Data-driven road safety enhancement: neural network–based accident classification and safe route identification using spatial network analysis

Prediction of Highway Multi‑Accident Sections Based on Machine Learning

A Sensor-Data-Driven Proactive Accident Detection and Traffic Prediction Method Based on Lane-Level Grid Partitioning and a Three-Dimensional Markov Model

The timely detection of road traffic accidents is essential for intelligent transportation systems. Leveraging multi-source sensor data including GPS, loop detectors, and vehicular sensors, this study proposes a proactive accident diagnostic method within a big-data framework. We introduce a lane-level traffic state representation that discretizes each lane into rectangular grids, enabling precise evaluation of local traffic conditions. To capture the spatio-temporal propagation of traffic disturbances, a three-dimensional Markov model is adopted, which accounts for both upstream–downstream traffic spread and temporal evolution, as well as historical features, to predict post-accident traffic dynamics. Experimental results demonstrate that the proposed method achieves high-accuracy lane-level accident detection and improves traffic prediction performance through the effective fusion of historical sensor records with real-time streaming data. The proactive detection mechanism efficiently reduces accident identification time, thereby mitigating potential secondary impacts. Additionally, the method proves effective in diagnosing other traffic anomalies, such as congestion, and for continuous monitoring of roadway incidents. These findings provide a practical sensor-enabled solution for accident detection and traffic flow prediction, offering a robust basis for real-time traffic management under intelligent network and big-data environments.

Integrating road infrastructure condition into intelligent transportation systems: a machine learning-based infrastructure-aware risk prediction approach

Although the majority of Intelligent Transportation Systems (ITS) and risk-prediction frameworks keep considering pavement quality and dynamic traffic behavior as separate phenomena, road infrastructure degradation and traffic flow instability both contribute to risky driving situations. This study combines segment-level Pavement Condition Index (PCI) data with actual traffic observations from New York City to present an integrated, data-driven methodology for simulating infrastructure-induced unsafe driving circumstances. A supervised machine learning model is developed by combining measures of traffic congestion, speed variation, and pavement deterioration to estimate hourly instability risk, which serves as a proxy for risky driving behavior. Evaluating on a temporally separated test set, the infrastructure-aware model achieves an ROC-AUC of 0.9804 and a PR-AUC of 0.9074, outperforming a traffic-only baseline (ROC-AUC 0.8785, PR-AUC 0.5833). These instability indicators correspond to high-level behavioral patterns commonly observed in ITS monitoring contexts, without relying on raw visual data. Model explainability using SHAP indicates that pavement condition and congestion are the most influential features, with comparable contributions to instability prediction. Predicted risk probabilities are geospatially mapped to identify infrastructure-driven hotspots, and the ORQCIAM framework demonstrates how such risk outputs can inform infrastructure-aware routing and maintenance prioritization. The findings reveal that machine learning enhanced with pavement condition data offers a data-driven approach for predicting hazardous driving situations and supporting infrastructure-aware decision-making, demonstrating how infrastructure-aware risk estimates might help with routing analysis and repair priority in future ITS applications.

A GIS-Based Framework for Identifying High-Risk Road Segments Using Spatial Analysis and K-Means Clustering for Road Safety Enhancement

Road traffic accidents on national highways pose a significant public health and economic challenge in Bangladesh, necessitating systematic safety assessment. This study analyzes accident trends, contributing factors, and spatial patterns by identifying accident-prone locations (blackspots) along the Kushtia–Jhenaidah National Highway (N704). Accident data for the period 2017–2021 were obtained from nearby police stations. In addition, a cluster random sampling approach was used to conduct a questionnaire survey involving 100 participants, including drivers and general road users, to capture behavioural insights related to accident occurrence. The study integrates descriptive statistical methods, such as trend analysis and frequency distribution, with spatial techniques including severity index evaluation, Kernel Density Estimation (KDE), and hotspot analysis.The findings indicate a decline in overall accident frequency from 2018 to 2021, while fatality rates increased in 2021. Heavy vehicles, particularly trucks, were identified as major contributors to accidents, and head-on collisions emerged as the most common crash type. Key risk factors include driver inexperience, mobile phone usage while driving, overspeeding, inadequate training, and nighttime driving conditions. The analysis further reveals that individuals aged 20–40 are the most affected group, with higher fatality rates among males and higher injury rates among females.A total of 35 accident-prone locations were identified, with several segments classified as blackspots based on accident frequency, injury severity, and fatality occurrence. The study recommends targeted interventions such as driver training, infrastructure improvement, enhanced enforcement, and coordinated policy actions to improve highway safety and reduce accident risks.

Road Traffic Accident Trends, Contributing Factors, and Predictive Analytics in India: A Data-Driven Analysis (2021–2023)

Road traffic crashes remain a major public-safety challenge in India, which records among the highest absolute crash and fatality counts of any country. This paper synthesises official crash statistics from India's Ministry of Road Transport and Highways (MoRTH) for 2021–2023 with recent peer-reviewed literature on machine-learning-based crash-severity prediction. National accidents rose from 412,432 (2021) to 480,583 (2023) and fatalities from 153,972 to 172,890. Overspeeding, non-use of helmets/seatbelts, and concentration of crashes on highways are the dominant factors; two-wheeler riders and pedestrians bear the largest fatality share. Recent ML/deep-learning models show measurable gains in severity-prediction accuracy, supporting more targeted interventions. Engineering, enforcement, and data-infrastructure recommendations are discussed. Keywords: road traffic accidents; road safety; crash severity prediction; machine learning; India; MoRTH

INTEGRATING SITUATIONAL FACTORS AND HETEROGENEOUS DATA FOR ENHANCED TRAFFIC ACCIDENT ANALYSIS

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.