Short-term Traffic Forecasting Research Articles

ST-DAGCN: A spatiotemporal dual adaptive graph convolutional network model for traffic prediction

Accurately predicting traffic flow characteristics is crucial for effective urban transportation management. Emergence of artificial intelligence has led to the surge of deep learning methods for short-term traffic forecast. Notably, Graph Convolutional Neural Networks (GCN) have demonstrated remarkable prediction accuracy by incorporating road network topology into deep neural networks. However, many existing GCN-based models are based on the premise that the graph network is static, which may fail to do justice in replicating the situations in the real World. On one hand, real road networks are dynamic and undergo changes such as road maintenance and traffic control, leading to altered network structures over time. On the other hand, relationships between road sections can fluctuate due to factors like traffic accidents, weather conditions, and other events, which can significantly impact traffic patterns and result in inaccurate predictions if a static network and static relationships between nodes are assumed. To address these challenges, we propose the spatiotemporal dual adaptive graph convolutional network (ST-DAGCN) model for spatiotemporal traffic prediction, which utilizes a dual-adaptive adjacency matrix comprising both a static and a dynamic graph structure learning matrix. The dual-adaptive mechanism can adaptively learn the global features and the local dynamic features of the traffic states by updating the correlations of nodes at each prediction step, while the gated recurrent unit (GRU), which is also a component of the model, extracts the temporal dependencies of traffic data. Through a comprehensive comparison analysis on two real-world traffic datasets, our model has achieved the highest prediction accuracy when compared to other advanced models.

Spatial matrices for short-term traffic forecasting based on time series

Spatial matrices for short-term traffic forecasting based on time series

Unlocking the Full Potential of Deep Learning in Traffic Forecasting Through Road Network Representations: A Critical Review

Research in short-term traffic forecasting has been blooming in recent years due to its significant implications in traffic management and intelligent transportation systems. The unprecedented advancements in deep learning have provided immense opportunities to leverage traffic data sensed from various locations of the road network, yet significantly increased the models’ complexity and data and computational requirements, limiting the actionability of the models. Consequently, the meaningful representation of traffic flow data and the road network has been highlighted as a key challenge in improving the efficiency, as well as the accuracy and reliability of forecasting models. This paper provides a systematic review of literature dedicated to spatiotemporal traffic forecasting. Three main representation approaches are identified, namely the stacked vector, image/grid, and graph, and are critically analyzed and compared in relation to their efficiency, accuracy and associated modeling techniques. Based on the findings, future research directions in traffic forecasting are proposed, aiming to increase the adoption of the developed models in real-world applications.

Review on short-term traffic flow prediction methods under big data

The goal of traffic forecast is to predict the related traffic situation in the future according to the historical concept. The predicted angle can be divided into short-term prediction and long-term prediction. This method can be used to solve the increasingly serious urban traffic congestion problem, and researchers have proposed a deep learning model to help decision makers in the field of traffic control. It has made great contributions to improving future road capacity and optimizing intelligent transportation services. In this paper, short-term traffic forecast related documents under big data are helpful after sorting out, and the traffic flow data characteristics of intelligent transportation system and related correction methods are analysed. Secondly, it then classifies the applications involved by big data algorithm calculation according to various related principles, and summarizes the prediction accuracy, computational complexity, applicable interval and computation time of each type of algorithm in an overview. According to the relevant data, the combination forecasting model is effectively diversified, and combined with the related combination forecasting, I hope to improve the forecasting accuracy of the future development prospect.

Multivariate Correlation Matrix-Based Deep Learning Model With Enhanced Heuristic Optimization for Short-Term Traffic Forecasting

Accurately capturing the spatial correlations of traffic network significantly benefits short-term traffic forecasting. Some existing works represent spatial correlations in a simple one-dimensional space, but they cannot represent the real spatial correlations among sensors comprehensively. The other existing works represent the spatial correlations through grid-based method, but the local correlation of constructed spatial map is too superficial to extract deep spatial features effectively. Therefore, a novel deep learning model is proposed, which aims to represent the spatial correlations more effectively through a new correlation matrix structure. In the proposed model, the correlations among sensors are calculated from multiple perspectives to construct the speed, volume, and occupancy correlation matrices respectively. Then, considering that highly correlated sensors are close in the spatial dimension, an enhanced heuristic optimization algorithm is proposed to evolve these three correlation matrices into optimal ones by reorganizing the highly correlated sensors into each others neighborhood. Finally, the three optimal correlation matrices are combined to form a three-dimensional multivariate correlation matrix characterized by locally high correlation, which is beneficial to exploit the deep spatial features of traffic network. The experiments show that the proposed model has better accuracy and stability than other commonly used baseline models.

Short Term Traffic Flow Prediction Using Hybrid Deep Learning

Traffic flow prediction in urban areas is essential in the Intelligent Transportation System (ITS). Short Term Traffic Flow (STTF) prediction impacts traffic flow series, where an estimation of the number of vehicles will appear during the next instance of time per hour. Precise STTF is critical in Intelligent Transportation System. Various extinct systems aim for short-term traffic forecasts, ensuring a good precision outcome which was a significant task over the past few years. The main objective of this paper is to propose a new model to predict STTF for every hour of a day. In this paper, we have proposed a novel hybrid algorithm utilizing Principal Component Analysis (PCA), Stacked Auto-Encoder (SAE), Long Short Term Memory (LSTM), and K-Nearest Neighbors (KNN) named PALKNN. Firstly, PCA removes unwanted information from the dataset and selects essential features. Secondly, SAE is used to reduce the dimension of input data using one-hot encoding so the model can be trained with better speed. Thirdly, LSTM takes the input from SAE, where the data is sorted in ascending order based on the important features and generates the derived value. Finally, KNN Regressor takes information from LSTM to predict traffic flow. The forecasting performance of the PALKNN model is investigated with Open Road Traffic Statistics dataset, Great Britain, UK. This paper enhanced the traffic flow prediction for every hour of a day with a minimal error value. An extensive experimental analysis was performed on the benchmark dataset. The evaluated results indicate the significant improvement of the proposed PALKNN model over the recent approaches such as KNN, SARIMA, Logistic Regression, RNN, and LSTM in terms of root mean square error (RMSE) of 2.07%, mean square error (MSE) of 4.1%, and mean absolute error (MAE) of 2.04%.

Short-term traffic forecasting model: prevailing trends and guidelines

Abstract The design parameters serve as an integral part of developing a robust short-term traffic forecasting model. These parameters include scope determination, input data preparation, output parameters and modelling techniques. This paper takes a further leap to analyse the recent trend of design parameters through a systematic literature review based on peer-reviewed articles up to 2021. The key important findings are summarized along with the challenges of performing short-term traffic forecasting. Intuitively, this paper offers insights into the next wave of research that contributes significantly to industries.

Short-Term Traffic Congestion Prediction Using Hybrid Deep Learning Technique

A vital problem faced by urban areas, traffic congestion impacts wealth, climate, and air pollution in cities. Sustainable transportation systems (STSs) play a crucial role in traffic congestion prediction for adopting transportation networks to improve the efficiency and capacity of traffic management. In STSs, one of the essential functional areas is the advanced traffic management system, which alleviates traffic congestion by locating traffic bottlenecks to intensify the interpretation of the traffic network. Furthermore, in urban areas, accurate short-term traffic congestion forecasting is critical for designing transport infrastructure and for the real-time optimization of traffic. The main objective of this paper was to devise a method to predict short-term traffic congestion (STTC) every 5 min over 1 h. This paper proposes a hybrid Xception support vector machine (XPSVM) classifier model to predict STTC. Primarily, the Xception classifier uses separable convolution, ReLU, and convolution techniques to predict the feature detection in the dataset. Secondarily, the support vector machine (SVM) classifier operates maximum marginal separations to predict the output more accurately using the weight regularization technique and a fine-tuned binary hyperplane mechanism. The dataset used in this work was taken from Google Maps and comprised snapshots of Bangalore, Karnataka, taken using the Selenium automation tool. The experimental outcome showed that the proposed model forecasted traffic congestion with an accuracy of 97.16%.

HMIAN: A Hierarchical Mapping and Interactive Attention Data Fusion Network for Traffic Forecasting

With the development of intelligent transportation system (ITS), the vital technology of ITS, short-term traffic forecasting, gains increasing attention. However, the existing prediction models ignore the impact of urban functional zones (FZs) on traffic data, resulting in inaccurate extractions of dynamic spatial relationships from network. Furthermore, how to calculate the influence of external factors, such as weather and holidays on traffic is an unsolved problem. This article proposes a spatio-temporal hierarchical mapping and interactive attention network (HMIAN), which extracts the spatial features from traffic network by constructing FZs, and designs an effective external factors fusion method. HMIAN uses the hierarchical mapping structure to aggregate the roads into FZs, calculate the interaction between FZs and feed this information back to the spatial features. And the interactive attention mechanism is utilized to fuse the traffic data with external factors effectively, and extracts temporal features. In addition, some experiments were carried out on three real traffic data sets. First, experiment results show the better prediction performance of the proposed model compared with other existing methods in a complex traffic network. Second, the longitudinal comparison experiment verifies that the hierarchical mapping structure is effective in extracting spatial features in a complex road network. Finally, the influence of different external factors and fusion methods on traffic prediction are compared, which provides a consult for subsequent research on the influence of external factors.

Feature engineering methodology for congestion forecasting

Short-term traffic forecasting is a key element in proactive traffic management, e.g., mitigating the negative effect of impending congestion through appropriate capacity allocation at signalized intersections. In this study, we develop a data-driven methodology for reliably and robustly predicting impending stable congestion. By incorporating feature engineering techniques into an iterative machine learning process, we develop a prediction model that can be intuitively understood by traffic experts and is amenable to diagnostics during implementation. Our iterative machine learning process combines the embedded and filter approaches for feature selection with the use of expert knowledge to create aggregative input variables. The embedded approach is represented by application of a decision tree algorithm, while the filter approach is reflected in use of the mean decrease in accuracy output of a random forest algorithm for identifying expressive variables. We tested the methodology by applying it to field data from a sub-network in Tel Aviv. We demonstrated a reduction in the number of decision tree input variables from 66 raw variables to the five most effective aggregative ones, while achieving statistically significant improvement in all performance indicators. The identification rate of stable congestion increased from 65% to 74% while the robustness of the results was enhanced: the standard deviations of the identification and false alarm rates fell from 8% to 3%, respectively, to 5% and 2%. • Short-term traffic forecasting is a key element in mitigating the negative effect of impending urban congestion. • A machine learning process for reliably predicting impending congestion was developed by using feature engineering. • Aggregative input variables were created by combining embedded and filter approaches with the use of expert knowledge. • The proposed methodology significantly increases both the congestion identification rate and the robustness of the results. • The final prediction model can be intuitively understood by traffic experts.

A Universal Framework of Spatiotemporal Bias Block for Long-Term Traffic Forecasting

Recent studies have demonstrated the great success of graph convolutional networks in short-term traffic forecasting (e.g., 15–30 min ahead) tasks by capturing dependencies in road network structure. Based on these models, long-term forecasting can be achieved by two approaches: (1) recursively generating a one-step-ahead prediction and (2) adapting the models to sequence-to-sequence (seq2seq) learning. However, in practice, these two approaches often show poor performance in long-term forecasting tasks. The recursive approach suffers from the error accumulation problem, as the model is trained based on one-step-ahead loss. On the other hand, seq2seq shows convergence issues that limit its application. To address the issues for long-term forecasting, in this paper, we propose a universal framework that directly transforms any existing state-of-the-art models for one-step-ahead prediction to achieve more accurate long-term forecasting. The proposed framework consists of two components—a base model and a bias block. The base model is assumed to be a well-trained state-of-the-art one-step-ahead forecasting model, and the bias block is constructed by a spatiotemporal graph neural network composed of gated temporal convolution layers and graph convolution layers. The base model and the bias block are residually-connected so that we can substantially reduce the training complexity. Extensive experiments are conducted on existing benchmark datasets. We experiment with several state-of-the-art models in the literature as base models, and our results demonstrate the ability of the proposed universal framework to greatly improve the long-term prediction accuracy for all models.

Enabling internet of things in road traffic forecasting with deep learning models

Integration of the latest technological advancements such as Internet of Things (IoT) and Computational Intelligence (CI) techniques is an active research area for various industrial applications. The rapid urbanization and exponential growth of vehicles has led to crowded traffic in cities. The deployment of IoT infrastructures for building smart and intelligent traffic management system greatly improves the quality and comfort of city dwellers. This work aims at building a cost effective IoT enabled traffic forecasting system using deep learning techniques. The case study experimentation is done in a real time traffic environment. The main contributions of this work include: (i) deploying road side sensor station built with ultrasonic sensor and Arduino Uno controller for obtaining traffic flow data (ii) building an IoT cloud system based on open source Thingspeak cloud platform for monitoring real time traffic (iii) performing short term traffic forecast using Recurrent Neural Network (RNN) models such as Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The performance of the prediction model is compared with the traditional statistical methods such as Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA) and Convolutional Neural Network (CNN). The results show good performance metrics with RMSE of 5.8, 7.9, 10.2 for LSTM model and 6.7, 8.6, 10.9 for GRU model for three different scenarios such as whole day, morning congested hour and evening congested hour datasets.

Survey of Decomposition-Reconstruction-Based Hybrid Approaches for Short-Term Traffic State Forecasting

Traffic state prediction provides key information for intelligent transportation systems (ITSs) for proactive traffic management, the importance of which has become the reason for the tremendous number of research papers in this field. Over the last few decades, the decomposition-reconstruction (DR) hybrid models have been favored by numerous researchers to provide a more robust framework for short-term traffic state prediction for ITSs. This study surveyed DR-based works for short-term traffic state forecasting that were reported in the past circa twenty years, particularly focusing on how decomposition and reconstruction strategies could be utilized to enhance the predictability and interpretability of basic predictive models of traffic parameters. The reported DR-based models were classified and their applications in this area were scrutinized. Discussion and potential future directions are also provided to support more sophisticated applications. This work offers modelers suggestions and helps to choose appropriate decomposition and reconstruction strategies in their research and applications.

Deep Learning for Road Traffic Forecasting: Does it Make a Difference?

Deep Learning methods have been proven to be flexible to model complex phenomena. This has also been the case of Intelligent Transportation Systems, in which several areas such as vehicular perception and traffic analysis have widely embraced Deep Learning as a core modeling technology. Particularly in short-term traffic forecasting, the capability of Deep Learning to deliver good results has generated a prevalent inertia towards using Deep Learning models, without examining in depth their benefits and downsides. This paper focuses on critically analyzing the state of the art in what refers to the use of Deep Learning for this particular Intelligent Transportation Systems research area. To this end, we elaborate on the findings distilled from a review of publications from recent years, based on two taxonomic criteria. A posterior critical analysis is held to formulate questions and trigger a necessary debate about the issues of Deep Learning for traffic forecasting. The study is completed with a benchmark of diverse short-term traffic forecasting methods over traffic datasets of different nature, aimed to cover a wide spectrum of possible scenarios. Our experimentation reveals that Deep Learning could not be the best modeling technique for every case, which unveils some caveats unconsidered to date that should be addressed by the community in prospective studies. These insights reveal new challenges and research opportunities in road traffic forecasting, which are enumerated and discussed thoroughly, with the intention of inspiring and guiding future research efforts in this field.

Robust-LSTM: a novel approach to short-traffic flow prediction based on signal decomposition

Intelligent transport systems need accurate short-term traffic flow forecasts. However, developing a robust short-term traffic flow forecasting approach is a challenging task due to the stochastic character of traffic flow. This study proposes a novel approach for short-term traffic flow prediction task, namely Robust Long Short Term Memory (R-LSTM) based on Robust Empirical Mode Decomposing (REDM) algorithm and Long Short Term Memory (LSTM). Short-term traffic flow data provided from the Caltrans Performance Measurement System (PeMS) database were used in the training and testing of the model. The dataset was composed of traffic data collected by 25 traffic detectors on different freeways’ main lanes. The time resolution of the dataset was set to 15 min, and the Hampel preprocessing algorithm was applied for outlier elimination. The R-LSTM predictions were compared with the state-of-the-art models, utilizing RMSE, MSE, and MAPE as performance criteria. Performance analyses for various periods show that R-LSTM is remarkably successful in all time periods. Moreover, developed model performance is significantly higher, especially during midday periods when traffic flow fluctuations are high. These results show that R-LSTM is a strong candidate for short-term traffic flow prediction, and can easily adapt to fluctuations in traffic flow. In addition, robust models for short-term predictions can be developed by applying the signal separation method to traffic flow data.

Estimate the limit of predictability in short-term traffic forecasting: An entropy-based approach

Accurate short-term traffic forecasting is the cornerstone for Intelligent Transportation Systems. In the past several decades, many models have been proposed to continuously improve the predictive accuracy. A key but unsolved question is whether there is a theoretical bound to the accuracy with which traffic can be predicted and whether that limit can be directly estimated from data. To answer this question, we use core concepts in information theory to derive the limit of predictability in short-term traffic forecasting. Theoretical analysis proves that conditional differential entropy poses a rigorous lower bound of negative-log-likelihood (NLL) for probabilistic models. And the continuous form of Fano’s theorem further gives a loose lower bound of mean-square-error (MSE) for deterministic models. Based on the special properties of traffic dynamics, two assumptions are made in the estimate of entropy metrics: cyclostationarity (traffic phenomena show strong periodicity) and localized spatial correlation (due to kinematic wave propagation). They allow formulating the limit of predictability as a function of longitudinal space and time-of-day which finds the most uncertain locations and periods solely from data. Experiments on univariate traffic accumulation forecasting and network-level speed forecasting show that the selected models, including some state-of-the-art deep learning models, indeed cannot outperform the estimated lower bounds but just approach them. The limit of predictability depends on time-of-day, network locations, observation range, and prediction horizon. The results reveal that the stochastic nature of traffic dynamics and improper assumptions on the prior distribution of output are two major factors that restrict the predictive performance. In summary, the proposed method estimates a trustworthy performance boundary for most traffic forecasting models. These conclusions are helpful for further studies in this domain.

Urban Safety: An Image-Processing and Deep-Learning-Based Intelligent Traffic Management and Control System.

With the rapid growth and development of cities, Intelligent Traffic Management and Control (ITMC) is becoming a fundamental component to address the challenges of modern urban traffic management, where a wide range of daily problems need to be addressed in a prompt and expedited manner. Issues such as unpredictable traffic dynamics, resource constraints, and abnormal events pose difficulties to city managers. ITMC aims to increase the efficiency of traffic management by minimizing the odds of traffic problems, by providing real-time traffic state forecasts to better schedule the intersection signal controls. Reliable implementations of ITMC improve the safety of inhabitants and the quality of life, leading to economic growth. In recent years, researchers have proposed different solutions to address specific problems concerning traffic management, ranging from image-processing and deep-learning techniques to forecasting the traffic state and deriving policies to control intersection signals. This review article studies the primary public datasets helpful in developing models to address the identified problems, complemented with a deep analysis of the works related to traffic state forecast and intersection-signal-control models. Our analysis found that deep-learning-based approaches for short-term traffic state forecast and multi-intersection signal control showed reasonable results, but lacked robustness for unusual scenarios, particularly during oversaturated situations, which can be resolved by explicitly addressing these cases, potentially leading to significant improvements of the systems overall. However, there is arguably a long path until these models can be used safely and effectively in real-world scenarios.

A Novel Hybrid Model for Predicting Traffic Flow via Improved Ensemble Learning Combined with Deep Belief Networks

The intelligent transportation system (ITS) plays an irreplaceable role in alleviating urban traffic congestion and realizing sustainable urban development. Accurate and efficient short-term traffic state forecasting is a significant issue in ITS. This study proposes a novel hybrid model (ELM-IBF) to predict the traffic state on urban expressways by taking advantage of both deep learning models and ensemble learning framework. First, a developed bagging framework is introduced to combine several deep belief networks (DBNs) that are utilized to capture the complicated temporal characteristic of traffic flow. Then, a novel combination method named improved Bayesian fusion (IBF) is proposed to replace the averaging method in the bagging framework since it can better fuse the prediction results of the component DBNs by assigning the reasonable weights to DBNs at each prediction time interval. Finally, the proposed hybrid model is validated with ground-truth traffic flow data captured by the remote traffic microwave sensors installed on the multiple road sections of 2nd Ring Road in Beijing. The experimental results illustrate that the ELM-IBF method can effectively capture sharp fluctuations in the traffic flow. Compared with several benchmark models (e.g., artificial neural network, long short-term memory neural network, and DBN), the ELM-IBF model reveals better performance in forecasting single-step-ahead traffic volume and speed. Additionally, it is proved that the ELM-IBF model is capable of providing stable and high-quality results in multistep-ahead traffic flow prediction.

Multistep traffic forecasting by dynamic graph convolution: Interpretations of real-time spatial correlations

Accurate and explainable short-term traffic forecasting is pivotal for making trustworthy decisions in advanced traffic control and guidance systems. Recently, deep learning approach, as a data-driven alternative to traffic flow model-based data assimilation and prediction methods, has become popular in this domain. Many of these deep learning models show promising predictive performance, but inherently suffer from a lack of interpretability. This difficulty largely originates from the inconsistency between the static input–output mappings encoded in deep neural networks and the dynamic nature of traffic phenomena. Under different traffic conditions, such as freely-flowing versus heavily congested traffic, different mappings are needed to predict the propagation of congestion and the resulting speeds over the network more accurately. In this study, we design a novel variant of the graph attention mechanism. The major innovation of this so-called dynamic graph convolution (DGC) module is that local area-wide graph convolutional kernels are dynamically generated from evolving traffic states to capture real-time spatial dependencies. When traffic conditions change, the spatial correlation encoded by DGC module changes as well. Using the DGC, we propose a multistep traffic forecasting model, the Dynamic Graph Convolutional Network (DGCN). Experiments using real freeway data show that the DGCN has a competitive predictive performance compared to other state-of-the-art models. Equally importantly, the prediction process in the DGCN and the trained parameters are indeed explainable. It turns out that the DGCN learns to mimic the upstream–downstream asymmetric information flow of typical road traffic operations. Specifically, there exists a speed-dependent optimal receptive field – which governs what information the DGC kernels assimilate – that is consistent with the back-propagation speed of stop-and-go waves in traffic streams. This implies that the learnt parameters are consistent with traffic flow theory. We believe that this research paves a path to more transparent deep learning models applied for short-term traffic forecasting.

Short-Term Traffic Forecasting: An LSTM Network for Spatial-Temporal Speed Prediction

Traffic forecasting remains an active area of research in the transport and data science fields. Decision-makers rely on traffic forecasting models for both policy-making and operational management of transport facilities. The wealth of spatial and temporal real-time data increasingly available from traffic sensors on roads provides a valuable source of information for policymakers. This paper adopts the Long Short-Term Memory (LSTM) recurrent neural network to predict speed by considering both the spatial and temporal characteristics of real-time sensor data. A total of 288,653 real-life traffic measurements were collected from detector stations on the Eastern Freeway in Melbourne/Australia. A comparative performance analysis among different models such as the Recurrent Neural Network (RNN) that has an internal memory that is able to remember its inputs and Deep Learning Backpropagation (DLBP) neural network approaches are also reported. The LSTM results showed average accuracies in the outbound direction ranging between 88 and 99 percent over prediction horizons between 5 and 60 min, and average accuracies between 96 and 98 percent in the inbound direction. The models also showed resilience in accuracies as the prediction horizons increased spatially for distances up to 15 km, providing a remarkable performance compared to other models tested. These results demonstrate the superior performance of LSTM models in capturing the spatial and temporal traffic dynamics, providing decision-makers with robust models to plan and manage transport facilities more effectively.