic sensors to detect the vehicles and send the data to the server in real time. The research successfully develops an IoT system for traffic congestion detection. Communication between nodes and API can be done well. Data exchange involving encryption and decryption with AES-256 is successfully done. The website application developed in this research successfully show the severity level of the congestion and their vehicle numbers. The average accuracy of the system is 78,97%. The system detected more vehicles than actual numbers due to the misreading value of the sensors.

Comparative Validation of Spatial Interpolation Methods for Traffic Density for Data-driven Travel-time Prediction

In data-driven travel-time prediction, previous studies have mainly used speed as the input. However, from a traffic engineering perspective, given that speed varies little in the free-flow regime, traffic density, which can accurately represent traffic conditions from the free-flow regime to the congested-flow regime, is preferable as an input. In this study, we compared the accuracy of traffic densities spatially interpolated using spatial statistical and machine learning methods, and validated their effectiveness as inputs for travel-time prediction. The results show that even traffic density interpolated by simple spatial interpolation contributes to the accuracy of travel-time prediction and is superior to speed for early detection of traffic congestion.

Smart Traffic Monitoring System Using Computer Vision and Edge Computing

Traffic management systems capture tremendous video data and leverage advances in video processing to detect and monitor traffic incidents. The collected data are traditionally forwarded to the traffic management center (TMC) for in-depth analysis and may thus exacerbate the network paths to the TMC. To alleviate such bottlenecks, we propose to utilize edge computing by equipping edge nodes that are close to cameras with computing resources (e.g., cloudlets). A cloudlet, with limited computing resources as compared to TMC, provides limited video processing capabilities. In this paper, we focus on two common traffic monitoring tasks, congestion detection, and speed detection, and propose a two-tier edge computing based model that takes into account of both the limited computing capability in cloudlets and the unstable network condition to the TMC. Our solution utilizes two algorithms for each task, one implemented at the edge and the other one at the TMC, which are designed with the consideration of different computing resources. While the TMC provides strong computation power, the video quality it receives depends on the underlying network conditions. On the other hand, the edge processes very high-quality video but with limited computing resources. Our model captures this trade-off. We evaluate the performance of the proposed two-tier model as well as the traffic monitoring algorithms via test-bed experiments under different weather as well as network conditions and show that our proposed hybrid edge-cloud solution outperforms both the cloud-only and edge-only solutions.

Regional Detection of Traffic Congestion Using in a Large-Scale Surveillance System via Deep Residual Traffic Net

With the growing demand for Smart cities applications, traffic control and its management have huge demand and a highly interested research area. Surveillance images and videos can be monitored effectively to identify traffic congestions. There is existing research available on traffic signal controls through image processing and various machine learning methods. Traffic prediction through surveillance camera videos, images are very interesting as it can update with live data for users. The proposed work detects traffic prediction based on multiclass problems. There are four classes considered for this proposal are heavy traffic, less Traffic, accident prediction and fire accident prediction. As a result, the suggested approach outperforms existing systems that rely mostly on binary categorization. For image training and detection, the suggested work uses a single deep learning technique, Convolutional Neural Network (CNN). With low maintenance, the proposed system can be used for large-scale traffic surveillance systems. The proposed system attained the best accuracy of 80% for 20 epoch training with four detection classes, according to the results of the experiment. Key Words: Machine learning, deep learning, Convolutional Neural Networks (CNN), Traffic prediction, multi-class classification.

High-precision traffic congestion detection using lane-by-lane polygons

High-precision traffic congestion detection using lane-by-lane polygons

RESEARCH ON TRAFFIC CONGESTION DETECTION FROM CAMERA IMAGES IN A LOCATION OF DA LAT

Many researchers are interested in traffic congestion detection and prediction. Traffic congestion occurs increasingly in many cities in Vietnam, including the city of Da Lat. This paper focuses on SVM, CNN, DenseNet, VGG, and ResNet models to detect traffic congestion from camera images collected at Nga 5 Dai Hoc, Da Lat. These images are labeled with the words traffic congestion or no traffic congestion. The experimental results have an accuracy of over 93%. The study is an initial contribution to a future system for predicting traffic congestion in Da Lat when the camera system is fully installed.

A traffic data clustering framework based on fog computing for VANETs

Vehicular Ad-hoc Networks (VANETs) are based on vehicle to infrastructure communications in which the vehicles periodically broadcast information to update a Road Side Unit (RSU). The traffic data is forwarded from all RSUs to a cloud or a central server for global analysis and detection of congestion levels on the roads. However, communication costs may considerably increase when a large amount of data is transmitted to such cloud-like service providers. In this paper, we propose a data clustering framework to perform traffic information reduction at the edge of vehicular networks by exploiting fog computing. The proposed data clustering framework defines two methods for the reduction of the traffic data stream: (i) Baseline method, which is an ordinary traffic congestion detection approach, and (ii) two adapted clustering methods for a data stream; namely, the Ordering Points to Identify the Clustering Structure (OPTICS) and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The results have shown that the proposed traffic data framework using clustering methods is accurate even when the vehicular traffic condition is highly congested, potentially reducing the communication costs and bringing significant results for the development of VANETs.

A novel image-based convolutional neural network approach for traffic congestion estimation

Traditional image-based traffic congestion estimation methods generally include two steps, which first extract the vehicles from the surveillance images, then calculate the congestion index using the vehicle counts. When working with vast amount of video frames, these approaches are time-consuming and hardly guarantee the real time detection of traffic congestion. In this study, firstly a specific and accurate definition of traffic congestion is proposed to quantify the level of traffic congestion. Then we construct an image-based traffic congestion estimation framework, in which a traffic parameter layer is integrated to the basic convolutional neural network (CNN) model. The proposed framework can directly perform traffic congestion calculation and estimation, which shortens the processing time and avoids the complicated postprocessing. A dataset of 1400 traffic images including 66,890 vehicles is collected for training the proposed CNN model. Another new dataset of 2400 traffic images including 113,516 vehicles is collected to test the proposed method on estimating traffic congestion. Experimental results show that our proposed approach has better efficiency and stability in both free flow and congested traffic conditions, as well as sunny and rainy scenes.

Detecting spatiotemporal extents of traffic congestion: a density-based moving object clustering approach

ABSTRACT Traffic congestion detection poses challenges in spatiotemporal data mining and intelligent transportation research. Existing studies primarily detect traffic congestion based on the speed estimation of traffic flows. Such detection techniques may overlook the formation of traffic congestion in space and time. This research proposes a density-based approach to moving object clustering that extracts the spatiotemporal extents of traffic congestion in three steps. The first step applies a map-matching strategy to project original trajectory points in a planar space onto a road network space and segments the trajectories into consecutive time windows. In the second step, we statistically detect moving clusters with significantly high-density subject to network constrained clustering. The final third step determines moving clusters indicative of traffic congestion through the analysis of both vehicle speed and time spans. Comparative experiments on both simulated trajectories and the real-life taxi trajectories in Wuchang demonstrate that the proposed method outperforms other methods through quantitative evaluations using three indicators, i.e. the precision, recall and F1 value. The proposed approach can illustrate the spatiotemporal regularities of traffic congestion, which can inform dynamic route planning and network design optimization.

Autonomous Parking-lots Detection with Multi-sensor Data Fusion Using Machine Deep Learning Techniques

The rapid development and progress in deep machine-learning techniques have become a key factor in solving the future challenges of humanity. Vision-based target detection and object classification have been improved due to the development of deep learning algorithms. Data fusion in autonomous driving is a fact and a prerequisite task of data preprocessing from multi-sensors that provide a precise, well-engineered, and complete detection of objects, scene or events. The target of the current study is to develop an in-vehicle information system to prevent or at least mitigate traffic issues related to parking detection and traffic congestion detection. In this study we examined to solve these problems described by (1) extracting region-of-interest in the images (2) vehicle detection based on instance segmentation, and (3) building deep learning model based on the key features obtained from input parking images. We build a deep machine learning algorithm that enables collecting real video-camera feeds from vision sensors and predicting free parking spaces. Image augmentation techniques were performed using edge detection, cropping, refined by rotating, thresholding, resizing, or color augment to predict the region of bounding boxes. A deep convolutional neural network F-MTCNN model is proposed that simultaneously capable for compiling, training, validating and testing on parking video frames through video-camera. The results of proposed model employing on publicly available PK-Lot parking dataset and the optimized model achieved a relatively higher accuracy 97.6% than previous reported methodologies. Moreover, this article presents mathematical and simulation results using state-of-the-art deep learning technologies for smart parking space detection. The results are verified using Python, TensorFlow, OpenCV computer simulation frameworks.

Nonrecurrent traffic congestion detection with a coupled scalable Bayesian robust tensor factorization model

Nonrecurrent traffic congestion (NRTC) usually brings unexpected delays to commuters. Hence, it is critical to accurately detect and recognize the NRTC in a real-time manner. The advancement of road traffic detectors provides researchers with a large-scale multivariable temporal-spatial traffic data, which allows the deep research on NRTC to be conducted. However, it remains a challenging task to construct an analytical framework through which the natural temporal-spatial structural properties of multivariable traffic information can be effectively represented and exploited to better understand and detect NRTC. In this paper, we present a novel analytical training-free framework based on the coupled scalable Bayesian robust tensor factorization (Coupled SBRTF). The framework can couple multivariable traffic variables including traffic flow, road speed, and occupancy through sharing the same sparse structure. Moreover, it naturally captures the high-dimensional temporal-spatial patterns of the traffic data by tensor factorization. With its entries revealing the distribution and magnitude of NRTC, the shared sparse structure of the framework compasses sufficiently abundant information about NRTC. While the low-rank part of the framework, expresses the distribution of general expected traffic conditions as an auxiliary product. Experimental results on real-world traffic data show that the proposed method outperforms the NRTC detection models based on the coupled Bayesian robust principal component analysis (coupled BRPCA), the rank sparsity tensor decomposition (RSTD), and standard normal deviates (SND). The proposed method performs even better when only traffic data in weekdays are utilized, and hence can provide more precise estimations of NRTC for daily commuters.

A Feature-Based Approach to Large-Scale Freeway Congestion Detection Using Full Cellular Activity Data

Most existing cellular probe-based freeway congestion detection methods rely on on-call WLT (Wireless Location Technologies) signal transition data. However, these techniques facing difficulties such as small sample size, frequent road tests, safety, and privacy issues. This article presents a novel approach using the FCA data for traffic congestion detection on freeways. Two cellular activity features, the link pseudo speed and link probe activity, are defined and calculated. A rule-based algorithm is then developed to determine the traffic congestion state. The proposed method has been implemented and a prototype system has been deployed for a major freeway corridor in China. Validated by fixed-point detector data and incident records, the proposed method is able to identify real-time freeway traffic congestion accurately.

An overview of traffic congestion detection and classification techniques in VANET

<span>Vehicular traffic congestion has been and still is a major problem for many countries and knowledge about the traffic condition is important in order to schedule, plan and avoid traffic congestion. With recent development in technology, various efforts and methods are proposed in mitigating traffic congestion. Vehicular Ad-hoc NETwork (VANET) is very much in the hype in addressing this issue due to its capabilities and adaptation to scalability, highly dynamic topology as well as cooperative communication. A popular focus is in detecting and classisying traffic congestion which presents various techniques and methodologies. This paper presents an overview of traffic congestion detection and classification methods of various related techniques in VANET, organized from the research perspective. Qualitative analysis is presented to classify these strategies in its system architecture, detection and classification methods, as well as its simulated mobility environment and simulation tools used. The analysis is useful in understanding all the techniques and methods applied in resolving this issue in the research domain. </span>

Traffic Measurement and Congestion Detection Based on Real-Time Highway Video Data

Since global road traffic is steadily increasing, the need for intelligent traffic management and observation systems is becoming an important and critical aspect of modern traffic analysis. In this paper, we cover the development and evaluation of a traffic measurement system for tracking, counting and classifying different vehicle types based on real-time input data from ordinary highway cameras by using a hybrid approach including computer vision and machine learning techniques. Moreover, due to the relatively low framerate of such cameras, we also present a prediction model to estimate driving paths based on previous detections. We evaluate the proposed system with respect to different real-life road situations including highway-, toll station- and bridge-cameras and manage to keep the error rate of lost vehicles under 10%.

Vision‐based vehicle detection for road traffic congestion classification

SummaryDue to the increasing number of vehicles in circulation in different urban cities, several automatic traffic monitoring systems have been developed. In particular, traffic monitoring systems using roadside cameras are becoming extensively deployed, as they offer imperative technological advantages compared with other traffic monitoring systems. Vehicle detection and traffic congestion classification are two main steps for video‐based traffic congestion detection systems; the associated methods have a deep impact on the performance of the whole system. In this paper, we investigate four selected vehicle detection methods namely Gaussian Mixture Model (GMM), GMM‐Kalman filter, Optical Flow, and ACF object detector in two contexts: urban and highway. Three traffic congestion classification methods are also studied. The comparative study of the different methods allows us to choose the most appropriate ones to be integrated in the framework proposed to solve the traffic issues in the bridge of Bizerte.

A secure road traffic congestion detection and notification concept based on V2I communications

Applying vehicular (V2X) communications in detecting traffic congestion is a promising approach, as smart and self-driving vehicles are equipped with sensors that can be used to detect an incident anywhere in real-time. Unfortunately, without appropriate security measures and careful design the communication can be vulnerable to malicious attacks, causing even more damage on the roads. Addressing these problems, we propose a high-level system architecture and a security protocol specifically designed for congestion detection based on vehicle-to-infrastructure (V2I) type communication. The security properties of our proposed approach are formally verified using the ProVerif tool, and its efficiency compared to the traditional traffic light systems is demonstrated through simulations with the Veins framework. Results show that our system is secure against a large set of attacks, and can have lower total travelling time compared to three traditional traffic light approaches based on induction loop, lane area detector/camera (installed near the junctions), and static lights.

Traffic Congestion Detection and Alerting Ambulance using IoT

Traffic Congestion Detection and Alerting Ambulance using IoT