Search Results (65)

Utilizing deep learning models to detect malicious anomalies within the traffic of application layer J1939 protocol networks, found on heavy-duty commercial vehicles, is becoming a critical area of research in platform protection. At the physical layer, the controller area network (CAN) bus is the backbone network for most vehicles. The CAN bus is highly efficient and dependable, which makes it a suitable networking solution for automobiles where reaction time and speed are of the essence due to safety considerations. Much recent research has been conducted on securing the CAN bus explicitly; however, the importance of protecting the J1939 protocol is becoming apparent. Our research utilizes long short-term memory models to predict the next binary data sequence of a J1939 packet. Our primary objective is to compare the performance of our J1939 detection system trained on data sub-fields against a published CAN system trained on the full data payload. We conducted a series of experiments to evaluate both detection systems by utilizing a simulated attack representation to generate anomalies. We show that both detection systems outperform one another on a case-by-case basis and determine that there is a clear requirement for a multifaceted security approach for vehicular networks. Full article

Urban traffic congestion poses persistent challenges to mobility, public safety, and governance efficiency in metropolitan areas. This study proposes an intelligent traffic flow forecasting framework based on an extended Long Short-Term Memory (xLSTM) model, specifically designed for real-time congestion prediction and proactive police dispatch support. Utilizing a real-world dataset collected from over 300 vehicle detector (VD) sensors, the proposed model integrates vehicle volume, speed, and lane occupancy data at five-minute intervals. Methodologically, the xLSTM model incorporates matrix-based memory cells and exponential gating mechanisms to enhance spatio-temporal learning capabilities. Model performance is evaluated using multiple metrics, including congestion classification accuracy, F1-score, MAE, RMSE, and inference latency. The xLSTM model achieves a congestion prediction accuracy of 87.3%, an F1-score of 0.882, and an average inference latency of 41.2 milliseconds—outperforming baseline LSTM, GRU, and Transformer-based models in both accuracy and speed. These results validate the system’s suitability for real-time deployment in police control centers, where timely prediction of traffic congestion enables anticipatory patrol allocation and dynamic signal adjustment. By bridging AI-driven forecasting with public safety operations, this research contributes a validated and scalable approach to intelligent transportation governance, enhancing the responsiveness of urban mobility systems and advancing smart city initiatives. Full article

Sustainable traffic management relies on accurate traffic flow prediction to reduce congestion, fuel consumption, and emissions and minimise the external environmental impacts of traffic operations. This study contributes to this objective by developing and evaluating advanced machine learning models that leverage multisource data to predict traffic patterns more effectively, allowing for the deployment of proactive measures to prevent or reduce traffic congestion and idling times, leading to enhanced eco-friendly mobility. Specifically, this paper evaluates the impact of multisource sensor inputs and spatial detector interactions on machine learning-based traffic flow prediction. Using a dataset of 839,377 observations from 14 detector stations along Melbourne’s Eastern Freeway, Bidirectional Long Short-Term Memory (BiLSTM) models were developed to assess predictive accuracy under different input configurations. The results demonstrated that incorporating speed and occupancy inputs alongside traffic flow improves prediction accuracy by up to 16% across all detector stations. This study also investigated the role of spatial flow input interactions from upstream and downstream detectors in enhancing prediction performance. The findings confirm that including neighbouring detectors improves prediction accuracy, increasing performance from 96% to 98% for eastbound and westbound directions. These findings highlight the benefits of optimised sensor deployment, data integration, and advanced machine-learning techniques for smart and eco-friendly traffic systems. Additionally, this study provides a foundation for data-driven, adaptive traffic management strategies that contribute to sustainable road network planning, reducing vehicle idling, fuel consumption, and emissions while enhancing urban mobility and supporting sustainability goals. Furthermore, the proposed framework aligns with key United Nations Sustainable Development Goals (SDGs), particularly those promoting sustainable cities, resilient infrastructure, and climate-responsive planning. Full article

Accurate traffic forecasting is challenging due to the complex spatial-temporal interdependencies of large road networks and sudden speed changes caused by unexpected events. Traditional models often struggle with the non-stationary and volatile characteristics of traffic time series. While existing sequence decomposition methods can capture stable long-term trends and periodic information, they fail to address complex fluctuation patterns. To tackle this issue, we propose the Spatial-Temporal traffic flow prediction with residual and trend Decomposition Transformer (STDformer), which decomposes time series into different components, thus enabling more accurate modeling of both short-term and long-term dependencies. Our method processes the time series in parallel using the Trend Decomposition Block and the Spatial-Temporal Relation Attention. The Spatial-Temporal Relation Attention captures dynamic spatial correlations across the road network, while the Trend Decomposition Block decomposes the series into trend, seasonal, and residual components. Each component is then independently modeled by the Temporal Modeling Block to capture its unique temporal dynamics. Finally, the outputs from the Temporal Modeling Block are fused through a selective gating mechanism, combined with the Spatial-Temporal Relation Attention output to produce the final prediction. Extensive experiments on PEMS traffic datasets demonstrate that STDformer consistently outperforms state-of-the-art traffic flow prediction methods, particularly under volatile conditions. These results validate STDformer’s practical utility in real-world traffic management, highlighting its potential to assist traffic managers in making informed decisions and optimizing traffic efficiency. Full article

The analysis of traffic crashes facilitates the identification of trends that can inform strategies to enhance road safety. This study aimed to detect high-risk zones and forecast collision patterns by integrating spatial analysis and predictive modeling. Traffic incidents along the Chone–Flavio Alfaro road segment in Manabí, Ecuador, were examined using Geographic Information Systems (GIS) and Kernel Density Estimation (KDE), based on official data from the National Traffic Agency (ANT) covering the period 2017–2023. Additionally, ARIMA, Prophet, and Long Short-Term Memory (LSTM) models were applied to predict crash occurrences. The most influential contributing factors were driver distraction, excessive speed, and adverse weather. Four main crash hotspots were identified: near Chone (PS 0–2.31), PS 2.31–7.10, PS 13.39–21.31, and PS 31.27–33.92, close to Flavio Alfaro. A total of 55 crashes were recorded, with side impacts (27.3%), pedestrian-related collisions (14.5%), and rear-end crashes (12.7%) being the most frequent types. The predictive models performed well, with Prophet achieving the highest estimated accuracy (90.8%), followed by LSTM (88.2%) and ARIMA (87.6%), based on MAE evaluations. These findings underscore the potential of intelligent transportation systems (ITSs) and predictive analytics to support proactive traffic management and resilient infrastructure development in rural regions. Full article

Ship-speed prediction is an emerging research area in marine traffic safety and other related fields, occupying an important position with respect to these areas. At present, the effectiveness of techniques used in in time-series forecasting methods in ship-speed prediction is poor, and there are accumulated errors in long-term forecasting, which is limited in its processing of ship-speed information combined with multi-feature data input. To overcome this difficulty and further optimize the accuracy of ship-speed prediction, this research proposes a new deep learning framework to predict ship speed by combining GANs (Generative Adversarial Networks) and LSTM (Long Short-Term Memory). First, the algorithm takes an LSTM network as the generating network and uses the LSTM to mine the spatiotemporal correlation between nodes. Secondly, the complementary characteristics linked between the generative network and the discriminant network are used to eliminate the cumulative error of a single neural network in the long-term prediction process and improve the prediction accuracy of the network in ship-speed determination. To conclude, the Generator–LSTM model advanced here is used for ship-speed prediction and compared with other models, utilizing identical AIS (automatic identification system) ship-speed information in the same scene. The findings indicate that the model demonstrates high accuracy in the typical error measurement index, which means that the model can reliably better predict the ship speed. The results of the study will assist maritime traffic participants in better taking precautions to prevent collisions and improve maritime traffic safety. Full article

Air quality (AQ) is one of the most important urban environment indicators for the quality of life. The paper proposes a software solution for predicting and forecasting the air quality index (AQI) in urban areas. The study integrates pollutant factors (CO, NO₂, SO₂, PM_2.5), meteorological parameters (temperature, humidity, wind speed), and traffic data to determine air quality. For this purpose, 19 predictive models were developed and compared: 12 machine learning algorithms, 7 deep learning, and 1 forecasting model based on structural component analysis. The Random Forest Regression model, customized within the study, achieved the best results, with an R² score of 99.59%, an MAE of 0.22%, an MAPE of 0.68%, and an OP (Overall Precision) score of 95.61%. It was subsequently validated on unseen data and recorded a mean deviation of 0.58%. For short-term AQI forecasting (5 days), the AQIF model achieved an R² of 71.62%, an MAE of 0.4%, and an MAPE of 0.9%. The proposed solution was integrated into a web application with IoT infrastructure and real-time alert mechanisms. Future directions include expanding the dataset and optimizing hyperparameters for the deep learning models to increase accuracy, as well as integrating PM₁₀ and O₃ factors, along with the degree of industrialization and demographic level. Full article

Traditional HVAC designs often struggle to respond promptly and accurately to dynamic changes in complex environments like hospital usage. This paper introduces a novel framework that integrates Building Information Modeling (BIM), digital twin technology, and practical medical processes to transform HVAC design for hospital construction. The framework ensured a smarter (with a reduction of 90% in calculation time and an improvement of 38.20–53.24% in respondence speed) and cleaner environment after identifying and calculating the rational layout of functional areas and optimizing intersecting flow lines. A key innovation of this research was the application of Support Vector Machine (SVM) and deep learning algorithm (Long Short-Term Memory) networks for real-time pedestrian traffic prediction. The implementation was validated through multiple simulations and applications including horizontal and vertical traffic flow and negative pressure analyses for three distinct departments. The findings underline the potential of BIM and digital twins to optimize HVAC systems and hospital design, providing adaptive, data-driven solutions for both routine operations and emergency scenarios. This framework offers a scalable approach for modernizing healthcare infrastructure, ensuring resilience and efficiency in diverse operational contexts. Full article

With the continuous development of intelligent transportation systems (ITSs), traffic flow prediction methods have become the cornerstone of this technology. This paper comprehensively reviews the traffic flow prediction methods used in ITSs and divides them into three categories: statistics-based, machine learning-based, and deep learning-based methods. Although statistics-based methods have lower data requirements and machine learning methods have faster calculation speeds, this paper concludes that deep learning methods have the best overall effect after a comprehensive analysis of the principles, advantages, limitations, and practical applications of each method. Deep learning methods can overcome many limitations that traditional statistical methods and machine learning methods cannot surpass, such as the ability to model complex nonlinear relationships. Experimental results show that hybrid neural networks are significantly superior to traditional methods in terms of their prediction accuracy and generalization abilities. By combining multiple models and techniques, hybrid neural networks can improve the accuracy of traffic flow prediction under different conditions. Although deep learning methods have achieved remarkable success in short-term prediction, challenges still exist, such as the generalization of models in different traffic scenarios and the difficulty of long-term traffic flow prediction. Finally, this paper discusses future research directions and anticipates the future development of ITS technology. Full article

Accurately predicting road traffic flows is a primary challenge in the development of smart cities, providing a scientific basis and reference for urban planning, construction, and traffic management. Road traffic flow is influenced by various complex features, including temporal and weather conditions, which introduce challenges to traffic flow prediction. To enhance the accuracy of traffic flow prediction and improve the adaptability across different weather conditions, this study introduced a traffic flow prediction model with explicit consideration of weather factors including temperature, rainfall, air quality index, and wind speed. The proposed model utilized grey relational analysis (GRA) to transform weather data into weighted traffic flow data, expanded input variables into a new data matrix, and employed one-dimensional convolutional neural networks (CNNs) to extract valuable feature information from these input variables, as well as bidirectional long short-term memory (BiLSTM) to capture temporal dependencies within the time-series data. Bayesian optimization was employed to fine-tune the hyperparameters of the model, offering advantages such as fewer iterations, high efficiency, and fast speed. The performance of the proposed prediction model was validated using the traffic flow data collected at an intersection in China and on the M25 motorway in the United Kingdom. The results demonstrated the effectiveness of the proposed model, achieving improvements of at least 9.0% in MAE, 2.8% in RMSE, 2.3% in MAPE, and 0.06% in R² compared to five baseline models. Full article

Predicting Signal Phase and Timing (SPaT) information and confidence levels is needed to enhance Green Light Optimal Speed Advisory (GLOSA) and/or Eco-Cooperative Adaptive Cruise Control (Eco-CACC) systems. This study proposes an architecture based on transformer encoders to improve prediction performance. This architecture is combined with different deep learning methods, including Multilayer Perceptrons (MLP), Long-Short-Term Memory neural networks (LSTM), and Convolutional Long-Short-Term Memory neural networks (CNNLSTM) to form an ensemble of predictors. The ensemble is used to make data-driven predictions of SPaT information obtained from traffic signal controllers for six different intersections along the Gallows Road corridor in Virginia. The study outlines three primary tasks. Task one is predicting whether a phase would change within 20 s. Task two is predicting the exact change time within 20 s. Task three is assigning a confidence level to that prediction. The experiments show that the proposed transformer-based architecture outperforms all the previously used deep learning methods for the first two prediction tasks. Specifically, for the first task, the transformer encoder model provides an average accuracy of 96%. For task two, the transformer encoder models provided an average mean absolute error (MAE) of 1.49 s, compared to 1.63 s for other models. Consensus between models is shown to be a good leading indicator of confidence in ensemble predictions. The ensemble predictions with the highest level of consensus are within one second of the true value for 90.2% of the time as opposed to those with the lowest confidence level, which are within one second for only 68.4% of the time. Full article

Predicting traffic speed is an important issue, especially in urban regions. Precise long-term forecasts would enable individuals to conserve time and financial resources while diminishing air pollution. Despite extensive research on this subject, to our knowledge, no publications investigate or tackle the issue of imbalanced datasets in traffic speed prediction. Traffic speed data are often biased toward high numbers because low traffic speeds are infrequent. The temporal aspect of traffic carries two important factors for low-speed value. The daily population movement, captured by the time of day, and the weather data, recorded by month, are both considered in this study. Hour-wise Pattern Organization and Month-wise Pattern Organization techniques were devised, which organize the speed data using these two factors as a metric with a view to providing a superior representation of data characteristics that are in the minority. In addition to these two methods, a Speed-wise Pattern Organization strategy is proposed, which arranges train and test samples by setting boundaries on speed while taking the volatile nature of traffic into consideration. We evaluated these strategies using four popular model types: long short-term memory (LSTM), gated recurrent unit networks (GRUs), bi-directional LSTM, and convolutional neural networks (CNNs). GRU had the best performance, achieving a MAPE (Mean Absolute Percentage Error) of 13.51%, whereas LSTM demonstrated the lowest performance, with a MAPE of 13.74%. We validated their robustness through our studies and observed improvements in model accuracy across all categories. While the average improvement was approximately 4%, our methodologies demonstrated superior performance in low-traffic speed scenarios, augmenting model prediction accuracy by 11.2%. The presented methodologies in this study are applied in the pre-processing steps, allowing their application with various models and additional pre-processing procedures to attain comparable performance improvements. Full article

Pedestrian safety is a growing global concern, particularly in urban areas, where rapid urbanization and increased mobile device usage have led to an increase in distracted walking. This study investigates the impact of technological distractions, specifically mobile usage (MU), on pedestrian behavior and safety at signalized urban intersections. Data were collected from 11 signalized intersections in New Delhi, India, using video recordings. Key inputs to the modeling process include pedestrian demographics (age, gender, group size) and behavioral variables (crossing speed, waiting time, compliance behaviors). The outputs of the models focus on predicting mobile usage behavior and its association with compliance behaviors such as crosswalk and signal adherence. The results show that 6.9% of the pedestrians used mobile phones while crossing the road. Advanced machine learning models, including Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Recurrent Neural Networks (RNN), have been applied to analyze and predict MU behavior. Key findings reveal that younger pedestrians and females are more likely to exhibit distracted behavior, with pedestrians crossing alone being the most prone to mobile usage. MU was significantly associated with increased levels of crosswalk violation. Among the machine learning models, the CNN demonstrated the highest prediction accuracy (94.93%). The findings of this study have a practical application in urban planning, traffic management, and policy formulation. Recommendations include infrastructure improvements, public awareness campaigns, and technology-based interventions to mitigate pedestrian distractions and to enhance road safety. These findings contribute to the development of data-driven strategies to improve pedestrian safety in rapidly urbanizing regions. Full article

This study introduces a method for classifying traffic flow segments on expressways to estimate impact zones in merging/diverging sections and accident-prone sites. I propose a spatiotemporal dynamic segmentation approach that enables real-time identification of traffic hazard sections, reflecting changes in traffic flow, as opposed to traditional traffic analysis based on predefined segments in a node–link network. This methodology uses high-resolution vehicle trajectory data to precisely identify unstable and low-speed traffic sections. Using the geohash algorithm, the area is hierarchically segmented based on the standard deviation of speed in general traffic flow, facilitating the identification of unstable traffic flow patterns. For eight expressway routes, traffic flow was categorized into stable or minimum-size spaces. From a total of 1207 segments, 943 unstable flow segments were identified. The impact zones of the merging and diverging sections on Expressway 50 were analyzed using the results of spatial segmentation. Furthermore, by comparing traffic data before and after accidents, I assessed the short- and long-term effects of accidents on traffic flow. The proposed methodology provides precise data essential for reducing the likelihood of traffic accidents and for predicting post-accident congestion and duration. The patterns of such accident impact zones can contribute to preventing secondary accidents by providing advance information to following vehicles through various communication methods, including those involving autonomous vehicles. This enables effective traffic management strategies and rapid responses to accidents. Full article

Quantifying and predicting driving risks for connected autonomous vehicles (CAVs) is critical to ensuring the safe operation of traffic in complex environments. This study first establishes a car-following model for CAVs based on molecular force fields. Subsequently, using a convolutional neural network and long short-term Memory (CNN-LSTM) deep-learning model, the future trajectory of the target vehicle is predicted. Risk is quantified by employing models that assess both the collision probability and collision severity, with deep-learning techniques applied for risk classification. Finally, the High-D dataset is used to predict the vehicle trajectory, from which the speed and acceleration of a target vehicle are derived to forecast driving risks. The results indicate that the CNN-LSTM model, when compared with standalone CNN and LSTM models, demonstrates a superior generalization performance, a higher sensitivity to risk changes, and an accuracy rate exceeding 86% for medium- and high-risk predictions. This improved accuracy and efficacy contribute to enhancing the overall safety of connected vehicle platoons. Full article