Traffic congestion forecasting using machine learning methods

Abstract. Quantifying patterns of nest survival is a first step toward understanding why birds decide when and where to breed. Most studies of nest survival have relied on generalized linear models (GLM) to explore these patterns. However, GLMs require assumptions about the models' structure that might preclude finding nonlinear patterns in survival data. Generalized additive models (GAM) provide a flexible alternative to GLMs for estimating linear and nonlinear patterns in data. Here we present a comparison of GLMs and GAMs for explaining variation in nest-survival data. We used two different model-selection criteria, the Bayes (BIC) and Akaike (AIC) information criteria, to select among simple and complex models. Our study was focused on the analysis of Redwinged Blackbird (Agelaius phoeniceus) nests in the Rainwater Basin wetlands of south-central Nebraska. Under BIC, our quadratic model of nest age had the most support, and the model predicted a concave pattern of daily nest survival. We found more mo...

The renewal speed of urban roads is far from meeting the growing demand for vehicle travel, leading to significant urban road congestion. To address this issue, Intelligent Transportation Systems (ITS) utilize low-rank matrices to process traffic data, aiming to identify traffic characteristics and the causes of failures. Abnormal values in traffic data are improved through sequential constraints, enabling the recovery of accurate traffic data. By leveraging data obtained from various sensing devices and combining it with historical road traffic data streams, ITS can uncover patterns in traffic data with different characteristics. This process involves finding suitable prediction methods and establishing predictive models. These models facilitate the analysis of traffic data from multiple perspectives using a method of multi-parameter fusion, making the traffic change characteristics more apparent and comprehensive when predicting short-term traffic conditions. For multi-source data series, the General Regression Neural Network (GRNN) model can be integrated in a higher-dimensional space. The traffic time and throughput of different models in various traffic sections exhibit high similarity. Notably, the Wavelet-based Fuzzy General Regression Neural Network (WF-GRNN) model demonstrates higher throughput and lower traffic time across different traffic sections. Variable Message Signs (VMS) are induced by Lyapunov exponents combined with a neural network. This approach helps to solve the problem of road congestion and brings considerable economic benefits to society.

Smart cities have revolutionized urban living by incorporating sophisticated technologies to optimize various aspects of urban infrastructure, such as transportation systems. Effective traffic management is a crucial component of smart cities, as it has a direct impact on the quality of life of residents and tourists. Utilizing deep radial basis function (RBF) networks, this paper describes a novel strategy for enhancing traffic intelligence in smart cities. Traditional methods of traffic analysis frequently rely on simplistic models that are incapable of capturing the intricate patterns and dynamics of urban traffic systems. Deep learning techniques, such as deep RBF networks, have the potential to extract valuable insights from traffic data and enable more precise predictions and decisions. In this paper, we propose an RBF-based method for enhancing smart city traffic intelligence. Deep RBF networks combine the adaptability and generalization capabilities of deep learning with the discriminative capability of radial basis functions. The proposed method can effectively learn intricate relationships and nonlinear patterns in traffic data by leveraging the hierarchical structure of deep neural networks. The deep RBF model can learn to predict traffic conditions, identify congestion patterns, and make informed recommendations for optimizing traffic management strategies by incorporating these rich and diverse data. To evaluate the efficacy of our proposed method, extensive experiments and comparisons with real-world traffic datasets from a smart city environment were conducted. In terms of prediction accuracy and efficiency, the results demonstrate that the deep RBF-based approach outperforms conventional traffic analysis methods. Smart city traffic intelligence is enhanced by the model capacity to capture nonlinear relationships and manage large-scale data sets.

Network traffic archival solutions are fundamental for a number of emerging applications that require: a) efficient storage of high-speed streams of traffic records and b) support for interactive exploration of massive datasets. Compression is a fundamental building block for any traffic archival solution. However, present solutions are tied to general-purpose compressors, which do not exploit patterns of network traffic data and require to decompress a lot of redundant data for high selectivity queries. In this work we introduce RasterZIP, a novel domain-specific compressor designed for network traffic monitoring data. RasterZIP uses an optimized lossless encoding that exploits patterns of traffic data, like the fact that IP addresses tend to share a common prefix. RasterZIP also introduces a novel decompression scheme that accelerates highly selective queries targeting a small portion of the dataset. With our solution we can achieve high-speed on-the-fly compression of more than half a million traffic records per second. We compare RasterZIP with the fastest Lempel-Ziv-based compressor and show that our solution improves the state-of-the-art both in terms of compression ratios and query response times without introducing penalty in any other performance metric.

The increasing complexity of urban transportation systems and the growing volume of vehicles have made traffic congestion a persistent challenge in modern cities. Efficient traffic flow prediction is essential for mitigating congestion, improving road safety, optimizing traffic signal control, and enhancing overall transportation efficiency. In recent years, artificial intelligence (AI) has emerged as a transformative tool in the field of traffic management, offering sophisticated algorithms capable of modeling, analyzing, and predicting complex traffic patterns with high accuracy. The application of AI in traffic flow prediction leverages vast amounts of real-time and historical data to generate precise forecasts, supporting data-driven decision-making by urban planners and traffic control authorities. The prediction of traffic flow involves analyzing time-series data that exhibit nonlinear, dynamic, and often stochastic behavior. Traditional statistical models, such as autoregressive integrated moving average (ARIMA), have proven to be limited in handling the high dimensionality and variability inherent in traffic systems. In contrast, AI algorithms possess the capacity to learn and adapt from complex data inputs without the need for explicit programming, making them particularly suitable for traffic-related applications. AI algorithms used in traffic flow prediction can be broadly categorized into machine learning (ML) and deep learning (DL) approaches. Machine learning algorithms such as k-nearest neighbors (KNN), support vector machines (SVM), decision trees, and random forests have demonstrated effectiveness in short-term traffic prediction tasks. These algorithms are capable of identifying hidden patterns in traffic data and adjusting to changes in traffic behavior over time. Ensemble methods, which combine the strengths of multiple learning models, further enhance prediction accuracy and robustness. Deep learning algorithms, a subfield of AI inspired by the human brain’s neural architecture, have shown exceptional performance in capturing spatial-temporal dependencies in traffic data. Recurrent neural networks (RNNs), particularly long short-term memory (LSTM) networks and gated recurrent units (GRUs), are widely used for their ability to process sequential data and retain information over extended time intervals. Convolutional neural networks (CNNs) are employed to extract spatial features from traffic sensor data or road network imagery. Hybrid models that integrate CNNs with RNNs have achieved high levels of predictive precision by simultaneously learning spatial and temporal correlations. In addition to supervised learning methods, unsupervised and reinforcement learning techniques are also applied in traffic flow prediction. Clustering algorithms, such as k-means and DBSCAN, assist in identifying traffic patterns, while reinforcement learning models optimize adaptive traffic signal control systems by learning optimal actions through environmental interaction. This study explores the different types of AI algorithms used in traffic flow prediction, examining their theoretical foundations, structural differences, and practical applications. It aims to evaluate the comparative advantages of various algorithms in addressing the challenges of real-time traffic prediction in increasingly complex transportation networks. Keywords: Machine Learning, Deep Learning, Neural Networks, Regression Models, Reinforcement Learning

Traffic data is a challenging spatio-temporal data, and a multivariate time series data with spatial similarities. Clustering of traffic data is a fundamental tool for various machine learning tasks including anomaly detection, missing data imputation and short term forecasting problems. In this paper, first, we formulate a spatio-temporal clustering problem and define temporal and spatial clusters. Then, we propose an approach for finding temporal and spatial clusters with a deep embedded clustering model. The proposed approach is examined on traffic flow data. In the analysis, we present the properties of clusters and patterns in the dataset. The analysis shows that the temporal and spatial clusters have meaningful relationships with temporal and spatial patterns in traffic data, and the clustering method effectively finds similarities in traffic data.

Effective traffic prediction is crucial for optimizing urban transportation systems, minimizing congestion, and enhancing overall efficiency. Traffic congestion results in prolonged travel durations, higher fuel consumption, economic setbacks, and increased environmental pollution. To tackle these issues, we introduce a Hybrid CNN-GRU-LSTM model—an advanced deep learning framework that combines convolutional neural networks (CNN), gated recurrent units (GRU), and long short-term memory (LSTM) networks. This integrated model is specifically designed to capture both spatial and temporal patterns of traffic flow, making it highly effective for predicting vehicle volumes at intersections. The Hybrid CNN-GRU-LSTM leverages CNN to model spatial dependencies between road segments, while GRU and LSTM layers handle short-term and long-term temporal patterns in traffic data. This combination allows for more accurate predictions by incorporating spatial relationships and temporal dynamics simultaneously. The model was tested using publicly available datasets, including PeMS, the England dataset, the P/Castellano dataset, and the Fedesoriano dataset, and results demonstrate that Hybrid CNN-GRU-LSTM significantly outperforms several state-of-the-art models, achieving a reduction of up to 30–35% in error values. This study highlights the effectiveness of combining CNN, GRU, and LSTM architectures for traffic prediction, offering a robust solution for transportation management. The proposed model’s significant improvement in prediction accuracy can help mitigate the adverse effects of traffic congestion and enhance the overall performance of transportation networks.

We describe how to employ machine learning (ML) methods in theory development. Compared to traditional causal inference methods, ML methods make far fewer a priori assumptions about the functional form of the underlying model that best represents the data. Given this, researchers could use such methods to explore novel and robust patterns in the data that could lead to inductive theory building. ML strengths include replicable identification of novel patterns in the data. Additionally, ML methods address several concerns (such as ‘p-hacking’ and confounding local effects for global effects) raised by scholars relative to the norms of empirical research in the fields of strategy and management. We develop a step-by-step roadmap that illustrates how to use four ML methods (decision trees, random forests, K-nearest neighbors and neural networks) to reveal patterns in data that could be used for theory building. We also illustrate how ML methods could better illuminate interactions and non-linear effects, relative to traditional methods. In summary, ML methods could act as a complementary tool to both existing inductive theory-creating methods such as multiple case inductive studies and traditional methods of causal inference.

Dynamic multi-granularity spatial-temporal graph attention network for traffic forecasting

The urban road network state can be characterized by diversiform parameters of all links; however, it is difficult to directly express the road network’s state by these parameters. Thus, a synthetic evaluation index should be extracted from various parameters to express the road network’s state comprehensively and intuitively. This paper proposes a novel model for extracting temporal and spatial features in a networked traffic system, Locality Sensitive Discriminant Analysis (LSDA) is used to reduce data dimensionality and extract features from traffic data analysis. Selected link and parameter data points are compressed and the output matrix has fewer dimensions than the original data. The corresponding relationship between the synthetic evaluation index and various parameters is analyzed. Simulation results prove that this method is effective in revealing hidden patterns in traffic data and the synthetic evaluation index can express the evolution of road network state comprehensively and accurately.

Power has totally different attributes than other material commodities as electrical energy stockpiling is a costly phenomenon. Since it should be generated when demanded, it is necessary to forecast its demand accurately and efficiently. As electrical load data is represented through time series pattern having linear and non-linear characteristics, it needs a model that may handle this behavior well in advance. This paper presents a scalable and hybrid approach for forecasting the power load based on Vector Auto Regression (VAR) and hybrid deep learning techniques like Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN). CNN and LSTM models are well known for handling time series data. The VAR model separates the linear pattern in time series data, and CNN-LSTM is utilized to model non-linear patterns in data. CNN-LSTM works as CNN can extract complex features from electricity data, and LSTM can model temporal information in data. This approach can derive temporal and spatial features of electricity data. The experiment established that the proposed VAR-CNN-LSTM(VACL) hybrid approach forecasts better than more recent deep learning methods like Multilayer Perceptron (MLP), CNN, LSTM, MV-KWNN, MV-ANN, Hybrid CNN-LSTM and statistical techniques like VAR, and Auto Regressive Integrated Moving Average (ARIMAX). Performance metrics such as Mean Square Error, Root Mean Square Error, and Mean Absolute Error have been used to evaluate the performance of the discussed approaches. Finally, the efficacy of the proposed model is established through comparative studies with state-of-the-art models on Household Power Consumption Dataset (UCI machine learning repository) and Ontario Electricity Demand dataset (Canada).

The complexity of traffic flow patterns significant challenges in predicting traffic green signal timings using conventional methods. Most of conventional methods relied on vehicle counts and speeds. These methods often did not consider crucial factors such as Spatial Occupancy, long-term dependencies, and the non-linear relationships. Recent advancements in Convolutional Neural Networks (CNNs) have enabled better capturing of patterns in traffic data. These advancements are essential for effectively predicting vehicle Green Signal Time by considering accurate detection and tracking, Spatial Occupancy calculation, long-term dependencies, and non-linear relationships in traffic data. The PVD-GSTPS framework has been proposed as an innovative solution for predicting vehicle Green Signal Time with the help of advanced CNN. This framework leverages the capabilities of two fine-tuned object detection models YOLO v8 and Faster R-CNN for precise vehicle detection, while a Byte Sort Tracker monitors the trajectories of detected vehicles. Additionally, a vehicle counting module assesses the number of vehicles in specified areas, and a size assignment process estimates Green Signal Time based on Spatial Occupancy calculations. This study is limited by the fixed duration of the QMUL video dataset utilized. This restricts data availability and complicates the establishment of strong correlations between Green Signal Time and Spatial Occupancy. To mitigate this issue, we utilized a Generative Adversarial Network (GAN) to generate realistic synthetic data. Long Short-Term Memory (LSTM) networks and polynomial regression techniques are utilized to capture the relationships within this dataset. In this study, we used the QMUL dataset to validate our hypothesis. The results demonstrate that our PVD-GSTPS framework significantly outperforms Enhanced YOLO v8, Original YOLO v8, and Faster R-CNN.

Traffic forecasting is crucial for a variety of applications, including route optimization, signal management, and travel time estimation. However, many existing prediction models struggle to accurately capture the spatiotemporal patterns in traffic data due to its inherent nonlinearity, high dimensionality, and complex dependencies. To address these challenges, a short-term traffic forecasting model, Trafficformer, is proposed based on the Transformer framework. The model first uses a multilayer perceptron to extract features from historical traffic data, then enhances spatial interactions through Transformer-based encoding. By incorporating road network topology, a spatial mask filters out noise and irrelevant interactions, improving prediction accuracy. Finally, traffic speed is predicted using another multilayer perceptron. In the experiments, Trafficformer is evaluated on the Seattle Loop Detector dataset. It is compared with six baseline methods, with Mean Absolute Error, Mean Absolute Percentage Error, and Root Mean Square Error used as metrics. The results show that Trafficformer not only has higher prediction accuracy, but also can effectively identify key sections, and has great potential in intelligent traffic control optimization and refined traffic resource allocation.

In recent years, advancements in deep learning and real-time data processing have significantly enhanced traffic management and accident prediction capabilities. Building on these developments, this study introduces an innovative approach ConvoseqNet to improve traffic accident prediction by integrating traditional traffic data with real-time social media insights, specifically using geographic data and Twitter sentiment analysis. ConvoseqNet combines Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks in a sequential architecture, enabling it to effectively capture complex spatiotemporal patterns in traffic data. To further enhance prediction accuracy, a meta-model called MetaFusionNetwork is proposed, which combines predictions from ConvoseqNet and a Random Forest Classifier. Results show that ConvoseqNet alone achieved the highest predictive accuracy, demonstrating its capacity to capture diverse accident-related patterns. Additionally, MetaFusionNetwork’s performance highlights the advantages of combining model outputs for better prediction. This research contributes to real-time data-driven traffic management by leveraging innovative data fusion techniques, improving prediction accuracy, and providing insights into model interpretability and computational efficiency. By addressing the challenges of integrating heterogeneous data sources, this approach presents a significant advancement in traffic accident prediction and safety enhancement.

Prediction Of Trends In Bus Travel Time Using Spatial Patterns