Periodic Shift and Event-aware Spatio-Temporal Graph Convolutional Network for Traffic Congestion Prediction

Traffic congestion prediction plays a guiding role in vehicle route selection. Through traffic congestion prediction, it can not only help to reduce urban traffic pressure, but also facilitate vehicle travel and save vehicle travel costs.[1]Aiming at the problem of poor accuracy of estimated traffic congestion time caused by inaccurate estimation of vehicle speed in case of serious traffic congestion, people extracted the key factors of urban traffic congestion such as average travel speed, morning and evening peaks, average travel time, average delay time, average waiting time, and number of weeks. People constructed a multi-linear regression prediction model based on R language and used the selected ones and used the selected five administrative districts of New York City (mainly on the main roads of cities and highways), the data of road traffic conditions are monitored every five minutes for calculation and validity verification.

PurposeThe study aims to propose an intelligent real-time traffic model to address the traffic congestion problem. The proposed model assists the urban population in their everyday lives by assessing the probability of road accidents and accurate traffic information prediction. It also helps in reducing overall carbon dioxide emissions in the environment and assists the urban population in their everyday lives by increasing overall transportation quality.Design/methodology/approachThis study offered a real-time traffic model based on the analysis of numerous sensor data. Real-time traffic prediction systems can identify and visualize current traffic conditions on a particular lane. The proposed model incorporated data from road sensors as well as a variety of other sources. It is difficult to capture and process large amounts of sensor data in real time. Sensor data is consumed by streaming analytics platforms that use big data technologies, which is then processed using a range of deep learning and machine learning techniques.FindingsThe study provided in this paper would fill a gap in the data analytics sector by delivering a more accurate and trustworthy model that uses internet of things sensor data and other data sources. This method can also assist organizations such as transit agencies and public safety departments in making strategic decisions by incorporating it into their platforms.Research limitations/implicationsThe model has a big flaw in that it makes predictions for the period following January 2020 that are not particularly accurate. This, however, is not a flaw in the model; rather, it is a flaw in Covid-19, the global epidemic. The global pandemic has impacted the traffic scenario, resulting in erratic data for the period after February 2020. However, once the circumstance returns to normal, the authors are confident in their model’s ability to produce accurate forecasts.Practical implicationsTo help users choose when to go, this study intended to pinpoint the causes of traffic congestion on the highways in the Bay Area as well as forecast real-time traffic speeds. To determine the best attributes that influence traffic speed in this study, the authors obtained data from the Caltrans performance measurement system (PeMS), reviewed it and used multiple models. The authors developed a model that can forecast traffic speed while accounting for outside variables like weather and incident data, with decent accuracy and generalizability. To assist users in determining traffic congestion at a certain location on a specific day, the forecast method uses a graphical user interface. This user interface has been designed to be readily expanded in the future as the project’s scope and usefulness increase. The authors’ Web-based traffic speed prediction platform is useful for both municipal planners and individual travellers. The authors were able to get excellent results by using five years of data (2015–2019) to train the models and forecast outcomes for 2020 data. The authors’ algorithm produced highly accurate predictions when tested using data from January 2020. The benefits of this model include accurate traffic speed forecasts for California’s four main freeways (Freeway 101, I-680, 880 and 280) for a specific place on a certain date. The scalable model performs better than the vast majority of earlier models created by other scholars in the field. The government would benefit from better planning and execution of new transportation projects if this programme were to be extended across the entire state of California. This initiative could be expanded to include the full state of California, assisting the government in better planning and implementing new transportation projects.Social implicationsTo estimate traffic congestion, the proposed model takes into account a variety of data sources, including weather and incident data. According to traffic congestion statistics, “bottlenecks” account for 40% of traffic congestion, “traffic incidents” account for 25% and “work zones” account for 10% (Traffic Congestion Statistics). As a result, incident data must be considered for analysis. The study uses traffic, weather and event data from the previous five years to estimate traffic congestion in any given area. As a result, the results predicted by the proposed model would be more accurate, and commuters who need to schedule ahead of time for work would benefit greatly.Originality/valueThe proposed work allows the user to choose the optimum time and mode of transportation for them. The underlying idea behind this model is that if a car spends more time on the road, it will cause traffic congestion. The proposed system encourages users to arrive at their location in a short period of time. Congestion is an indicator that public transportation needs to be expanded. The optimum route is compared to other kinds of public transit using this methodology (Greenfield, 2014). If the commute time is comparable to that of private car transportation during peak hours, consumers should take public transportation.

In order to improve the efficiency of transportation networks, it is critical to forecast traffic congestion. Large-scale traffic congestion data have become available and accessible, yet they need to be properly represented in order to avoid overfitting, reduce the requirements of computational resources, and be utilized effectively by various methodologies and models. Inspired by pooling operations in deep learning, we propose a representation framework for traffic congestion data in urban road traffic networks. This framework consists of grid-based partition of urban road traffic networks and a pooling operation to reduce multiple values into an aggregated one. We also propose using a pooling operation to calculate the maximum value in each grid (MAV). Raw snapshots of traffic congestion maps are transformed and represented as a series of matrices which are used as inputs to a spatiotemporal congestion prediction network (STCN) to evaluate the effectiveness of representation when predicting traffic congestion. STCN combines convolutional neural networks (CNNs) and long short-term memory neural network (LSTMs) for their spatiotemporal capability. CNNs can extract spatial features and dependencies of traffic congestion between roads, and LSTMs can learn their temporal evolution patterns and correlations. An empirical experiment on an urban road traffic network shows that when incorporated into our proposed representation framework, MAV outperforms other pooling operations in the effectiveness of the representation of traffic congestion data for traffic congestion prediction, and that the framework is cost-efficient in terms of computational resources.

Highway congestion has become a normal social problem that needs to be managed more accurately and deeply. In this paper, a traffic flow congestion prediction model is established based on LSTM, and the traffic parameters obtained from four observation points of China's long and deep high-speed video are used for training, so as to provide real-time early warning before the continuous congestion of the road section between the third and fourth points. The results show that the model constructed in this paper can reach 89.25% accuracy after predicting 400 times steps in 50 times Windows. After further evaluation of the constructed traffic flow congestion prediction model, it is found that the mean square error (MSE), mean absolute error (MAE) and R2 is 0.0897, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.2334,0.6028$</tex>, respectively, revealing that the model can well predict the future traffic congestion.

&amp;lt;p style=&amp;quot;text-align: justify;&amp;quot;&amp;gt;Artificial intelligence (AI) is becoming a key element in enhancing transportation systems, enabling automation, optimization, and real-time traffic flow prediction. Its application in Intelligent Transportation Systems (ITS) contributes to more efficient passenger transport in urban areas, where infrastructure fails to keep up with growing demand, leading to frequent traffic congestion and bottlenecks.This paper will describe how advanced AI algorithms and deep learning models enable traffic analysis, congestion prediction, and route optimization, improving the reliability and efficiency of public transportation. The integration of AI into ITS not only optimizes traffic flows but also enhances sustainability by reducing emissions and improving energy performance. Data-driven smart solutions make transportation safer, more environmentally friendly, and better suited to user needs.&amp;lt;/p&amp;gt;

Traffic congestion prediction is the premise to solve the problem of traffic congestion. Aiming at the problem that traffic volume was rarely considered in traffic congestion prediction based on spatio-temporal characteristics, this paper added two features of bus flow and time occupancy on the basis of temporal correlation and spatial correlation analysis of speed based on floating bus data. A BP neural network speed prediction model optimized by whale optimization algorithm (WOA) considering the temporal and spatial characteristics of bus flow was proposed, and the traffic state was divided into three levels by fuzzy theory. The results show that the speed prediction method based on temporal and spatial characteristics and bus flow characteristics proposed in this paper has good performance. Compared with the traditional BP neural network prediction results, the root mean square error and mean absolute error of WOA-BP neural network prediction model are reduced by 11.7% and 11.2% respectively, and the determination coefficient reaches 93.7%. The prediction accuracy of traffic congestion based on fuzzy theory is 90.06%, and the prediction accuracy of the model is higher.

With the development of urbanization, the number of residents’ motor vehicles has increased sharply, and traffic congestion problem has become increasingly serious. The construction of Intelligent Traffic System (ITS) has become the main means to alleviate traffic congestion. Short-term traffic flow prediction has guiding significance for residents’ travel planning and intelligent management of transportation, and has become one of the research hotspots in intelligent transportation field.Therefore, A short-term traffic flow prediction method based on the spatio-temporal characteristics of complex road networks is proposed to further improve the prediction accuracy and reduce the prediction cost. Firstly, a graph convolutional network (GCN) capable of processing non-Euclidean data structures is used to extract the spatial characteristics of traffic flow data. Then, the long and short-term memory (LSTM) neural network is used to process the time characteristics. Finally, the two are combined to realize the effective processing of the spatio-temporal characteristics of traffic flow data. Experimental results on the real traffic flow dataset prove the feasibility and effectiveness of the proposed method, and can provide a basis for intelligent traffic control and smart city construction.

Dynamic traffic correlations based spatio-temporal graph convolutional network for urban traffic prediction

With a large number of traffic parameters data, it is an important issue that how to set up an efficient model of classification and prediction to identify the congestion state as soon as possible. The article provided a model of predicting traffic congestion based on the learn vector quantization neural network by making use of traffic parameters such as speed, volume and occupancy which were detected by vehicle detectors. The model can finally classify the traffic congestion situation and normal situation by training the LVQ neural network in the software Matlab. The model can predict the road traffic situation by inputting the traffic flow data, thus providing exact road information for the dispersion of traffic congestion. Finally, an example was given to train and test the network. And the training result demonstrated the algorithm was feasible to the prediction of traffic congestion and can be actually useful in reality.

Traffic flow prediction remains a significant challenge in intelligent transportation systems. To enhance prediction accuracy, a hybrid model is proposed that integrates an improved Variational Mode Decomposition (VMD), a Residual Spatio-Temporal Graph Convolutional Network (RSTGCN), a Long-term Time Series Informer Network (LTSIN), and an iterative error compensation strategy. This approach effectively addresses the non-stationarity, spatial correlations, and temporal dependencies inherent in traffic data. First, the VMD is optimized using a Crested Porcupine Optimizer algorithm to decompose traffic flow time series into intrinsic mode functions (IMFs), each capturing different traffic patterns. Next, these IMFs are then input into the LTSIN and RSTGCN modules. The LTSIN models long-term temporal dependencies, while the RSTGCN captures short-term temporal and spatial relationships. Residual connectivity within the RSTGCN allows the model to retain node features and aggregate information from neighboring nodes, mitigating the over-smoothing issue typically seen in graph convolutional networks. Lastly, an iterative error correction mechanism is applied, where the IMF-derived predictions are refined and reconstructed to yield the final forecast. The effectiveness of the proposed model is demonstrated by extensive experiments, including ablation experiments, baseline comparisons, and multi-step forecasting experiments. Compared to existing models such as GCN and Informer, the proposed method reduces MAE by 8.78 and 6.79, and MAPE by 6.48% and 5.52%, respectively. These results indicate that the proposed model achieves superior prediction accuracy and can be effectively applied to traffic flow prediction tasks.

With the rapid development of intelligent transportation systems, vehicle detection and traffic flow statistics have become increasingly critical in traffic management and the construction of smart cities. However, complex weather conditions lead to challenges in vehicle detection algorithms, increasing false negatives and false positives. Additionally, the rapid growth of vehicle ownership makes traditional algorithms inadequate for meeting practical application needs. Therefore, this paper proposes a collaborative optimization-based method combining YOLOv11 for vehicle detection and traffic flow analysis under complex weather conditions. Vehicle information is accurately extracted through object detection and tracking methods, and further analyzed using Logistic Regression to assess traffic congestion. The main contributions of this study are: (1) Integration of the CBAM attention mechanism into YOLOv11, enhancing the vehicle feature extraction ability through collaborative channel and spatial attention, thereby improving interference resistance under complex weather conditions. (2) Adoption of the Shape-IoU loss function, with dynamic weight allocation to optimize the accuracy of detecting smaller vehicles and the precision of bounding box regression in images where vehicles are located at a greater distance. (3) Construction of a traffic congestion prediction model based on the detection results, integrated with Logistic Regression. The proposed method's effectiveness is validated on the public dataset UA-DETRAC. Through the synergistic effect of the CBAM attention mechanism and Shape-IoU loss function, the improved model's mAP50-95 shows a 0.3 percentage point increase over the YOLOv11 baseline, providing efficient and reliable technical support for intelligent transportation systems. This study enhances the prediction performance of traffic states under complex weather conditions by effectively calculating traffic flow parameters such as vehicle count and density, thus offering foundational data support for traffic management decision-making. The research conclusions are of practical significance for urban traffic improvement.

The ongoing increase in urban populations has intensified the persistent issue of traffic congestion, negatively impacting the quality of life by increasing commute times, compromising road safety, and deteriorating local air quality. As a result, identifying and forecasting traffic congestion patterns has become critical, positioning Traffic Congestion Prediction (TCP) as a rapidly growing field of study. Recent advancements in Machine Learning (ML), Artificial Intelligence (AI), and Internet of Things (IoT) sensor technologies have further underscored the importance of TCP in the development of Intelligent Transportation Systems (ITS).This review paper explores advanced TCP methodologies, with a particular emphasis on innovative forecasting techniques and technologies vital to the ITS domain. We provide a comprehensive overview of statistical and machine learning approaches, including hybrid and ensemble models, that are commonly used for TCP. Additionally, the paper discusses various forecasting techniques and elaborates on performance evaluation metrics from both regression and classification perspectives.To provide a practical framework, a standardized step-bystep methodology often adopted in TCP problems is presented. As part of the implementation, a KNearest Neighbors (KNN) classifier was employed as the proposed predictive model. Comparative evaluation revealed that KNN outperformed the Logistic Regression model in terms of both accuracy and robustness in predicting traffic congestion events. The results validate the potential of data-driven approaches to enhance proactive traffic management and informed decision-making in the context of smart cities

The increase in vehicle ownership and urban population puts a strain on roads and increases traffic congestion within developed and developing nations. Congestion affects an individual’s time and increases an individual’s fuel consumption and pollution output, undercutting efforts towards smart/ sustainable cities. Outdated statistical congestion predicting methods do not account for the non-linear, dynamic nature of congestion with regards to external factors such as, weather, road incidents, and infrastructure. The adoption of machine and deep learning develops the framework towards adaptable and data-driven congestion predicting methods. This study develops a framework that combines the fundamentals of machine learning algorithms, Queuing Theory, Decision Trees, Random Forests, and Deep Belief Networks (DBN) for robust and interprefig traffic congestion forecasting. The traffic flow micro-dynamics integration with Queuing Theory and the use of historical/ real-time multimodal data such as sensor feeds, GPS trajectories, and incident reports as a basis for forecasting helps in robust congestion prediction. Polynomials of moving and rest queues with defined traffic characteristics fuel flow mechanisms. The designed framework uses the DBN and Queuing Theory for robust traffic forecasting.

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.

As one of the most widely used applications of the Internet of Things (IoT), intelligent transportation system (ITS) is of great significance for urban traffic planning, traffic control, and traffic guidance. However, widespread traffic congestion occurs with the increased number of vehicles. The traffic flow prediction is a good idea for traffic congestion. Therefore, many schemes have been proposed for accurate and real-time traffic flow prediction, but there still exist many issues, including low accuracy, weak adaptability and inferior real-time. Meanwhile, the complex spatial and temporal dependencies in traffic flow are still challenging. To address the above issues, we propose a novel spatial-temporal Chebyshev graph neural network model (ST-ChebNet) for traffic flow prediction to capture the spatial-temporal features, which can ensure accurate traffic flow prediction. Concretely, we first add a fully connected layer to fuse the features of traffic data into a new feature to generate a matrix, and then the long short-term memory (LSTM) model is adopted to learn traffic state changes for capturing the temporal dependencies. Then, we use the Chebyshev graph neural network (ChebNet) to learn the complex topological structures in the traffic network for capturing the spatial dependencies. Eventually, the spatial features and the temporal features are fused to guarantee the traffic flow prediction. The experiments show that ST-ChebNet can make accurate and real-time traffic flow prediction compared with other eight baseline methods on real-world traffic data sets PeMS.