Jul 2026· NATURAL AND MAN-MADE RISKS (PHYSICO-MATHEMATICAL AND APPLIED ASPECTS)· 0 citations· 8 references
TL;DR
The impact of diverse information on crash frequency and severity estimates is identified, thereby improving road safety, optimizing traffic management, and refining crash prevention approaches and technologies.
Abstract
Road traffic accidents (RTAs) pose a significant threat to public safety and transportation networks, requiring advanced modeling techniques to study their causes, trends, and consequences. The complexity of RTAs modeling stems from the need to integrate diverse data sources, including spatiotemporal changes, environmental variables, vehicle and driver characteristics, and infrastructure elements. Recent advances in machine learning, including graph neural networks, attention-based deep learning models, and hybrid tree classifiers, have improved prediction accuracy and interpretability. These methods leverage heterogeneous data to provide real-time crash prediction, severity assessment, and identification of problem areas. However, obstacles remain, such as data sparsity, class imbalance, and model interpretability. This article reviews modern approaches to RTAs modeling and identifies the impact of diverse information on crash frequency and severity estimates, thereby improving road safety, optimizing traffic management, and refining crash prevention approaches and technologies. Promising research directions in this area of modeling and machine learning are identified: integration of heterogeneous data, use of causal machine learning methods and real-time decision support systems.
Traffic accidents remain a persistent challenge for urban safety, particularly in Medan City, Indonesia, where heterogeneous road networks, mixed traffic flows, and varying environmental conditions complicate risk prediction. Existing statistical and classical machine learning methods often fail to capture the nonlinear spatial–temporal dependencies inherent in such environments, creating a gap in accurate, context-specific accident risk forecasting for developing countries. This study addresses this gap by proposing a Hybrid CNN–LSTM Deep Learning Model that jointly learns spatial features from georeferenced accident maps, road networks, and traffic density heatmaps, and temporal dependencies from historical accident logs, GPS traces, meteorological data, and road surface conditions. Unlike prior models, the proposed framework is explicitly tailored to localized urban traffic patterns in Medan, enabling robust performance under heterogeneous and data-imbalanced conditions. Data preprocessing included cleaning, normalization, and categorical encoding, followed by model training with an Adam optimizer and tuned hyperparameters. Experimental evaluation against baseline models—pure CNN, pure LSTM, and Random Forest—demonstrated statistically significant improvements (p < 0.05), with the hybrid CNN–LSTM achieving an accuracy of 96.8% (95% CI: 96.5–97.1%), precision of 96.5%, recall of 96.7%, and F1-score of 96.6%, outperforming baselines by up to 5% in predictive accuracy. The model effectively identified high-risk spatial clusters and peak accident periods, offering actionable intelligence for targeted safety interventions. These findings highlight the model’s potential for integration into intelligent transportation systems to support real-time monitoring, proactive policymaking, and enhanced urban traffic safety management.
Rusmin Saragih, Suria Alamsyah Putra, Togu Harlen Lbn Rajab et al.· JOIV: International Journal...· 0 citations
Road traffic accidents continue to pose a significant challenge to public safety, resulting in substantial human suffering, economic losses, and increasing pressure on transportation systems. This study proposes a data-driven intelligent transportation framework that integrates machine learning and spatial network analysis to support accident severity prediction, risk-aware route recommendation, and emergency response. A comprehensive dataset comprising traffic conditions, weather information, temporal attributes, roadway characteristics, vehicle information, and driver-related factors was analysed to identify the key determinants of accident severity. Multiple machine-learning models were evaluated, and the Multi-Layer Perceptron (MLP) classifier achieved the highest predictive performance, attaining an overall accuracy of 91.2%. To transform predictive outcomes into practical safety interventions, the proposed framework combines accident severity prediction with GPS-enabled spatial network analysis to identify high-risk road segments and recommend safer alternative routes. In addition, an automated SMS notification mechanism is incorporated to provide location-aware emergency alerts when high-risk situations are detected. The integration of predictive analytics, spatial risk assessment, safety-oriented routing, and emergency communication establishes a comprehensive decision-support framework for proactive accident prevention and transportation-safety management. The experimental results demonstrate that the proposed approach can effectively support safer mobility, improved situational awareness, and enhanced emergency response within intelligent transportation environments.
V. Naresh, Ayyappa Dullam· European Transport Research...· 0 citations
This systematic review critically examines recent advancements in deep learning-based traffic flow prediction models, emphasizing studies published between 2018 and 2026 and indicates that although graph-based and transformer-based architectures currently achieve state-of-the-art predictive performance, integrating explainability, real-time adaptation, and privacy-preserving learning remains a significant research priority.
Dr. P. K. S. Bhadauria· International Journal of Cre...· 0 citations
This study proposes a deep-learning-based approach for short-term traffic-state classification using real-world traffic data collected during 2022 at the Alésia intersection in Paris, and demonstrates that recurrent architectures substantially outperform the ANN baseline, highlighting the importance of temporal dependencies in traffic-state classification.
Chaymae Chouiekh, Ali Yahyaouy, M. A. Sabri et al.· Vehicles· 0 citations
A generic framework is presented which exploits the zero-shot, few-shot and multi-modal capabilities of foundation models to forecast traffic flow, predict traffic incidents and improve public transit schedules.
W. T L· International Journal of Com...· 0 citations
Real-time traffic risk prediction, enabled by the simultaneous extraction of traffic state variables and their associated risks from vehicle trajectory data, provides a promising approach for proactive traffic safety management. However, existing studies overlook the costs associated with misprediction and the varying consequences of different misprediction types, which undermines the reliability of prediction results. To address these gaps, this study employs empirical data sourced from the NGSIM dataset, from which traffic state variables and risk data aggregated over 5‑second intervals are extracted. Furthermore, this study refines traffic risk classification into four levels, and incorporates misprediction costs into the prediction process through a cost-sensitive learning framework, with the optimal cost coefficients calibrated using a Genetic Algorithm (GA). By integrating this framework with four baseline models, four enhanced models are proposed and systematically evaluated in terms of prediction performance (e.g., precision) and computational efficiency. Results demonstrate that the proposed models consistently outperform their baseline counterparts across multiple evaluation metrics, particularly in identifying high-risk events. Moreover, the computational time of the proposed models remains within acceptable limits for real-time deployment. Reliability analysis further confirms the robustness of the GA-based cost coefficient optimization process.
Song Chen, Bowen Cui, Ande Chang· Scientific Reports· 0 citations