Traffic State Identification Research Articles

Classification of the Traffic Status Subcategory with ETC Gantry Data: An Improved Support Tensor Machine Approach

Accurate and reliable traffic state identification is the prerequisite for developing intelligent traffic programs. With the improvement of intelligent traffic control measures, the traffic state of some highways has gradually stabilized. The current research on traffic state identification needs to fully meet the highly informative intelligent traffic system and traffic state subcategory analysis. To fill the gap above, we propose an improved support tensor machine (STM) method based on self-training and multiclassification for traffic state subcategory identification (ISTM) with ETC gantry data. This paper takes the excellent application of the support vector machine (SVM) in traffic state identification as the starting point of method design and extends to the STM. The ETC gantry data are represented as a third-order tensor model. This paper utilizes the similarity among tensor samples to construct the kernel function and recognize the traffic states. We simplify STM calculation with a one-against-one model and a self-training idea. An optimal fit of the characteristics is supplied by maximizing inter-subcategory tensor block distances and minimizing intra-subcategory tensor block distances throughout a joint utilization of the STM and multiscale training theories. The experiment in this paper uses ETC gantry data from the Jingtai highway in Shandong Province, and the findings reveal that the ISTM has optimum values of 0.2578 and 0.3254 for the SumD and 0.1718 and 0.1901 for the DBI as compared to K-mean clustering and the SVM. The ISTM trains the traffic state subcategory classifiers with high accuracy and strong generalization ability.

Reliable Traffic State Identification Using High-Resolution Data: A Consistent Offline-Online Dynamic Time Warping–Based Time Series Clustering Approach

Reliable real-time traffic state identification (TSI) provides key support for traffic management and control. Although substantial efforts have been devoted to TSI, considering the high dynamics and stochasticity of traffic flows, there remain challenges in providing reliable and consistent TSI results, especially in online network–level applications. In this study, we propose a time series clustering-based offline-online modeling framework for reliable TSI using high-resolution traffic data. Specifically, in the proposed framework, the offline module extracts representative traffic state patterns from massive historical data, which serve as the state references in the online module when performing real-time TSI with streaming information. Instead of point data, the proposed framework uses high-resolution traffic data in the form of time series, providing rich information on traffic flows and details on their short-term fluctuations and stable long-term trends. In the offline module, considering the fuzziness of traffic states, we introduce a fuzzy c-means based clustering method for offline traffic flow series clustering and traffic state pattern extraction, within which the dynamic time warping algorithm is adopted for measuring the similarity between different time series, and the optimal number of clusters is determined by a proposed critical segment–based method to reach consistent TSI in network-wide applications. In the online module, a dynamic programming–based real-time TSI approach is developed to produce reliable and smooth identification results. Extensive numerical experiments on a 20-mi-long freeway corridor in California, USA, were performed to validate the proposed framework. Results demonstrate the effectiveness of the proposed framework.

Hidden Mixture Vehicle Discharge State Inference at Signalized Intersection Using Vehicle Travel Time and Discharge Headway Data

Accurate and reliable traffic state identification is crucial to developing responsive and proactive traffic management applications. In this study, the problem of vehicle discharge state identification at signalized intersections is investigated, which focuses on the vehicle discharge process during the green interval. Instead of using detailed vehicle trajectory data and treating the observations of vehicles independently, this study formulates the vehicle discharge process in a Hidden Markov Model (HMM) framework using sequential observations of vehicle travel time and discharge headway as inputs. Three vehicle discharge states (i.e., overflow, single stop, and free arrival) are encoded as latent states, and a restricted left-to-right state transition matrix is imposed to respect the nature of the vehicle discharge process in the real world. The standard HMM is further extended to incorporate two informative covariates to parameterize the probabilities of the initial states and state transitions. The proposed models have been validated on the Next Generation Simulation (NGSIM) dataset. Compared to a benchmark model, the proposed models show their strength in correctly inferring the vehicle discharge state and are more reliable to use in presence of random missing observations. The effectiveness of covariate incorporation is also investigated, and several extended applications of the proposed models are discussed.

A traffic state recognition model based on feature map and deep learning

Real-time and accurate traffic state identification can provide reference for urban traffic control and guidance. Due to the randomness and complexity of traffic flow, it is difficult to identify the traffic state accurately. The existing researches mainly adopted traffic state feature vectors and machine learning to identify the traffic state. Few studies attempted to use the feature map and deep learning for traffic state recognition. Therefore, this paper proposes a traffic state recognition model based on the traffic state feature map and deep learning. The feature map is a chromatogram form of digital traffic state feature vector. And the deep learning has strong predictive performance in image identification. In the model, the road traffic state feature vector is extracted from the vehicle trajectory data, and the Gram Angle Field (GAF) is adopted to transform the feature vector into feature map. Then, the deep learning algorithm is utilized in traffic state offline classification and online recognition. In the offline training phase, the historical vehicle trajectory data and the DeepCluster algorithm are used to establish the mapping relationship between the feature maps and the traffic states. In the online identification stage, the real-time vehicle trajectory data and CoAtNet algorithm are utilized to identify the current traffic state. In experiments, the proposed model is compared with the Convolutional Neural Network (CNN), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) models using the open vehicle trajectory data of Shanghai North Cross Channel. The results showed that the proposed model achieved the best performance with the accuracy of 92.06% and could provide support for road traffic control and guidance.

A Novel Support Vector Machine Model of Traffic State Identification of Urban Expressway Integrating Parallel Genetic and C-Means Clustering Algorithm

A Novel Support Vector Machine Model of Traffic State Identification of Urban Expressway Integrating Parallel Genetic and C-Means Clustering Algorithm

MAGIC Dataset: Multiple Conditions Unmanned Aerial Vehicle Group-Based High-Fidelity Comprehensive Vehicle Trajectory Dataset

High-fidelity vehicle trajectory data contain rich spatiotemporal characteristics and play a major role in the field of transportation research, for example, driving behavior models, traffic flow models, traffic state identification, and driver assistance strategies. There are some coverage issues with the existing datasets recorded in continuous traffic flow facilities, which may limit the further development of research on the safety and efficiency of continuous traffic flow facilities. For example, the observation areas of these datasets lack different traffic states and section types. Therefore, it is necessary to collect a new trajectory dataset. This paper proposes a detailed plan for aerial photography by unmanned aerial vehicle (UAV) group. An experiment was conducted from 7:40 to 10:40 a.m. on a section of the Shanghai Inner Ring, Shanghai, China, with a total length of 4,000 m in both directions including a large radius curve and six ramps. The trajectory dataset, named MAGIC, is extracted and compared with NGSIM US-101 and HIGH-SIM from the aspects of experiment field, traffic states, and so on. Further, to illustrate the advantages of the MAGIC dataset incorporating different traffic states and section types, the MAGIC dataset is evaluated from the three aspects of traffic congestion state, fundamental diagram, and traffic conflict. Overall, there are significant differences in macroscopic traffic flow and traffic safety characteristics between road sections with different section types or traffic states. Therefore, the proposed method presents some unique advantages and may perform effectively in many fields.

Traffic System State Identification with Integrated Traffic State, Model Parameter and Queue Profile Estimation: Nonlinear Programming Reformulation with Differentiable Traffic State Variables Across Resolutions

Traffic System State Identification with Integrated Traffic State, Model Parameter and Queue Profile Estimation: Nonlinear Programming Reformulation with Differentiable Traffic State Variables Across Resolutions

Traffic State Recognition of Intersection Based on Image Model and PCA Hashing

The premise of implementing an effective traffic control strategy is the accurate traffic state recognition. In the existing study, traffic state recognition methods were processed by using statistical characteristics and long-term scale detection of field traffic data. Hence, the dynamic characteristics and subtle changes in traffic flow were easy to overlook. At present, more and more advanced traffic detection technology provides reliable and accurate data for measuring and distinguishing the state of urban road traffic, such as the cooperative vehicle-infrastructure system, wide-area radar technology, and 5G technology. This study proposes a novel method called HTSI (High Precision Traffic State Identification Method), which is based on the advanced detection technology in traffic state recognition at the intersection: The raw data used for intersection traffic state recognition is high-precision detection data of tracking characteristics, which make the data look like a picture of the intersection at God’s perspective. To this end, we construct an image model for intersections and implement image feature extraction in a way that is different from traditional image processing. Then, the traffic state recognition problem at the intersection is translated into an image searching problem with tags. The image searching is realized by the hashing algorithm. Finally, the comprehensive experiments prove that the proposed method is more accurate and finer than other methods.

An Improved Selective Ensemble Learning Method for Highway Traffic Flow State Identification

Reliable and accurate real-time traffic flow state identification is crucial for an intelligent transportation system (ITS). This identification is a prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved traffic flow state identification model that is based on selective ensemble learning (SEL). First, we adopted the fuzzy C-means (FCM) clustering method to divide the traffic flow data into three main kinds of traffic flow states and obtained the parameters that correspond to each kind of traffic flow state. Second, we applied the random subspace (RS) algorithm as the ensemble method and support vector machine (SVM) model as base learners to construct the RS-SVM ensemble model for traffic flow identification. Significantly, the discrete binary particle swarm optimization (BPSO) algorithm with global optimization search ability was employed to select the classifiers obtained by the random subspace training in the ensemble system. We experimentally validated the effectiveness of the proposed BPSO–RS-SVM-SEL approach. The research results reveal that compared with other classical traffic flow state identification methods, the proposed model has a higher maximum accuracy of 98.68%. It can be seen that our model improves the classification accuracy of traffic flow state identification and the difference in the ensemble system to a certain extent.

A Road Network Traffic State Identification Method Based on Macroscopic Fundamental Diagram and Spectral Clustering and Support Vector Machine

Accurate identification of road network traffic status is the key to improve the efficiency of urban traffic control and management. Both data mining method and MFD-based methods can divide the traffic state of road network, but each has its own advantages and disadvantages. The data mining method is oriented to traffic data with high efficiency, but it can only discriminate traffic status from microlevel, while the MFD of road network can discriminate traffic status from macrolevel, but there are still some problems, such as the fact that the discriminant method of equivalence points based on MFD lacks theoretical support or that traffic status could not be subdivided. If data mining methods and road network’s MFD are combined, the accuracy of road network traffic state identification will be greatly improved. In addition, the research shows that the combination of unsupervised learning clustering analysis method (such as spectral clustering algorithm) and supervised learning machine algorithm (such as support vector machine algorithm (SVM)) is more accurate in traffic state identification. Therefore, a traffic state identification method based on MFD and spectral clustering and SVM is proposed, combining the advantages of spectral clustering algorithm and SVM algorithm. Firstly, spectral clustering algorithm is used to classify the traffic state of road network’s MFD. Secondly, SVM multiclassifier is trained with the partitioned road network’s MFD parameters, and the accuracy evaluation method of classification results based on obfuscation matrix is given. Finally, the connected-vehicle network simulation platform is built for empirical analysis. The results show that the classification results of spectral clustering algorithm are closer to the theoretical values, compared with K-means algorithm, and the accuracy of SVM multiclassifier is 96.3%. It can be seen that our algorithm can identify the road network traffic state more effectively from the macrolevel.

Real-Time Freeway Traffic State Estimation Based on the Second-Order Divided Difference Kalman Filter

Abstract Reliable road traffic state identification systems should be designed to provide accurate traffic state information anywhere and anytime. In this paper we propose a road traffic classification system, based on traffic variables estimated using the second order Divided Difference Kalman Filter (DDKF2). This filter is compared with the Extended Kalman Filter (EKF) using both simulated and real-world dataset of highway traffic. Monte-Carlo simulations indicate that the DDKF2 outperforms the EKF filter in terms of parameters estimation error. The real-word evaluation of the DDKF2 filter in terms of classification rate confirms that this filter is promising for real-world traffic state identification systems.

Interval data‐based k ‐means clustering method for traffic state identification at urban intersections

Identifying traffic states at urban intersections plays a significant role in achieving the full potential of intelligent transportation systems for various traffic applications (e.g. real-time traffic signal control). Many traffic state identification methods use mean values of measured or estimated traffic flow variables to identify traffic states; however, lacking a consideration of traffic flow uncertainty leads to inaccurate identifications. To investigate the impact of traffic flow uncertainty on intersection traffic states, this article proposes an interval data-based k -means clustering method for traffic state identification at urban intersections. The uncertainty of three traffic flow variables (volume to capacity ratio, queue length, and delay) are represented in the form of interval data and employed as input variables. The proposed method was implemented on a real-world traffic network in Kunshan, China. Test results show that the clustering results are explicable and can accurately describe the trend of traffic state evolution. Further investigation shows that the proposed method outperforms the mean-value-based method, and queue length has a more significant contribution to the clustering results after the use of interval data. The findings of this study demonstrate the effectiveness of the proposed method in traffic states identification at urban intersections.

A Multi-Index Fusion Clustering Strategy for Traffic Flow State Identification

This paper attempts to improve the identification accuracy of traffic flow states, and disclose the impacts of different evaluation indices on the identification results. To this end, a multi-index fusion clustering strategy is proposed in this research. Firstly, flow, velocity and occupancy were selected as the evaluation indices. Then, the weights of the three indices were initialized by a group of experts. After that, the objective function of a weight optimization model was set up to maximize the distance between projection centers of samples under different traffic flow states and to minimize the projection variance between samples under the same traffic flow state. The model was solved by the method of Lagrange multipliers, producing the optimal weight combination. Then, the optimal weights were introduced to the fuzzy c-means (FCM) clustering, forming the multi-index fusion clustering method. The results of example analysis show that our method differentiated between traffic flow states more accurately than the original FCM clustering. And the traffic flow identification accuracy improved from 94.0% to 96.6%. This is because the improved method retains most of the original features of the evaluation indices, which further facilitates the accurate clustering of traffic flow states.

Genetic and Simulated Annealing Algorithms-based Traffic State Identification

Accurate and scientific traffic state identification is the basis of traffic navigation system, traffic control system and traffic organization optimization. In this paper, the dynamic traffic data collected by geomagnetic detector are firstly used to identify traffic state. We proposed SAGA-FCM clustering algorithm which is combined simulated annealing algorithm (SA) with genetic algorithm (GA) for urban expressway traffic state identification. This method can overcome the problems those the dynamic data of other detectors are not accurate and the time interval is not uniform. Meanwhile, it can overcome the instability of FCM algorithm clustering center and it is easy to fall into the local extreme value and “premature” problem of genetic algorithm. Research results show that, Compared with FCM algorithm and GA-FCM algorithm, SAGA-FCM clustering algorithm can be more effective and fast convergence, so as to improve the accuracy of urban traffic state identification.

RETRACTED ARTICLE: Research on the intelligent judgment of traffic congestion in intelligent traffic based on pattern recognition technology

Traffic congestion is becoming more and more frequent with the increase of city vehicles. There are still some problems in data processing and real-time traffic state identification for the intelligent judgment of road congestion. Based on this, a multi-class support vector machine method for pattern recognition was proposed, which was an improvement of the traditional support vector machine. Firstly, the road situation was divided into three kinds: “traffic”, “congestion” and “traffic paralysis” by using pattern recognition technology, and the road traffic situation was divided into “traffic” and “congestion” by using support vector machine, on the basis of this, the quadratic discriminant of “congestion” and “traffic paralysis” were carried out to “congestion” state, so that the intelligent judgment of three kinds of traffic state was met. Then combined with the actual road sections and real-time monitoring of road data, the simulation experiment of the pattern recognition was carried out to show that the pattern recognition method can effectively divide and analyze the road traffic situation, and realize the function of intelligent judgment, which could promote the intelligent management of the road, improve the urban road planning and improve the service quality of the traffic system.

A New Traffic Congestion Detection and Quantification Method Based on Comprehensive Fuzzy Assessment in VANET

Recently, road traffic congestion is becoming a serious urban phenomenon, leading to massive adverse impacts on the ecology and economy. Therefore, solving this problem has drawn public attention throughout the world. One new promising solution is to take full advantage of vehicular ad hoc networks (VANETs). In this study, we propose a new traffic congestion detection and quantification method based on vehicle clustering and fuzzy assessment in VANET environment. To enhance real-time performance, this method collects traffic information by vehicle clustering. The average speed, road density, and average stop delay are selected as the characteristic parameters for traffic state identification. We use a comprehensive fuzzy assessment based on the three indicators to determine the road congestion condition. Simulation results show that the proposed method can precisely reflect the road condition and is more accurate and stable compared to existing algorithms.

Network communication load state recognition model based on correlation vector machine

Network communication load state recognition has important practical application value, in order to improve the network communication load state recognition accuracy is proposed based on relevance vector machine load of the network traffic state identification model. When the network throughput overload, need to adjust the network communication strategy to ensure the overall performance of sensor network, so first of all need to network transmission state identification. Firstly, uses wavelet analysis in order to eliminate the noise signal, and extract the features, and then the artificial bee colony optimization algorithm to determine the optimal parameters of relevance vector machine, establishing the network communication load state identification model, finally through with other network communication load condition recognition models to be compared with the experimental. The results show that the model can describe the relationship between the state and the characteristics of network communication, and improve the recognition accuracy of the network traffic load state, and the recognition results are higher than the comparison model.

Hesitant distance set on hesitant fuzzy sets and its application in urban road traffic state identification

Since fuzziness lacks the distinction between a set and its complement, it is difficult to measure the distance between different hesitant fuzzy sets (HFSs) by a single value. In this study, a new concept “hesitant distance set (HDS)” is proposed, where the distance between different HFSs can be characterized by a series of different values. This study has three primary contributions. Firstly, most of the existing distance measures on HFSs are based on vector operation, while the novel proposed HDSs are based on set operation. Secondly, a statistical method is proposed to compare different HDSs, and some important properties of the comparison method are introduced. Thirdly, the characteristics of the novel HDSs and the classical hesitant distances are studied comparatively. Finally, the practicality and validity of the HDSs on HFSs are illustrated through an urban road traffic state identification example.

Research on Traffic Flow Mathematical Model in Urban Traffic

In this paper, we take the urban road network as the object, a further study on the method of traffic signal self organization under the condition of traffic state identification and local congestion. In the urban road traffic state identification study, according to the need of traffic signal control, respectively from qualitative and quantitative point of view, the urban road traffic state is defined. In order to be able to obtain real-time traffic data as the foundation, design urban road traffic state evaluation index system. Based on the current mainstream micro traffic simulation software VB and VISSIM as the tool to build the experimental platform, and set up the UTC-CI (Urban Traffic Control for Congested Intersections) experimental environment, the above method is verified by simulation. The results show that the traffic state identification method and the related signal control method have the expected effect in reducing congestion duration.

Urban Road Traffic State Identification Algorithm Based On Particle Filter and Fuzzy Discrimination

Urban road traffic state identification is a key link to realize the intelligent transportation based on the Internet of Vehicles, and accurately positioning vehicles is the foundation to realize the traffic state identification. Aiming at the problem that GPS has signal blind area in positioning vehicles, a vehicle positioning algorithm based on particle filter was proposed, it could improve the traditional algorithm on degradation and large amount of calculations; Based on vehicle positioning, an urban road traffic state identification algorithm based on fuzzy discrimination was proposed, it could comprehensively consider multiple factors’ influence on traffic state. The experiment results show that the improved particle filter algorithm’s mean squared error has increased about 55.437% compared with GPS method, and the traffic state identification algorithm can accurately identify the traffic state of the study area, it can prove that the urban road traffic state identification algorithm based on particle filter and fuzzy discrimination is feasible and effective.