SNSJam: Road traffic analysis and prediction by fusing data from multiple social networks

Methods of treatment (case reports)

With the increasing percentage penetration of wind energy, accurate Wind Speed/Power Forecasting (WSF/WPF) has become a crucial tool for power trading and electricity grid operations. The physical and statistical methods used to forecast mostly rely on meteorological information, site specific data and historical data from wind farms. Most of the existing works are limited to utilize single input data source and a prediction variable. However, some gap in using multiple data sources and multiple prediction variables as input has been identified to substantiate and enhance forecast accuracy of these models. In this paper, a multi-source and multivariate surveillance approach has been conducted using time series methods and machine learning algorithms. Firstly, the proposed model uses multiple sources to understand the deciding prediction variables. Secondly, an analogy among time series Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Networks (RNNs) with Long Short-term Memory units (LSTMs) is used. Although, the prediction models using single data source are economical for forecast providers and wind power producers considering multiple sources can certainly improve the forecasting accuracy thus reducing wind curtailments and imbalance penalties. A comparative approach has been made using data sources obtained for Gulf of Khambhat, a south to north penetration of the Arabian Sea on the western shelf of India. The obtained results show that the forecasting accuracy can be improved by considering multiple sources of input data.

Traffic prediction plays an important role in the intelligent transportation system (ITS), because it can increase people’s travel convenience. Despite the deep neural network has been widely used in the field of traffic prediction, literature surveys of such methods and data categories are rare. In this paper, we have a summary of traffic forecasting from data, methods and challenges. Firstly, we are according to the difference of in spatio-temporal dimensions, divide the data into three types, including the spatio-temporal static data, spatial static time dynamic data, and spatio-temporal dynamic data. Secondly, we explore three significant neural networks of deep learning in traffic prediction, including the convolutional neural network (CNN), the recurrent neural network (RNN), and the hybrid neural networks models. These methods are used in many aspects of traffic prediction, including road traffic accidents forecast, road traffic flow prediction, road traffic speed forecast, and road traffic congestion forecast introduced. Finally, we provide a discussion of some current challenges and development prospects. KeywordsDeep neural networkTraffic forecastingSpatio-temporal data

Traffic prediction is a critical element of Intelligent Transportation Systems (ITS), enhancing road safety, traffic flow, and environmental sustainability. By leveraging big data and machine learning, ITS optimizes fuel consumption, route planning, and traffic signal control. This study evaluates the performance of four machine learning models—K-Nearest Neighbors (KNN), Naïve Bayes, Decision Tree, and Random Forest—for classifying traffic conditions based on 11 months of vehicle count data collected every 15 minutes. The traffic levels are categorized into four classes: Heavy Traffic, Medium Traffic, No Traffic, and Traffic Jam. Among the models, Random Forest achieved the highest overall accuracy (99%) and delivered strong precision and recall across all traffic levels, especially in distinguishing between complex traffic patterns. KNN also showed high accuracy but required more computational resources. Decision Tree performed comparably, particularly for Heavy Traffic, while Naïve Bayes struggled with lower precision and recall due to its limitations in handling complex data distributions. The results underscore the robustness of Random Forest for real-time traffic condition classification and support its integration into ITS for effective, data-driven traffic and congestion management in urban environments.

Road traffic has long been recognized to represent a country’s prosperity. Unfortunately, traffic congestion coming with it is considered as one of the most significant challenge in modern cities. Thus, a promising paradigm, namely, Intelligent Transportation System (ITS) has become a research focus which lies on its feasibility and efficiency of solving transportation issues. In the open literature, researchers have proposed theories and developed corresponding models to alleviate traffic congestion. However, many solutions are static, which means they serve to analyze and evaluate the traffic systems rather than schedule the road traffic dynamically. To this end, this paper develops a road traffic congestion prediction model to forecast the congestion level. The congestion performance metrics are obtained by developing a queueing system subject to self-similar traffic. This is because Hurst parameter evaluation, subject to road traffic flow at rush hour, presents strong self-similar characteristic. The developed model is validated by comparing results derived from model with that of simulations on real road traffic data provided by government authorities.

Traffic congestion has a negative impact on our daily life. Predicting the trend of traffic congestion can provide a valuable guideline to address such problems. Most existing approaches focus on the tasks of predicting traffic volume or traffic speed, which do not effectively address the challenges of traffic congestion prediction. First, traffic congestion exhibits daily and weekly temporal patterns, but these patterns are not strictly the same, which indicates complicated long-term periodicity. Second, traffic congestion sparsely distributes over different periods of time, which leads to complex short-term and mid-term temporal dependencies. Third, since traffic congestion will propagate to adjacent road segments over time, it exhibits complex spatio-temporal correlations. To address them, we propose a periodic shift and event-aware spatio-temporal graph convolutional network for traffic congestion prediction. Specifically, we propose to capture the differences and similarities of long-term periodic temporal patterns to handle the complicated long-term periodicity. To effectively capture short-term and mid-term temporal dependencies, we regard a continuous time sequence of the congested condition as a traffic congestion event, and then adopt the widely-used long short-term memory model to learn the sequential dependencies of traffic congestion events. Finally, we integrate the graph convolutional network into the modeling of temporal dependencies to capture the complex spatio-temporal correlations. Extensive experiments demonstrate the superiority of our model. In addition, we deploy our model in production at Amap, and it achieves great performance improvement in terms of the F1-score compared to the production baseline. This confirms that our model is a practical solution for real-world congestion prediction services.

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.

Vehicular Clouds introduces a new paradigm that addresses and potentially enhances underutilization of on-board computing resources through aggregation to solve several computational tasks in Intelligent Transportation System. The most challenging issue in Vehicle Cloud is the task allocation among the dynamically changing amount of available resources. For further research towards this issue, a realistic road traffic system models which could generate traffic flow with high accuracy must be designed. In this paper, we conduct a study on short-term traffic flow predictions for our envisioned road traffic prediction system. Five prediction models, including double exponential smoothing (DES), seasonal autoregressive moving average (SARIMA), K-nearest neighbor (KNN), back-propagation neural network (BP-NN) and support vector regression (SVR), are implemented. Then, three different error metrics are used to evaluate the performance of these models. Finally, the results shows that SARIMA and BP neural network are two precise and stationary prediction models and thus are the best candidates to be embedded in an road traffic load prediction system.

In metropolitan areas, common traffic issues include traffic congestion, traffic accidents, air pollution, and energy consumption occur. To resolve this issues, Intelligent Transportation Systems (ITS) have been evolved by many researchers. One of the important sub-systems in the development of ITS is a Traffic Management System (TMS) which attempts to reduce a traffic congestion. In fact, TMS itself relies on the estimation of traffic flow, therefore providing such an accurate traffic flow prediction is needed. For this reason, we aim to provide an accurate traffic flow prediction to facilitate this system. In this works, a Supervised Deep Learning Based Traffic Flow Prediction (SDLTFP) was proposed which is a type of fully-connected deep neural network (FC-DNN). Timely prediction is also a major issue in guaranteeing reliable traffic flow prediction. However, training a deep network could be time-consuming, and overfitting is might be happening, especially when feeding small data into the deep architecture. The network is learned perfectly during the training, but in testing with the new data, it could fail to generalize the model. We adopt the Batch Normalization (BN) and Dropout techniques to help the network training. SGD and momentum are carried out to update the weight. We then take advantage of open data as historical traffic data which are then used to predict future traffic flow with the proposed method and model above. Experiments show that the Mean Absolute Percentage Error (MAPE) for our traffic flow prediction is within 5 % using sample data and between 15% to 20% using out of the sample data. Training a deep network faster with BN and Dropout reduces the overfitting.

With the rapid development of Internet of Vehicles (IoV), the applications of traffic congestion detection based on Intelligent Traffic System (ITS) are proposed and widely used. However, location privacy leakage has become a serious problem in traffic congestion detection based on traffic voting, because vehicles will upload traffic congestion information and their own locations to the traffic management server. In this paper, we propose a decentralized weighted vote traffic congestion detection framework, which allows vehicles can safely participate in the traffic voting, under location privacy protection, and accurately obtain the traffic congestion status of the road. Specifically, we introduce blockchain technology into traffic congestion detection, and apply smart contract to replace the traditional trusted third party. We propose a weighted voting scheme, and set the weight of votes depending on the distance between the vehicle’s current position and the location of congestion. The security analysis result shows that our framework can satisfy the above requirements. Finally, the experiment results demonstrate that our framework is efficient and practical.

Traffic flow prediction is an essential part of intelligent transportation systems (ITS), facilitating dynamic traffic control, congestion alleviation, and route planning. The past few years have seen the booming of Graph Neural Networks (GNNs), providing a strong tool to capture the sophisticated spatial compositions and dynamic temporal patterns inherent in urban road networks. In this paper, I provide a systematic and comprehensive review of GNN-based solutions for short-term traffic prediction. I first briefly review the basic concepts and categorization of GNNs, then elaborate on representative models such as spatial and spectral convolutional networks, temporal graph structures, and hybrid models with attention mechanisms and recurrent units. Besides, I recap typical traffic datasets and evaluation metrics, compare the performance of different models from multiple aspects, and highlight key technical challenges such as spatiotemporal heterogeneity, scalability, and interpretability. Finally, I suggest future research lines to improve the accuracy, efficiency, and robustness of GNN-based traffic prediction models for real-world ITS applications.

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.

With the growth of large cities, it becomes necessary to overcome several challenges related to infrastructure. In this context, urban mobility, including transportation systems, is a key topic. It is known that more efficient traffic management makes cities more efficient as a whole, improving the quality of life of their residents and tourists. A common problem that arises concerns traffic jams, encompassing their detection and management. With proper traffic jam detection, it is possible to implement traffic management methods to prevent long-term occurrences or even notify drivers to avoid specific locations. Some traffic jam detection solutions use sensors spread across cities, while others are based on sensors in drivers' smartphones. These approaches have limitations related mainly to the information reliability or to the cost and complexity of implementation. In this work, we propose an approach using Machine Learning techniques applied to public data of transportation systems to detect urban traffic jams. The proposed approach is evaluated using the publicly available GPS data from the buses of the Rio de Janeiro/Brazil transportation system and traffic jam events posted on Twitter by the Rio de Janeiro City Hall.

By integrating automated driving systems (ADS) and AI-driven advanced driver assistance systems (ADAS) like the traffic sign detection (TSD) technology, the automotive sector can develop smart and self-driving cars. Traffic signs (TS) play a crucial role in avoiding accidents and traffic congestion. Motorists need to understand the visual representations of various data elements incorporated in traffic symbols. There are often instances where drivers neglect TS located ahead of their vehicles, resulting in severe outcomes. This research offers an automatic TSD forecast utilising the hybrid deep belief network (HDBN) model for classification to address this issue. When it comes to forecasting the future world of smart urban cities, the given HDBN model primarily focuses on high-precision traffic prediction. The rider sunflower optimization (RSFO) technique is utilised to improve the hyper parameter tuning, which improves the overall effectiveness of the traffic flow prediction process. Overall, the suggested TSD system is found to be a highly efficient method of detecting TS, performing exceptionally well in relation to precision, recall, accuracy, and F1. The suggested solution under evaluation appears to perform better in terms of accuracy than other current methods stated in this chapter.

Aiming at the current situation that the existing traffic event detection algorithm is complex and cumbersome, a traffic incident detection method based on the width characteristics of the moving object was proposed. Firstly, the foreground object is extracted by ViBe (visual background extractor) and the foreground target is subjected to hole removal and smoothing processing. Then, the moving target is marked with a label, the width change characteristic of the moving target mark frame is analysed and the traffic event detection model is introduced, thereby achieving detection of traffic events. The experimental results show that the method can effectively detect the rear-end event, the crossing event and the collision-fixing event in the traffic incident.