Traffic Congestion Prediction Research Articles

Bi-State Prediction Network Model for Mixed Traffic Congestion Prediction

Forecasting traffic congestion has become an important issue than ever before. Information provided by traffic congestion prediction methods becomes helpful to traffic management agencies to take suitable actions in due time. On the other hand, with prior information, passengers can plan their trip routes accordingly. In this paper, we tackle the problem of mixed traffic congestion prediction and develop a new Bi-State Prediction Network (BiSPNet). Two heads form BiSPNet. Each head information passed through the layers of three-dimensional convolution (Conv3D), two-dimensional convolutional long short-term memory (ConvLSTM2D), and two-dimensional convolution (Conv2D). Then the dense layer merges both head outputs. These layers capacitate the model to capture the characteristics of multiple inputs. The proposed model is validated by the ground truth data collected by the video cameras installed on the roadways in Delhi, India. Experimental results confirm that the prediction accuracy of the BiSPNet model surpasses that of several state-of-the-art benchmark models. In particular, the BiSPNet reduces mean absolute error by 53.23%, 40.95%, and 0.38%, respectively, in congestion score prediction for 5, 10, and 15-min time horizons compared to the mixed deep learning network (MDLNet). The source data and code can be accessed on GitHub at the following link: https://github.com/amanojup/ .

A Review on Highway Network System - Samruddhi Expressway

Highway networks serve as critical arteries of transportation infrastructure, facilitating the movement of goods and people across vast geographical regions. As traffic volumes continue to increase and technological advancements reshape transportation systems, there is a pressing need to optimize the efficiency, safety, and sustainability of highway networks. This abstract presents a comprehensive system approach aimed at addressing these challenges. The proposed highway network system integrates advanced technologies such as intelligent transportation systems (ITS), real-time traffic monitoring, data analytics, and machine learning algorithms to optimize traffic flow, minimize congestion, and enhance safety. By leveraging data from various sources including sensors, cameras, and connected vehicles, the system enables proactive traffic management and congestion prediction, allowing authorities to implement timely interventions and mitigate potential disruptions. Furthermore, the integration of artificial intelligence and machine learning algorithms enables predictive maintenance of infrastructure, identifying potential issues before they escalate into critical failures. This proactive approach not only reduces maintenance costs but also enhances the reliability and lifespan of highway assets. Moreover, the system prioritizes safety through the implementation of automated collision avoidance systems, adaptive speed control, and real-time incident management. By leveraging vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication technologies, the system enables vehicles to exchange critical information, such as hazardous road conditions or impending collisions, thereby reducing the risk of accidents and improving overall safety. Additionally, the system incorporates sustainability principles by optimizing traffic flow patterns to minimize fuel consumption and emissions, promoting eco-friendly transportation practices, and facilitating the integration of electric and autonomous vehicles into the existing infrastructure.

Advanced Data Science Applications in Vehicles: A Comprehensive Review

Purpose: This research paper aims to explore the transformative impact of data science applications within the automotive industry, with a focus on the evolution of vehicles into intelligent, connected, and autonomous entities. Through an extensive literature review and analysis of recent developments, the paper seeks to provide a comprehensive understanding of the current state and future prospects of data science in vehicles.
 Methodology: Advancements in sensor technologies, such as LiDAR, HD maps, radars, location tracking and cameras, are discussed, highlighting their role in data collection for applications like advanced driver assistance systems (ADAS) and autonomous driving. Relevant citations are provided. This subsection covers the collection and transmission of real-time data through telematics systems, showcasing their importance for predictive maintenance, fleet management, and personalized insurance programs. Citations support the presented information. The deployment of edge computing for real-time data processing in vehicles is discussed, emphasizing its significance for safety-critical applications like collision avoidance. Citations are provided to support the information. This section explores the application of machine learning algorithms to predict vehicle failures, optimize fuel efficiency, and analyze driver behavior. The importance of leveraging historical data to create accurate models is highlighted with supporting citations.
 Findings: The pivotal role of data science in enabling autonomous vehicles to navigate complex environments is discussed, emphasizing the use of machine learning models for real-time decision-making. Citations support the presented information. This subsection explores the integration of data science in traffic management systems, covering dynamic traffic signal control, congestion prediction, and route optimization. Citations support the findings related to traffic management applications. The paper discusses challenges associated with widespread implementation, including data privacy concerns, cybersecurity risks, and the need for standardized communication protocols. Additionally, potential future directions are outlined, such as the integration of blockchain for secure data sharing and the development of advanced human-machine interfaces.
 Unique Contribution to Theory, Practice and Policy: This research paper provides a well-rounded contribution by seamlessly integrating theoretical concepts, practical applications, and policy considerations in the realm of advanced data science applications in vehicles.

Traffic Congestion Prediction Using Feature Series LSTM Neural Network and a New Congestion Index

Large and expanding cities suffer from a traffic congestion problem that harms the environment, travelers, and the economy. This paper aims to predict short term traffic congestion on a road section of expressway in Delhi city. For this purpose, we first propose a traffic congestion index based on traffic speed and flow. Clustering techniques and the Greenshield’s model were used for the derivation of the congestion index. Using this congestion index, congested time intervals of each day and each location of a weekday were identified. This study also introduces a feature series long short-term memory neural network (FSLSTMNN), which links a long short-term memory (LSTM) layer to each feature. It is trained using the many heterogeneous traffic features data collected in Delhi city for the next five minutes of traffic flow and speed prediction. FSLSTMNN achieved the good capability to learn feature series data. We also trained several traditional and deep-learning models using the same traffic data. The FSLSTMNN reduces mean absolute error 12.90% and 17.13%, respectively, in speed and traffic flow prediction compared to the second good-performance long short-term memory neural network (LSTMNN). Finally, traffic congestion is predicted classwise (light, medium, and congested) using the developed congestion index and traffic speed and flow predicted by the FSLSTMNN. Predicted results are consistent with the measured field data. Study results confirm that the developed congestion index and FSLSTMNN can be used successfully to predict traffic congestion.

Traffic management approaches using machine learning and deep learning techniques: A survey

Traffic management is improved in cutting-edge smart cities using technologies such as machine learning and deep learning to streamline daily tasks and boost productivity. However, traffic management still suffers from challenging issues such as poor traffic congestion prediction, lack of traffic flow management, public transportation optimization, and emergency management. In this article, we provide a thorough understanding of the benefits, drawbacks, and practical implications of leveraging machine learning and deep learning in Traffic Management Systems (TMSs) by methodically reviewing and critically analysing various traffic management techniques. We also present a generic traffic management architecture that uses a set of assessment criteria to evaluate 20 recently proposed research prototypes (i.e., published since 2019). Finally, we highlight the ongoing challenges and prospective trajectories within the rapidly evolving domain of traffic management, underscoring the need to address emerging issues and directions in its dynamic development. This survey article offers insights that might help in efficiently tackling the issues posed by traffic management while maximizing the potential of machine learning and deep learning techniques. This survey can be a significant resource for researchers, policymakers, and practitioners.

Design and development of prime herder optimization based BiLSTM congestion predictor model in live video streaming

High-quality content for the user in video streaming services depends critically on the ability to predict the continuous user’s quality of experience (QoE). However, continuous QoE prediction has proven challenging due to the complexity imposed by the temporal dependencies in QoE data and the non-linear correlations among QoE impact elements. In this research congestion prediction model is developed using the prime herder optimization-based BiLSTM (PHO-based BiLSTM). The input database is first gathered from the NIMS and darpa99 week 1 database and, the data collection is analyzed and the packet information is extracted after that the extracted features are then fed into the optimized BiLSTM classifier to train the classifier. The classifier’s hyperparameters are successfully tuned by the recommended prime herder optimization, which is made by fusing the herding characteristics of a prime sheepdog and herder optimization. Based on the traffic congestion prediction achievements, at training percentage (TP) 90, the accuracy is 94.81%, specificity is 94.90%, and mean square error (MSE) is 4.91 respectively for D1, similarly based on D2 the accuracy is 95.62%, specificity is 95.96%, and MSE is 0.38 respectively.

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.

Improving Detection and Prediction of Traffic Congestion in VANETs: An Examination of Machine Learning

Improving Detection and Prediction of Traffic Congestion in VANETs: An Examination of Machine Learning

Efficient Road Traffic Video Congestion Classification Based on the Multi-Head Self-Attention Vision Transformer Model

Abstract Due to rapid population growth, traffic congestion has become one of the major issues in urban areas. The utilization of technology may help to address this issue. This paper proposes a new Multi-head Self-attention Vision Transformer (MSViT) based macroscopic approach, for road traffic congestion classification. To evaluate this approach, we use the UCSD (University of California San Diego) dataset that includes different weather conditions (clear, overcast and rainy) and different traffic scenarios (light, medium and heavy). The classification accuracy reached a high level of 99.76% with this dataset and 99.37% when night-mode frames are added to it. The proposed MSViT based method outperforms the state-of-the-art macroscopic and microscopic methods that have been evaluated using the same UCSD dataset, which makes it an efficient solution for traffic congestion prediction.

Aquila Optimizer-Based Hybrid Predictive Model for Traffic Congestion in an IoT-Enabled Smart City

Effective traffic congestion prediction is need of the hour in a modern smart city to save time and improve the quality of life for citizens. In this study, AB_AO (ARIMA Bi-LSTM using Aquila optimizer), a hybrid predictive model, is proposed using the most effective time-series data prediction statistical model ARIMA (Autoregressive Integrated Moving Average) and sequential predictive Deep Learning (DL) technique LSTM (Long Short-Term Memory) which helps in traffic congestion prediction with a minimum error rate. Also, the Aquila optimizer (AO) is used to elevate the adequacy of the AB_AO model. Three road traffic datasets of different cities from the “CityPulse EU FP7 project” are used to implement the proposed hybrid model. In a time-series dataset, two components need to be handled with care, i.e., linear and nonlinear. In this study, the ARIMA model has been used to manage linear components and Bi-LSTM is used to handle nonlinear components of the time-series dataset. The Aquila Optimizer (AO) is used for hyperparametric tuning to enhance the performance of Bi-LSTM. Error measurement parameters like the Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE) are used to validate the results. A detailed mathematical and empirical analysis is given to justify the performance of the AB_AO model using an ablation study and comparative analysis. The AB_AO model acquires more stable and precise results with MSE as 18.78, MAE as 3.18, and MAPE as 0.21 than other models. It may further help to predict the vehicle count on the road, which may be of great help in reducing wastage of time in traffic congestion.

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.

The Traffic Congestion Prediction Using Machine Learning

Around the world, traffic congestion is a major issue that affects everyday commutes, economic activity, and the sustainability of the environment. The goal of this research project is to use machine learning approaches to address the problem of traffic congestion. Through the utilization of an extensive dataset that encompasses several spatiotemporal parameters, including date, time, weather, and holiday indicators, the research creates predictive models that enable precise forecasting of traffic congestion levels. In order to determine how well machine learning predicts congestion dynamics, the research uses multiple regression methods, such as MLP Regressor, Stacking Regressor, and SVR. Historical traffic volume data is used to train and assess the models, making it possible to identify the main variables impacting patterns of congestion. The findings demonstrate how weather, time of day, and holidays all have a major impact on traffic congestion. Transportation authorities and urban planners may enhance overall urban mobility, optimise infrastructure utilisation, and regulate traffic flows proactively by leveraging the predictive capabilities of machine learning models. This work advances intelligent transport systems by offering a strong framework for anticipating and controlling traffic congestion. The results highlight the significance of utilising data-driven methodologies to tackle intricate urban issues, ultimately cultivating more effective and sustainable urban settings.

Research on Traffic Congestion Prediction Based on XGBoost

Research on Traffic Congestion Prediction Based on XGBoost

Research on traffic congestion prediction based on lasso and ridge regression

Research on traffic congestion prediction based on lasso and ridge regression

An Adaptive Framework for Traffic Congestion Prediction Using Deep Learning

Aim and background:: Congestion on China's roads has worsened in recent years due to the country's rapid economic development, rising urban population, rising private car ownership, inequitable traffic flow distribution, and growing local congestion. As cities expand, traffic congestion has become an unavoidable nuisance that endangers the safety and progress of its residents. Improving the utilization rate of municipal transportation facilities and relieving traffic congestion depend on a thorough and accurate identification of the current state of road traffic and necessitate anticipating road congestion in the city. Methodology:: In this research, we suggest using a deep spatial and temporal graph convolutional network (DSGCN) to forecast the current state of traffic congestion. To begin, we grid out the transportation system to create individual regions for analysis. In this work, we abstract the grid region centers as nodes, and we use an adjacency matrix to signify the dynamic correlations between the nodes. Results and Discussion:: The spatial correlation between regions is then captured utilizing a Graph Convolutional-Neural-Network (GCNN), while the temporal correlation is captured using a two-layer long and short-term feature model (DSTM). Conclusion:: Finally, testing on real PeMS datasets shows that the DSGCN has superior performance than other baseline models and provides more accurate traffic congestion prediction.

Adaptive traffic lights based on traffic flow prediction using machine learning models

<span lang="EN-US">Traffic congestion prediction is one of the essential components of intelligent transport systems (ITS). This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections.</span>

Urban Traffic Flow Congestion Prediction Based on a Data-Driven Model

Intelligent transportation systems need to realize accurate traffic congestion prediction. The spatio-temporal features of traffic flow are essential to analyze and predict congestion. Our study proposes a data-driven model to predict the traffic congested flow. Firstly, the traffic zone/grid method is used to store the local area roads’ average speed of the vehicles. Second, the discrete snapshot set is proposed to characterize traffic flow’s spatial and temporal features over a continuous period. Third, the evolution of traffic congested flow in various time dimensions (weekly days, weekend days, and one week) is examined by transforming the global urban transportation network into traffic zones. Finally, the data-driven model is constructed to predict urban road traffic congestion by using the extracted spatio-temporal characteristics of traffic zones’ traffic flow, the snapshot set of which serves as inputs for this model. The model adopts the convolutional LSTM network to learn the temporal and local spatial features of traffic flow, while utilizing a convolutional neural network to effectively capture the global spatial features inherent in traffic flow. The numerical experiments are conducted on two cities’ transportation networks, and the results demonstrate that the performance of the proposed model outperforms traditional traffic flow prediction models.

Temporal-Spatial Quantum Graph Convolutional Neural Network Based on Schrödinger Approach for Traffic Congestion Prediction

Traffic congestion prediction (TCP) plays a vital role in intelligent transportation systems due to its importance of traffic management. Methods for TCP have emerged greatly with the development of machine learning. However, TCP is always a challenging work due to the dynamic characteristics of traffic data and the complex structure of traffic network. This paper presents a new quantum algorithm that can capture temporal and spatial features of traffic data simultaneously for TCP. The algorithm consists of the following steps. First, we give a closed-form solution in the Schrödinger approach theoretically to analyze this TCP problem in time dimension. Then we can get the temporal features from the solution. At last, we construct a quantum graph convolutional network and apply temporal features into it. Thus, the temporal-spatial quantum graph convolutional neural network is proposed. The feasibility of this method is proved through experiments on the simulation platform. The experimental results show the average error rate is 0.21 and can resist perturbation effectively.

Ising-Traffic: Using Ising Machine Learning to Predict Traffic Congestion under Uncertainty

This paper addresses the challenges in accurate and real-time traffic congestion prediction under uncertainty by proposing Ising-Traffic, a dual-model Ising-based traffic prediction framework that delivers higher accuracy and lower latency than SOTA solutions. While traditional solutions face the dilemma from the trade-off between algorithm complexity and computational efficiency, our Ising-based method breaks away from the trade-off leveraging the Ising model's strong expressivity and the Ising machine's strong computation power. In particular, Ising-Traffic formulates traffic prediction under uncertainty into two Ising models: Reconstruct-Ising and Predict-Ising. Reconstruct-Ising is mapped onto modern Ising machines and handles uncertainty in traffic accurately with negligible latency and energy consumption, while Predict-Ising is mapped onto traditional processors and predicts future congestion precisely with only at most 1.8% computational demands of existing solutions. Our evaluation shows Ising-Traffic delivers on average 98X speedups and 5% accuracy improvement over SOTA.

An advanced approach for incremental flexible periodic pattern mining on time-series data

Periodic pattern mining is a topic for mining periodic event patterns with sufficient confidence. The resulted patterns are often used to predict future events because they have established confidence that their periodicity will be maintained after the last time of the given data. Therefore, intelligent decision-making through periodic patterns has led to popularity in various applications, such as oil price fluctuations prediction, traffic congestion prediction, human behavior analysis, and sensor-based data analysis in the industry. Meanwhile, previous approaches adopting data structures based on the suffix-tree or trie developed the mining performance of this topic, and a concept that flexible periodic patterns consider don’t-care events in their intermediate events increased the flexibility of the results in this field. However, the data structures have limitations in terms of computing performance. Meanwhile, technological development has made collecting and accumulating data faster and faster. To conduct mining on modern databases which keep increasing incrementally, processing data streams in real time should be considered in the mining method. In this paper, we propose a novel flexible periodic pattern mining approach that can operate on incremental time-series databases by utilizing an enhanced data structure. Moreover, various experiments, such as multivariable analysis, sensitivity analysis, scalability analysis, demonstrate that the proposed approach has superior performance over previously proposed state-of-the-art algorithms in terms of runtime and memory usage.