Short-term Traffic Prediction Research Articles

Short-term traffic flow prediction based on vehicle trip chain features

ABSTRACT Short-term traffic flow prediction can improve the efficiency of transportation operations. Historical data-driven prediction methods have been proved to perform well. However, saturated or oversaturated traffic operations cannot be accurately predicted based only on detector data from a single intersection. This study proposes a short-term traffic prediction method based on vehicle trip chain features. First, the video data is pre-processed and quality assessed. Then, vehicle trip chain features are mined to correlate upstream and downstream intersections.Convolutional neural networks and long-short-term-memory model are built next. The model is launched to train the predictor and output the traffic flow for all turns at each approach to the intersection. After cases we demonstrate that the prediction accuracy of CNNs-LSTM is usually better than other methods, especially during oversaturation. In addition, we demonstrate that vehicle trip chain features can improve prediction accuracy and shorten the time consumed by the model.

AST3DRNet: Attention-Based Spatio-Temporal 3D Residual Neural Networks for Traffic Congestion Prediction.

Traffic congestion prediction has become an indispensable component of an intelligent transport system. However, one limitation of the existing methods is that they treat the effects of spatio-temporal correlations on traffic prediction as invariable during modeling spatio-temporal features, which results in inadequate modeling. In this paper, we propose an attention-based spatio-temporal 3D residual neural network, named AST3DRNet, to directly forecast the congestion levels of road networks in a city. AST3DRNet combines a 3D residual network and a self-attention mechanism together to efficiently model the spatial and temporal information of traffic congestion data. Specifically, by stacking 3D residual units and 3D convolution, we proposed a 3D convolution module that can simultaneously capture various spatio-temporal correlations. Furthermore, a novel spatio-temporal attention module is proposed to explicitly model the different contributions of spatio-temporal correlations in both spatial and temporal dimensions through the self-attention mechanism. Extensive experiments are conducted on a real-world traffic congestion dataset in Kunming, and the results demonstrate that AST3DRNet outperforms the baselines in short-term (5/10/15 min) traffic congestion predictions with an average accuracy improvement of 59.05%, 64.69%, and 48.22%, respectively.

Network-level short-term traffic state prediction incorporating critical nodes: A knowledge-based deep fusion approach

The critical nodes (CNs) in urban transportation networks, defined as road entities (such as road segments or detectors in a road network) that present highly volatile traffic states, can significantly impact the overall traffic conditions. However, recent studies are unable to explicitly address CNs in traffic state prediction tasks. In this study, we develop a novel knowledge-based graph convolutional gated recurrent network incorporating critical nodes (KGCGRN-CN) to forecast the network-level short-term traffic states. The KGCGRN-CN model consists of three learning blocks: (i) a spectral graph convolution block that captures the network-level and CN-level spatial features in the data; (ii) a novel knowledge-based spatial feature fusion block that tailors the fusion of the network-level and the CN-level spatial features using geographical and traffic information of each road entity; and (iii) a temporal feature learning block, which captures the temporal patterns of the fused features. The KGCGRN-CN model is compared with several state-of-the-art benchmark models using a real-world freeway dataset. Results show that the developed model outperforms the benchmark models by a mean absolute error of 1.03 km/h on average. Further numerical experiments are conducted to demonstrate the effectiveness of the KGCGRN-CN model.

Urban Traffic Congestion Prediction: A Multi-Step Approach Utilizing Sensor Data and Weather Information

The continuous growth of urban populations has led to the persistent problem of traffic congestion, which imposes adverse effects on quality of life, such as commute times, road safety, and the local air quality. Advancements in Internet of Things (IoT) sensor technology have contributed to a plethora of new data streams regarding traffic conditions. Therefore, the recognition and prediction of traffic congestion patterns utilizing such data have become crucial. To that end, the integration of Machine Learning (ML) algorithms can further enhance Intelligent Transportation Systems (ITS), contributing to the smart management of transportation systems and effectively tackling traffic congestion in cities. This study seeks to assess a wide range of models as potential solutions for an ML-based multi-step forecasting approach intended to improve traffic congestion prediction, particularly in areas with limited historical data. Various interpretable predictive algorithms, suitable for handling the complexity and spatiotemporal characteristics of urban traffic flow, were tested and eventually shortlisted based on their predictive performance. The forecasting approach selects the optimal model in each step to maximize the accuracy. The findings demonstrate that, in a 24 h step prediction, variating Ensemble Tree-Based (ETB) regressors like the Light Gradient Boosting Machine (LGBM) exhibit superior performances compared to traditional Deep Learning (DL) methods. Our work provides a valuable contribution to short-term traffic congestion predictions and can enable more efficient scheduling of daily urban transportation.

Dimensions management of traffic big data for short-term traffic prediction on suburban roadways

Since intelligent systems were developed to collect traffic data, this data can be collected at high volume, velocity, and variety, resulting in big traffic data. In previous studies, dealing with the large volume of big traffic data has always been discussed. In this study, big traffic data were used to predict traffic state on a section of suburban road from Karaj to Chalous located in the north of Iran. Due to the many and various extracted features, data dimensions management is necessary. This management was accomplished using principal component analysis to reduce the number of features, genetic algorithms to select features influencing traffic states, and cyclic features to change the nature of features. The data set obtained from each method is used as input to the models. The models used include long short-term memory, support vector machine, and random forest. The results show that using cyclic features can increase traffic state prediction's accuracy than the model, including all the initial features (base model). Long short-term memory model with 71 cyclic features offers the highest accuracy, equivalent to 88.09%. Additionally, this model's reduced number of features led to a shorter modelling execution time, from 456 s (base model) to 201 s.

A Novel Deep Stacking-Based Ensemble Approach for Short-Term Traffic Speed Prediction

A Novel Deep Stacking-Based Ensemble Approach for Short-Term Traffic Speed Prediction

A Machine Learning Scheme for Speed Prediction in Intelligent Transportation Systems Using a Bi-LSTM Based Model

Congestion has become a big issue in major cities throughout the world. Numerous transportation activities have been affected, and travel times have increased as a result of many travelers spending lengthy hours on the road. To overcome these challenges, the Intelligent Transportation System (ITS), which provides efficient traffic service and management, has sparked widespread attention. Collection and analysis of traffic data has been made possible by the algorithms implemented by the ITS. Huge volumes of data are produced by the vast wide range of sensors used in the Internet of Things (IoT), enabling the collection of a variety of traffic information. Development of short-term traffic speed prediction, has been made possible using deep learning models such as Long Short Term Memory (LSTM) and Bidirectional LSTM. Numerous variables, including the weather, the state of the roads, and traffic congestion, can have long-term dependencies and influence traffic speed. The bidirectional architecture of Bi-LSTMs enables them to handle long-term dependencies in sequences and efficiently capture both past and future context in a sequence, which is crucial for producing accurate forecasts of traffic speed. In this paper, the upstream and downstream flow of traffic speed on various pathways has been investigated using a traffic path planning algorithm based on Bi-LSTM models. The algorithm considers the factors affecting the flow of traffic at different seasons and time of the day and tries to predict the average speed associated to that path several timeslots ahead. The experimental results demonstrated that the Bi-LSTM model has the benefit of predicting speed for various timeslots while retaining a high level of accuracy.

A Novel Short-Term Traffic Prediction Model Based on SVD and ARIMA With Blockchain in Industrial Internet of Things

With the construction and development of smart cities, accurate and real-time traffic prediction plays a vital role in urban traffic. However, traffic data has the characteristics of non-linearity, non-stationary and complex structure, so traffic prediction has always been a challenging problem. The traditional statistical model is good at dealing with linear data and poor at dealing with nonlinear data. Although the ability to capture nonlinear data has improved, the deep learning approach has difficulty in meeting the real-time requirements of traffic prediction. To solve the above challenges, we propose a novel approach based on the Autoregressive Integrated Moving Average model (ARIMA) model and combining empirical mode decomposition (EMD) and singular value decomposition (SVD) technology, i.e., ESARIMA. This method first uses EMD to stabilize the traffic data, then uses SVD to compress data and reduce the noise, so as to improve the efficiency and accuracy of ARIMA model in predicting traffic flow. Finally, we use real datasets to verify the feasibility of ESARIMA. The experimental results show that our method outperforms state-of-art baselines.

Performance analysis of machine learning methods for short-term traffic prediction

With the rapid development of urbanization and the rapid increase of the number of motor vehicles, the problem of urban traffic congestion has become increasingly prominent. The accurate prediction of short-term traffic flow is considered as a promising solution, which can provide a key decision-making basis for route planning and traffic flow scheduling, so that can greatly alleviate or even prevent congestion. Researchers have used many machine learning methods to predict traffic flow, but few people pay attention to the boundaries of different machine algorithms. In this paper, we use AdaBoost, Random Forest, SVM and BP neural network to predict short-term traffic flow in California, which aims to compare the differences in prediction performance of different algorithms and analyze their potential reasons. The results show that, the integration methods such as AdaBoost and Random Forest are quite appropriate to solve the short-term traffic flow, which can obtain an accuracy more than 95%, while prediction made by SVM is less precise than the two aforementioned methods with a 79% accuracy. And BP neural network may be inappropriate if the parameters remain default. The different results are due to the periodicity of the database. Integration methods can recognize the periodicity while the SVM and BP neural network fail to do it. When employing the SVM and BP neural network, the datasets need to be divided within a period to avoid being disturbed by cyclically. Besides, the precise of BP neural network can be improved when adjusting the parameters to the optimal.

Temporal data-driven short-term traffic prediction: Application and analysis of LSTM model

With the development of the economy, the number of private cars has increased, and traffic congestion is becoming increasingly common. In order to prevent traffic congestion, short-term prediction of traffic flow is urgent. This article is based on the data of a Hangzhou elevated bridge from October 4th to 18th, 2015, with a timestamp of 5 minutes and a total of over 4000 pieces of data, to have the Long Short-Term Memory (LSTM) model to be trained and the following days traffic flow to be predicted. The outcomes show that the model has high predictive capability and can reliably forecast future short-term transportation passenger flow, accurately reflecting the trend of data changes. Compared with the Auto-Regressive Moving Average (ARIMA) method with a Mean-Square Error (MSE) of 36, the LSTM model has a MSE of 22, which indicates a smaller MSE, indicating a more accurate prediction performance of LSTM. The LSTM models prediction is beneficial for alleviating traffic congestion, providing great convenience for peoples daily travel, and greatly reducing their travel time.

Advanced Vehicle Safety: A Prototype Circuit for Accident Prevention and Emergency Response

In contemporary society, the pursuit of robust accident prevention, detection, and reporting systems is paramount, particularly within the context of vehicular safety. This study introduces a meticulously designed prototype circuit, adaptable for deployment across diverse vehicle types. This intelligent circuitry endeavors to mitigate accidents and safeguard human lives by employing a multifaceted approach.The system's primary functions encompass the verification of seat belt usage and the assessment of alcohol consumption by the driver. Additionally, it incorporates a vibration sensor to detect accidents promptly. Complementing these features, the integration of GPS and GSM modules facilitates the rapid notification of emergency services, ensuring prompt assistance in the event of an accident.The core of this system is an Arduino microcontroller, orchestrating the interconnected components to process data and trigger actions based on predefined conditions. The circuit's performance has been rigorously tested, initially through simulation in Proteus software, and subsequently via real-world hardware implementation. Comparative analysis of software and hardware results lends insights into the system's functionality and reliability.The overarching objective of this study is to curtail accidents arising from intoxicated driving, unforeseen driver fatigue, and road obstructions. In instances of accidents, the electronic apparatus employed has the capacity to dispatch spontaneous and precise distress signals to law enforcement and medical personnel, thereby expediting casualty recovery and potentially saving lives. This research advances the fusion of technology and safety measures to augment road safety comprehensively.[6] Upadhyay, V., Gupta, S., Chaturvedi, S. and Singh, D. [2020], ‘Integrated accident prevention detectionand response system (iapdrs)’,International journal of engineering and advanced technology9(3), 2086–2089.[7] Vitkar, S. P., Banare, A. and Nadar, J. [2022], ‘Conceptual framework for accident detection and pre-vention’,Journal of Pharmaceutical Negative Resultspp. 7449–7455.[8] Wu, C., Zhang, P., Zhang, Z., Zheng, W., Xu, B., Wang, W., Yu, Z., Dai, X., Zhang, B. and Zang, K. [2023],‘Slip partitioning and crustal deformation patterns in the tianshan orogenic belt derived from gpsmeasurements and their tectonic implications’,Earth-Science Reviewsp. 104362.[9] Xu, X., Hu, X., Zhao, Y., Lü, X. and Aapaoja, A. [2023], ‘Urban short-term traffic speed prediction withcomplicated information fusion on accidents’,Expert Systems with Applicationsp. 119887.

Distributional prediction of short-term traffic using neural networks

Neural network (NN)-based models have recently achieved outstanding results in short-term traffic prediction. However, most of these are based on the regression approach and trained to generate a single data point as a predicted value for future timesteps, which does not provide information on prediction uncertainty and limits its performance under different traffic conditions. To solve this problem, this study proposes a novel, high-dimensional distributional prediction (HDP) framework. This method has been validated by a series of experiments using the Caltrans Performance Measurement System dataset and four widely used NN models. The results suggest that the proposed HDP scheme can help existing NN structures to (1) generate adaptive distributional predictions for quantifying the uncertainty of multiple targets, and (2) gain better point prediction in terms of accuracy and robustness. Furthermore, we demonstrate that predicted speed distributions can be used for travel time estimation, outperforming other traditional methods in unexpected traffic conditions such as traffic incidents.

Spatial–temporal multi-feature fusion network for long short-term traffic prediction

Spatial–temporal multi-feature fusion network for long short-term traffic prediction

Urban short-term traffic speed prediction with complicated information fusion on accidents

Urban short-term traffic speed prediction with complicated information fusion on accidents

Few-Shot traffic prediction based on transferring prior knowledge from local network

Short-term traffic prediction has been widely studied in the community of Intelligent Transport Systems for decades. Despite the advances in machine learning-based prediction techniques, a challenging problem that affects the applications of such methods in practice is the prevalence of insufficient data across an entire road network. To address this few-shot traffic prediction problem at a local network scale, we develop a hybrid framework in conjunction with the prior knowledge transferring algorithm and two widely used models, i.e. Long-short Term Memory and Spatial–Temporal Graph Convolutional Neural Network. The proposed modelling framework is trained and tested using five-minute interval traffic flow data collected from London under different few-shot learning scenarios. Results show that transferring local network prior knowledge can improve the accuracy of both one-step prediction and multi-step prediction under inadequate data conditions, regardless of the deep-learning tool used.

Multidirectional short-term traffic volume prediction based on spatiotemporal networks

Multidirectional short-term traffic volume prediction based on spatiotemporal networks

AGNP: Network-wide short-term probabilistic traffic speed prediction and imputation

The data-driven Intelligent Transportation System (ITS) provides great support to travel decisions and system management but inevitably encounters the issue of data missing in monitoring systems. Hence, network-wide traffic state prediction and imputation is critical to recognizing the system level state of a transportation network. Abundant research works have adopted various approaches for traffic prediction and imputation. However, previous methods ignore the reliability analysis of the predicted/imputed traffic information. Thus, this study originally proposes an attentive graph neural process (AGNP) method for network-level short-term traffic speed prediction and imputation, simultaneously considering reliability. Firstly, the Gaussian process (GP) is used to model the observed traffic speed state. Such a stochastic process is further learned by the proposed AGNP method, which is utilized for inferring the congestion state on the remaining unobserved road segments. Data from a transportation network in Anhui Province, China, is used to conduct three experiments with increasing missing data ratio for model testing. Based on comparisons against other machine learning models, the results show that the proposed AGNP model can impute traffic networks and predict traffic speed with high-level performance. With the probabilistic confidence provided by the AGNP, reliability analysis is conducted both numerically and visually to show that the predicted distributions are beneficial to guide traffic control strategies and travel plans.

Short-Term Traffic Speed Prediction for Multiple Road Segments

Short-Term Traffic Speed Prediction for Multiple Road Segments

A Novel STFSA-CNN-GRU Hybrid Model for Short-Term Traffic Speed Prediction

Short-term traffic speed prediction is fundamental to intelligent transportation systems (ITS), and the accuracy of the model largely determines the performance of real-time traffic control and management. In this study, a short-term traffic speed prediction method based on the spatial-temporal analysis of traffic flow and a combined deep-learning model, and a hybrid spatial-temporal feature selection algorithm (STFSA) of a convolutional neural network–gated recurrent unit (CNN-GRU)) is initially developed. Specifically, the STFSA is firstly employed to reconstruct the spatial-temporal matrix of traffic speed based on temporal continuity and spatial characteristics, and then this matrix is considered as the input feature of the prediction model. After this, the nonlinear fitting ability of the CNN is adopted to extract deep features from the convolutional and pooling layers for model training. Finally, by combining the timing and long-range dependence of the captured data with the forward GRU and the reverse GRU, the accuracy of the prediction result is further improved. The validity of the proposed model can be verified by comparing the prediction results with the actual traffic data. Accordingly, in the case study, the performance is compared with various benchmark methods under the same prediction scenario, verifying the superiority of the proposed model.

Improving Traffic Safety through Traffic Accident Risk Assessment

The continuous development of sensors and the Internet of Things has produced a large amount of traffic data with location information. The improvement of traffic safety benefits from the availability of traffic accident data. Managers can patrol and control relevant areas in advance with limited police resources, according to the short-term traffic accident predictions. As a result, the possibility of accidents can be reduced, and the level of traffic safety can be improved. The traditional approach to accident prediction relies too much on the subjective experience of traffic managers. Inspired by the deep learning technology in the field of computer vision, this study first divides the road network into regular grids and takes the number of traffic accidents in each grid as the pixel value of an image. Then, a traffic accident prediction approach based on a bi-directional ConvLSTM U-Net with densely connected convolutions (BCDU-Net) is proposed. This method mines the regular information hidden in the accident data and introduces densely connected convolutions to further extract the deep spatial-temporal features contained in the traffic accident sequence. Thus, the issues of gradient disappearance and model over-fitting caused by the traditional model in model training can be avoided. Finally, the simulation experiment is carried out on the historical traffic accident data of Yinzhou District, Ningbo City. Results show that BCDU-Net has better accuracy and precision than other models in three data sets: motor vehicle accidents, non-motor vehicle accidents, and single-vehicle accidents. Therefore, the BCDU-Net is more suitable for traffic accident prediction and has good application prospects for improving road safety.