Traffic State Classification Research Articles

A Study of Adjacent Intersection Correlation Based on Temporal Graph Attention Network.

Traffic state classification and relevance calculation at intersections are both difficult problems in traffic control. In this paper, we propose an intersection relevance model based on a temporal graph attention network, which can solve the above two problems at the same time. First, the intersection features and interaction time of the intersections are regarded as input quantities together with the initial labels of the traffic data. Then, they are inputted into the temporal graph attention (TGAT) model to obtain the classification accuracy of the target intersections in four states-free, stable, slow moving, and congested-and the obtained neighbouring intersection weights are used as the correlation between the intersections. Finally, it is validated by VISSIM simulation experiments. In terms of classification accuracy, the TGAT model has a higher classification accuracy than the three traditional classification models and can cope well with the uneven distribution of the number of samples. The information gain algorithm from the information entropy theory was used to derive the average delay as the most influential factor on intersection status. The correlation from the TGAT model positively correlates with traffic flow, making it interpretable. Using this correlation to control the division of subareas improves the road network's operational efficiency more than the traditional correlation model does. This demonstrates the effectiveness of the TGAT model's correlation.

Accident Detection and Flow Prediction for Connected and Automated Transport Systems

Effective accident detection and traffic flow forecasting are of great importance for quick respond, impact elimination and intelligent control of the traffic flow consisting of autonomous vehicles. This paper proposes a traffic accident detection method for connected and automated transport systems by conducting a grid-based parameter extracting and SVC-based traffic state classification. Allowing for the dynamic spread of traffic flow over time, from upstream to downstream and from accident lanes to other lanes, a spatiotemporal Markov model is established to predict the evolution of traffic flow after accident by introducing the grid as state detection unit and fitting the spatiotemporal evolution with the parameter space mean speed to match the need of both detection accuracy and monitoring scope. Compared with actual accident data, the validation results indicate that the proposed methods present a good performance in accident detection with the accident detection rate as 87.72% and a higher precision rate than both SVM (support vector machine) and ANN (artificial neural network) models in traffic flow prediction. With the active traffic accident identification and dynamic traffic flow prediction, it is beneficial to shorten detection time, reduce possible impacts of traffic accidents and carbon emissions from congestion. The methods can be implied to traffic state recognition and traffic flow prediction, which is one of the significant sections of connected and automated transport systems, and serve as references for accident handling and urban traffic management.

Development of Traffic State Classification Technique

Development of Traffic State Classification Technique

A Combined Discrete Road Traffic State Prediction Model Based on GFD-ARMA-FISHER Analytical Framework

With the popularization of the Internet and the widespread application of mobile terminal, travelers are increasingly dependent on traffic information. It is particularly important to construct and develop more accurate discrete traffic state prediction models. Considering that most of the previous studies about traffic state prediction are essentially a prediction of specific parameters, this paper proposes a generalized fractional difference (GFD)-auto regressive moving average (ARMA)-Fisher combined model that can directly predict the discrete traffic state. First, using the original historical traffic flow data as training samples for cluster analysis, we obtain different traffic state classification samples. Then, using the Fisher discriminant method, we develop a discrete traffic state recognition model based on the traffic state classification samples. The model can help us identify the discrete state of the future traffic flow when we input a set of predicted traffic parameters to it. In order to improve the accuracy of the model prediction, this paper creatively proposes to apply the GFD method to the stabilization of original traffic flow data and then develop an ARMA model to predict the traffic parameters processed by GFD (GFD-ARMA). Last, we obtain the predicted discrete traffic state using the calibrated Fisher discrimination model whose inputs are the parameters predicted by the GFD-ARMA. The proposed method is tested using field data from CHangzhou, China. The results suggest that the developed GFD-ARMA-FISHER method shows a higher accuracy for traffic state prediction and is better than other similar methods.

A Hybrid Method for Traffic State Classification Using K-Medoids Clustering and Self-Tuning Spectral Clustering

As an important part of intelligent transportation systems, traffic state classification plays a vital role for traffic managers when formulating measures to alleviate traffic congestion. The proliferation of traffic data brings new opportunities for traffic state classification. In this paper, we propose a hybrid new traffic state classification method based on unsupervised clustering. Firstly, the k-medoids clustering algorithm is used to cluster the daily traffic speed data from multiple detection points in the selected area, and then the cluster-center detection points of the cluster with congestion are selected for further analysis. Then, the self-tuning spectral clustering algorithm is used to cluster the speed, flow, and occupancy data of the target detection point to obtain the traffic state classification results. Finally, several state-of-the-art methods are introduced for comparison, and the results show that performance of the proposed method are superior to comparable methods.

Freeway traffic safety state classification method based on multi-parameter fusion clustering

In order to deeply analyze the quantitative relationship between traffic flow state and crash risk, a highway traffic safety state classification method based on multi-parameter fusion clustering was proposed. First, attribute data of highway traffic crashes and corresponding upstream and downstream traffic flow data were extracted, and matched with paired case-control method. Secondly, considering the different roles of traffic volume, speed and occupancy in traffic state classification, the weight optimization algorithm is introduced to calculate the weight of the three parameters. Therefore, the comprehensive evaluation index of traffic state with the fusion of three parameters is obtained and used as the input index of traffic safety state clustering. Finally, [Formula: see text]-means clustering method is used to classify the highway traffic safety status. The result of the case study shows that the proposed method can achieve reasonable and effective traffic safety state division. The classification results are helpful to quantitatively evaluate highway crash risk levels under different traffic safety states.

Prediction of Road Network Traffic State Using the NARX Neural Network

To provide reliable traffic information and more convenient visual feedback to traffic managers and travelers, we proposed a prediction model that combines a neural network and a Macroscopic Fundamental Diagram (MFD) for predicting the traffic state of regional road networks over long periods. The method is broadly divided into the following steps. To obtain the current traffic state of the road network, the traffic state efficiency index formula proposed in this paper is used to derive it, and the MFD of the current state is drawn by using the classification of the design speed and free flow speed of the classified road. Then, based on the collected data from the monitoring stations and the weighting formula of the grade roads, the problem of insufficient measured data is solved. Meanwhile, the prediction performance of NARX, LSTM, and GRU is experimentally compared with traffic prediction, and it is found that NARX NN can predict long-term flow and the prediction performance is slightly better than both LSTM and GRU models. Afterward, the predicted data from the four stations were integrated based on the classified road weighting formula. Finally, according to the traffic state classification interval, the traffic state of the road network for the next day is obtained from the current MFD, the predicted traffic flow, and the corresponding speed. The results indicate that the combination of the NARX NN with the MFD is an effective attempt to predict and describe the long-term traffic state at the macroscopic level.

Traffic state classification and prediction based on trajectory data

With the rapid increasing of car usage, congestion is becoming more and more severe in metropolitans. To tackle this problem, the classification and prediction of traffic states are vital for the authority to quickly react. This study proposes a machine learning method to identify and predict traffic states at small region levels. In this paper, we compare two different spatial division methods, namely rectangular grids and hexagonal grids and measure the traffic characteristics through aggregating floating car trajectory data. Then a dimensionality reduction technique is applied to the traffic parameter, followed by a clustering model for traffic state recognition and labeling. From the analysis in this study, speed is found to be an important feature for classifying the traffic state. Therefore, two Convolutional Neural Network (CNN) models using different architecture were used for both speed and traffic state prediction, which could consider the spatial and temporal features simultaneously. Random forest regression, Gradient boosting decision tree and Long-short term memory neural network model are used as baseline models. The case study performed in Chengdu demonstrates that the proposed model outperforms the baseline models.

A fuzzy weighted c-means classification method for traffic flow state division

Traffic status recognition and classification is an important prerequisite for traffic management and control. Based on the idea of weight optimal, a weighted fuzzy c-means clustering method for improving the accuracy of traffic classification is proposed in this study to ease traffic congestion. First, since there are many indexes that affect the traffic flow state classification, three commonly used indexes namely, volume, speed and occupancy are chosen as the main parameters for the traffic flow state classification in this paper. Second, in order to quantitatively analyze the influence degree of different traffic flow parameters on traffic flow state division, based on the principle of weight optimization, the objective function of weight optimization is established. Then the weight of each attribute index is obtained by using the branch and bound algorithm. Finally, since the traditional fuzzy c-means clustering method will not consider the influence of different traffic flow parameter weights on the traffic flow state classification results, the classification effect needs to be further improved. A fuzzy weighted c-means classification method which uses weighted Euclidean distance instead of Euclidean distance is proposed to classify the traffic flow states. Based on the same traffic flow data sample on the same road section, the traffic state classification results with different methods show that it is helpful to improve the traffic flow state classification accuracy by weighting the clustering index. Because the influence of different parameters on the traffic flow state classification is considered in the process of clustering, it is more conducive to improve the classification accuracy. Moreover, it can provide more accurate classification information for traffic control and decision making.

IMM/EKF filter based classification of real-time freeway video traffic without learning

ABSTRACT This paper addresses the problem of traffic variable estimation and traffic state classification of highway traffic, from video. To solve this problem, we propose to use the Interactive Multiple Model (IMM) filter with a multi-class macroscopic model. This filter runs two Extended Kalman Filters (EKF) to smooth the measured traffic parameters. In addition, the models’ probabilities that it provides are exploited to simply classify the traffic state as either free or congested, without the need for a training phase. The evaluation of the proposed system using simulated traffic parameters shows that it achieves a very accurate traffic state classification. The system was also tested in the real world, using video data acquired on a freeway by camera sensors. The obtained classification rates are comparable to those obtained by SVM classification, but at a significantly lower computational load.

An Improved Mixed Distribution Model of Time Headway for Urban Roads Based on a New Traffic State Classification Method

The Mixed distribution model of time headway plays an important role in Intelligent Transportation System. A more accurate improved model based on a new traffic state classification method and correlation coefficient method was established in this study. Upon analyzing traffic data from three unban roads in Nanjing, China from December 8, 2017 to April 24, 2018 in clear days free of fogs and haze, it was found that 1) the improved model dwarfs the other models, for it boasts higher goodness-of-fit and it represents the only one to successfully pass the Chi-square test. 2) the improved model shows better performance in application because the relative errors between the field traffic rate and calculated traffic rate obtained from the improved model are the smallest. 3) the goodness-of-fit of the improved model is affected by traffic state, and the fitting precision of the improved model under four states is the best. Thus, it is hard to tell performance of which model established under two states or three-state model is better. 4) correlation coefficients between time headway and the absolute value of relative speed make the improved Mixed model more fitted and precise. The improved model proposed in this study is a more accurate time headway distribution model.

Traffic State Estimation and Classification on Citywide Scale Using Speed Transition Matrices

The rising need for mobility, especially in large urban centers, consequently results in congestion, which leads to increased travel times and pollution. Advanced traffic management systems are being developed to take the advantage of increased mobility positive effects and minimize the negative ones. The first step dealing with congestion in urban areas is the detection of congested areas and the estimation of the congestion level. This paper presents a a method for a traffic state estimation on a citywide scale using the novel traffic data representation, named Speed Transition Matrix (STM). The proposed method uses traffic data to extract the STMs and to estimate the traffic state based on the Center Of Mass (COM) computation for every STM. The COM-based approach enables the simplification of the clustering process and provides increased interpretability of the resulting clusters. Using the proposed method, traffic data is analyzed, and the traffic state is estimated for the most relevant road segments in the City of Zagreb, which is the capital and the largest city in Croatia. The traffic state classification results are validated using the cross-validation method and the domain knowledge data with the resulting accuracy of 97% and 91%, respectively. The results indicate the possible application of the proposed method for the traffic state estimation on macro- and micro-locations in the city area. In the end, the application of STMs for traffic state estimation, traffic management, and anomaly detection is discussed.

Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison.

Automatic traffic flow classification is useful to reveal road congestions and accidents. Nowadays, roads and highways are equipped with a huge amount of surveillance cameras, which can be used for real-time vehicle identification, and thus providing traffic flow estimation. This research provides a comparative analysis of state-of-the-art object detectors, visual features, and classification models useful to implement traffic state estimations. More specifically, three different object detectors are compared to identify vehicles. Four machine learning techniques are successively employed to explore five visual features for classification aims. These classic machine learning approaches are compared with the deep learning techniques. This research demonstrates that, when methods and resources are properly implemented and tested, results are very encouraging for both methods, but the deep learning method is the most accurately performing one reaching an accuracy of 99.9% for binary traffic state classification and 98.6% for multiclass classification.

Classifying the traffic state of urban expressways: A machine-learning approach

The classification of the urban traffic state and its application is an important part of intelligent transportation systems (ITS), which can not only help traffic managers grasp the traffic operation situation and analyze congestion, but also provide travelers with more traffic information and help them avoid congestion. Thus, an accurate traffic state classification method would be very practical for urban traffic management. The primary objective of this study is to classify the urban traffic state using a machine-learning method (i.e., the FCM clustering method, and the classification results can be determined from the corresponding clustering labels). In this approach, two parts are developed. First, a new classification indicator, i.e., the ample degree of road network is proposed, and it will make up a comprehensive classification indicator system with other parameters such as traffic flow, speed and occupancy. Then, the traditional fuzzy c-means (FCM) clustering approach is improved in two regards, i.e., the fuzzy membership function improvement and weighting processing of the samples, and these improvements can enhance the clustering performance. As a result, an improved machine-learning method (i.e., the improved FCM clustering approach) is developed and used to conduct the clustering analysis with real-world traffic flow data. Next, a case study of Shanghai is used to guide the study process, which consists of data processing, clustering analysis and method comparison. The other methods (e.g., the support vector machines (SVM) method, the decision tree method, the k-Nearest Neighbor (KNN) method and the traditional FCM clustering approach) are introduced to compare with the improved FCM clustering approach. The discussion shows the superiority of the proposed method (e.g., compared with the traditional FCM clustering approach, the objective function value of the improved method decreased by 31.11%, and cluster center error also show a descending trend), and it outperforms the other methods in classification performance (e.g., the overall classification accuracy of the improved FCM method increased by 10.10%, 5.45%, 30.92% and 35.66% in comparison with the traditional FCM method, SVM method, decision tree method and KNN method, respectively). Additionally, the NMI, ROC curve results also illustrated the superiority of the improved FCM method to other methods. These comaprison results suggest that the improved FCM clustering approach is feasible and the results can be well used in the advanced traffic management system, which may have the potential to serve as a reference for releasing accurate traffic state information and preventing traffic congestion and risk.

Burstiness-Aware Bandwidth Reservation for Ultra-Reliable and Low-Latency Communications in Tactile Internet

The Tactile Internet that will enable humans to remotely control objects in real time by tactile sense has recently drawn significant attention from both academic and industrial communities. Ensuring ultra-reliable and low-latency communications with limited bandwidth is crucial for Tactile Internet. Recent studies found that the packet arrival processes in Tactile Internet are very bursty. This observation enables us to design a spectrally efficient resource management protocol to meet the stringent delay and reliability requirements while minimizing the bandwidth usage. In this paper, both model-based and data-driven unsupervised learning methods are applied in classifying the packet arrival process of each user into high or low traffic states, so that we can design efficient bandwidth reservation schemes accordingly. However, when the traffic-state classification is inaccurate, it is very challenging to satisfy the ultra-high reliability requirement. To tackle this problem, we formulate an optimization problem to minimize the reserved bandwidth subject to the delay and reliability requirements by taking into account the classification errors. Simulation results show that the proposed methods can save 40%-70% bandwidth compared with the conventional method that is not aware of burstiness, while guaranteeing the delay and reliability requirements. Our results are further validated by the practical packet arrival processes acquired from experiments using a real tactile hardware device.

Quantitative Analysis of Urban Regional Traffic Status

In order to monitor the real-time operation condition of urban region traffic flow, and to quickly identify regional traffic status, this paper adopts CNM (Clauset-Newman-Moore) Community Division Method of Complex Network to analyze traffic status information deeply implied from the regional road network traffic flow data, which aims to objectively develop the reasonable classification of regional traffic state with no classification criteria of determining regional traffic state. Combined with the regional road network traffic data from a certain city, the example analysis shows that this proposed method can easily provide the reasonable division of regional traffic condition and verifies the feasibility of the regional traffic state classification method. Besides, the example analysis gives the rough regional traffic status determination standard, laying theoretical basis for accurately judging the regional traffic state.

Road-RFSense: A Practical RF Sensing--Based Road Traffic Estimation System for Developing Regions

An unprecedented rate of growth in the number of vehicles has resulted in acute road congestion problems worldwide, especially in many developing countries. In this article, we present Road-RFSense, a practical RF sensing--based road traffic estimation system for developing regions. Our first contribution is a new mechanism to sense road occupancy, based on variation in RF link characteristics, when line of sight between a transmitter-receiver pair is obstructed. We design algorithms to classify traffic states into two classes, free-flow versus congested, at timescales of 20 seconds with greater than 90% accuracy. We also present a traffic queue length measurement system, where a network of RF sensors can correlate the traffic state classification decisions of individual sensors and detect traffic queue length in real time. Deployment of our system on a Mumbai road gives correct estimates, validated against 9 hours of image-based ground truth. Our third contribution is a large-scale data-driven study, in collaboration with city traffic authorities, to answer questions regarding road-specific classification model training. Finally, we explore multilevel classification into seven different traffic states using a larger set of RF-based features and careful choice of classification algorithms.

SCALING AND COMPLEXITY-ENTROPY ANALYSIS IN DISCRIMINATING TRAFFIC DYNAMICS

In this paper, the complexity-entropy causality plane approach is applied to analyze traffic data. The R/S analysis and detrended fluctuation analysis (DFA) methods are also used to compare with this approach. Moreover, based on the concept of entropy, we propose to use permutation to calculate the probability distribution of the time series when applying the representation plane. The empirical results indicate that traffic dynamics exhibit different levels of traffic congestion and demonstrate that this statistical method can give a more refined classification of traffic states than the R/S analysis and DFA.

Analysis of Characteristics of the Dynamic Flow-Density Relation and its Application to Traffic Flow Models

Online traffic flow modeling is of increasing importance due to intelligent transport systems and technologies. The flow-density relation plays an important role in traffic flow modeling and provides a basic way to illustrate traffic flow behavior under different traffic flow and traffic density conditions. Until now the research effort has focused mainly on the shape of the relation. The time series of the relation has not been identified clearly, even though the time series of the relation reflects the upstream/downstream traffic conditions and should be considered in the traffic flow modeling. In this paper, the dynamic flow-density relation is identified based on the classification of traffic states and is quantified employing fuzzy logic. The quantified dynamic flow-density relation builds the basis for online application of a macroscopic traffic flow model. The new approach to online modeling of traffic flow applying the dynamic flow-density relation alleviates parameter calibration problems stemming from the static flow-density relation.