Traffic State Recognition Research Articles

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.

An Efficient Feature Augmentation and LSTM-Based Method to Predict Maritime Traffic Conditions

The recent emergence of futuristic ships is the result of advances in information and communication technology, big data, and artificial intelligence. They are generally autonomous, which has the potential to significantly improve safety and drastically reduce operating costs. However, the commercialization of Maritime Autonomous Surface Ships requires the development of appropriate technologies, including intelligent navigation systems, which involves the identification of the current maritime traffic conditions and the prediction of future maritime traffic conditions. This study aims to develop an algorithm that predicts future maritime traffic conditions using historical data, with the goal of enhancing the performance of autonomous ships. Using several datasets, we trained and validated an artificial intelligence model using long short-term memory and evaluated the performance by considering several features such as the maritime traffic volume, maritime traffic congestion fluctuation range, fluctuation rate, etc. The algorithm was able to identify features for predicting maritime traffic conditions. The obtained results indicated that the highest performance of the model with a valid loss of 0.0835 was observed under the scenario with all trends and predictions. The maximum values for 3, 6, 12, and 24 days and the congestion of the gate lines around the analysis point showed a significant effect on performance. The results of this study can be used to improve the performance of situation recognition systems in autonomous ships and can be applied to maritime traffic condition recognition technology for coastal ships that navigate more complex sea routes compared to ships navigating the ocean.

An Equivalent Consumption Minimization Strategy for a Parallel Plug-In Hybrid Electric Vehicle Based on an Environmental Perceiver.

An energy management strategy is a key technology used to exploit the energy-saving potential of a plug-in hybrid electric vehicle. This paper proposes the environmental perceiver-based equivalent consumption minimization strategy (EP-ECMS) for parallel plug-in hybrid vehicles. In this method, the traffic characteristic information obtained from the intelligent traffic system is used to guide the adjustment of the equivalence factor, improving the environmental adaptiveness of the equivalent consumption minimization strategy (ECMS). Two main works have been completed. First, a high-accuracy environmental perceiver was developed based on a graph convolutional network (GCN) and attention mechanism to complete the traffic state recognition of all graph regions based on historical information. Moreover, it provides the grade of the corresponding region where the vehicle is located (for the ECMS). Secondly, in the offline process, the search for the optimal equivalent factor is completed by using the Harris hawk optimization algorithm based on the representative working conditions under various grades. Based on the identified traffic grades in the online process, the optimized equivalence factor tables are checked for energy management control. The simulation results show that the improved EP-ECMS can achieve 7.25% energy consumption optimization compared with the traditional ECMS.

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 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.

Automatic Traffic State Recognition Based on Video Features Extracted by an Autoencoder

Video surveillance has become an important measure of urban traffic monitoring and control. However, due to the complex and diverse video scenes, traffic data extraction from original videos is a sophisticated and difficult task, and corresponding algorithms are of high complexity and calculation cost. To reduce the algorithm complexity and subsequent computation cost, this study proposed an autoencoder model which effectively reduces the video dimension by optimizing structural parameters; thus several traffic recognition models can conduct image processing work based on dimension-reduced videos. Firstly, an optimal autoencoder model A ∗ with five hidden layers was constructed. Then, it was combined with a linear classifier, support vector machine, deep neural network, DNN linear classification method, and the k-means clustering method; thus, five traffic state recognition models were constructed: A ∗ -Linear, A ∗ -SVM, A ∗ -DNN, A ∗ -DNN_Linear, and A ∗ -k-means. Train and test results show that the accuracy rate and recall rate of A ∗ -linear, A ∗ -SVM, A ∗ -DNN, and A ∗ -DNN_Linear were 94.5%–97.1%, and the F1 score was 94.4%–97.1%; besides, the accuracy rate, recall rate, and F1 score of A ∗ -k-means were all approximately 95%, which suggests that the combination of the autoencoder A ∗ and common classification or clustering methods achieve good recognition performance. Comparison was also implemented among the five models proposed above and four CNN-based models such as AlexNet, LeNet, GoogLeNet, and VGG16, which shows that the five proposed modes achieve F1 scores of 94.4%–97.1%, while the four CNN-based models achieve F1 scores of 16.7%–94%, indicating that the proposed light weight design methods outperform more complex CNN-based models in traffic state recognition.

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 Two-Layer Risky Driver Recognition Model With Context Awareness

For autonomous vehicles and intelligent connected vehicles, the real-time recognition of risky drivers can play an important role in traffic accident prevention. However, the external environment substantially impacts driving behavior and driving risk and is usually costly to acquire. Existing risky driver recognition models often ignore external environment information or assume this information is given. We propose two hierarchical two-layer context-aware machine learning structures. The first layer can speculate external context, for example, traffic states. The second layer recognizes risky drivers based on the contextual information speculated from the first layer. The German Highway Drone Dataset is used to establish risky driver recognition and traffic state recognition models. Rear-end collision risk and side collision risk are evaluated for each vehicle. Drivers with high collision risk are labeled as risky drivers. By analyzing vehicle trajectory data from three traffic states: free-flow, saturated, and congested, we find that traffic states have a significant influence on vehicle’s longitudinal speed, lateral speed, longitudinal acceleration/deceleration, and collision risk. Six classifiers, including SVM, KNN, RF, Adaboost, Extra trees, and XGBoost, are applied to train recognition models. Results show that the proposed structures can significantly improve model’s ability to recognize risky drivers.

Automatic Traffic State Recognition from Videos Based on Autoencoder and k -Means Clustering

An automatic video traffic state recognition method based on an autoencoder and k-means clustering is proposed to timely and effectively recognize road traffic state. First, candidate autoencoders are established through a reasonable optimization of the structural parameters on the input data dimensions, number of hidden layers, and dimensions of dimension-reduced data through cross-examination. Then, three image data sets are formed with 1 500-4 500 sample images. On this basis, the candidate autoencoders are trained and tested; thus, the best autoencoder AE* is proposed according to precision, recall, and F1-value. Lastly, four traffic state recognition models are constructed by combining AE* with k-means clustering, Support Vector Machine (SVM), Linear Classifier, and DNN Linear Classifier (Deep Neural Network with Linear Classifier), which are named as AE*-kmeans, AE*-SVM, AE*-Linear, and AE*-DNN_Linear, respectively. The models are trained and tested on the basis of the three image data sets. Results show that the four models' average precision in terms of precision and recall is 91.9%-92.7%, and their average recall is 91.6%-92.6%, while AE*-kmeans performs best or second to best in terms of precision and recall. With regard to the comprehensive evaluation index F1-value, AE*-kmeans achieves 92.4%, a little lower than 92.7% of AE*-SVM, and better than AE*-DNN_Linear (92.1%) and AE*-Linear (91.8%). Given that k-means is an unsupervised clustering method, compared with AE*-SVM, AE*-Linear, and AE*-DNN_Linear, AE*-kmeans can reduce the workload, such as manual data calibration and supervised training and cut down calculation cost. Meanwhile, AE*-kmeans also obtains a good traffic state recognition result. Therefore, this mechanism has high practical significance for an accurate real-time extraction of a video traffic status.

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.

Utilizing Artificial Neural Network in GPS-Equipped Probe Vehicles Data- Based Travel Time Estimation

Real-time traffic status information provides good references for urban traffic control and management. Travel time is easy to understand and widely employed in representing traffic status. With significantly improved positioning accuracy and coverage, trajectory data collected from GPS-equipped probe vehicles have great potential for traffic state recognition. This paper presents a machine learning enabled travel time estimation method based on the GPS-equipped probe vehicles data. This research considers the spatial-temporal relevancy while solving the travel time allocation problem: the travel time of target segment might be associated with its previous travel times and/or the traffic states of nearby relevant segments. After data normalization and network clustering, an artificial neural network (ANN) algorithm considering such spatial-temporal relevancy was conducted to infer the travel time distribution among the traveled segments within one path. Furthermore, a weighted summation of the travel time estimation result from various trajectories was calculated to better represent the segment travel time in one time step. The proposed method was evaluated by evaluating the estimation results with automatic vehicle identification obtained ground truth. The experimental results illustrated that by utilizing the ANN to consider the spatial-temporal relevancy, the proposed method is effective and efficient in estimating the travel time.

Intelligent energy management of a fuel cell vehicle based on traffic condition recognition

This paper presents a methodological approach for intelligent control of fuel cell vehicles based on traffic condition recognition. For this purpose, employing an extensive real driving pattern database, a six-mode representative traffic condition is developed for the city of Tehran by means of fuzzy subtractive clustering approach. Subsequently, an adaptive fuzzy logic controller is designed, with the assistance of particle swarm optimization algorithm. Finally, a traffic condition recognition algorithm is proposed to establish the most probable driving mode. The fuzzy logic controller is employed as a real-time controller and its modes are singled out with respect to the traffic condition recognition algorithm results. Moreover, effectiveness of the proposed controller has been examined during several real driving periods containing various traffic conditions. Simulation results prove successful performance of the proposed intelligent controller under different traffic conditions according which a nine-to-seventeen percent fuel consumption improvement has been achieved.

Research on Traffic Condition Recognition Algorithm Based on the Internet of Things

The article provides an overview of integration in the field of Internet technology and its trend, combined with the successful application of intelligent transportation system case system provides next - generation model of intelligent transportation system based on Internet of Things, details the functions and features of each subsystem, a case study of typical traffic guidance applications, describes the model and key technology of the Internet of its implementation.

A Fuzzy Logic Method of Traffic State Recognition on Urban Traffic Networks

The study object is traffic flow on main road of urban traffic networks, the traffic condition is recognized by traffic flow theory and fuzzy logic method. The average space speed is a variable of the fact flow function, the road congestion degree is described by the ratio of fact flow and traffic capacity; the ratio of congestion time length and total time length is the congestion frequency. Considering congestion degree and congestion frequency, a fuzzy logic method is used to describe the traffic state by three grades: free, congestion and serious congestion. At last, the numerical example is given to analyze traffic state.

Traffic condition recognition using the [formula omitted]-means clustering method

This paper presents a methodological approach to traffic condition recognition, based on driving segment clustering. Traffic condition recognition has many applications to various areas, such as intelligent transportation, adaptive cruise control, pollutant emissions dispersion, safety, and intelligent control strategies in hybrid electric vehicles. This study focuses on the application of driving condition recognition to the intelligent control of hybrid electric vehicles. For this purpose, driving features are identified and used for driving segment clustering, using the k-means clustering algorithm. Many combinations of driving features and different numbers of clusters are evaluated, in order to achieve the best traffic condition recognition results. The results demonstrate that traffic conditions can be correctly recognized in 87 percent of situations using the proposed approach.