A Composite Framework for Implementation of ICT Enabled Road Accident Prediction Using Spatial Data Analysis

Today, one of the top concerns for governments is road safety. There are many safety features built into cars, yet traffic accidents still happen frequently and are unavoidable. To lessen the harm caused by traffic accidents, predicting their causes has become the primary goal. In this situation, it will be beneficial to examine the frequency of accidents so that we can use this information to further aid us in developing strategies to lessen them. From this, we can deduce the connections between traffic accidents, road conditions, and the impact of environmental factors on accident occurrence. In order to construct an accident prediction model, I used machine learning techniques, including the Decision Tree, Random Forest, and Logistic Regression. The development of safety measures and accident prediction will both benefit from these classification systems. Several elements, including weather, vehicle condition, road surface condition, and light condition, can be used to predict road accidents. Three dataset files—accidents, casualties, and vehicles are loaded into this dataset. This allows us to forecast the severity of accidents.

The rapid development of AI shows its power and great development potential in practical engineering applications. Critical issues and potential solutions can reduce road traffic accidents and application of AI in road accident prediction. The published use of AI for road accident prediction is reviewed, presented, and represented as the main objective. The methods are collecting article data, quotations, presentation, and representation. The article data collection was obtained from 671 conference and journal articles in 2019-2023, but the suitability of articles that can be used is 69. Quotation produces a grouping of approaches, models, predictions, and benefits. The presentation showed that most approaches used were machine learning, the most used model was random forest, the prediction was mostly about severity, and the most benefit was about number reduction. Representation produces road accidents and related factors into factors in road accident predictions using artificial intelligence, so strategies and anticipation can be made to overcome them to improve road safety. AI in road accident prediction plays an important role in building predictive models with the hope that road accidents can be identified early, risk factors can be reduced, and effective preventive measures can be taken to improve road safety.

The Yos Sudarso arterial road, a critical ring road connecting industrial zones and ports in Central Java, experiences high accident rates (averaging 6 accidents/month from 2019 to 2022). Despite its economic significance, inadequate traffic signage and complex topography contribute to recurring accidents, necessitating a targeted safety analysis. This study investigates (1) the correlation between road complementary devices (e.g., traffic signs) and accident rates, and (2) the relationship between traffic volume and accident frequency, with a focus on high-risk vehicle groups. Field surveys and spatial analysis were conducted to evaluate road conditions, signage compliance, and accident hotspots. Data from 262 accidents were analyzed alongside traffic volume measurements and road equipment assessments. Key results include: (1) A strong correlation between insufficient traffic signs and accident occurrence, and (2) An inverse relationship between traffic volume and accidents, with Group 1 vehicles (e.g., cargo trucks) identified as most vulnerable. While compliant with regulations, road geometries revealed blind spots due to median vegetation. The study advocates for enhanced signage and ITS integration (e.g., adaptive signals) to mitigate accidents. It also calls for dedicated lanes for high-risk vehicles and further research into behavioral factors underlying the traffic volume paradox. These findings contribute to evidence-based policies for arterial road safety in urban-industrial corridors.

India possesses one of the world’s largest road networks, which is vital to the country’s economic development. Transportation of products and people is primarily centred on this network, which has about 5.8 million kilometres of road. Data analytics and machine learning approaches can be used to control and optimise this network. In this regard, we explored the application of classification algorithms to predict road surface quality using a dataset of synthetic road surface data. Several classification methods have been trained and evaluated, including Decision Trees, Random Forest, Logistic Regression, Support Vector Machine, XGBoost, CatBoost, and LGBM. The accuracy, precision, recall, and F1-score measures were used to compare the performance of various methods. Researchers have investigated the use of feature augmentation techniques such as Variational Autoencoder (VAE) to improve the performance of classification models. VAE is a deep generative model capable of learning a low-dimensional latent representation of high-dimensional input. Researchers can develop new features that represent the underlying structure of data by using VAE to road-related data such as traffic patterns and road conditions. Our results indicates the potential of applying machine learning algorithms to forecast road surface condition, which may be utilised to improve road network maintenance and safety.

The prediction of traffic accidents continues to provide a considerable problem, primarily because present models inadequately capture and integrate the dynamic condition of drivers with immediate environmental risks, resulting in weak severity predictions. This research proposes a Hybrid Risk Assessment Framework aimed at predicting accident severity through the integration of diverse data streams, including real-time driver physiological signals and current road conditions. The proposed system utilises a stacked ensemble architecture, incorporating a Bi-LSTM to model the temporal aspects of internal risk (e.g., Heart Rate Variability, RMSSD) and an XGBoost classifier for static exterior risk features (e.g., Road Surface, TIT). The forecasts from these specialised base-learners are integrated by a Meta-Classifier, allowing the framework to understand complex non-linear interactions. The findings indicate the enhanced effectiveness of the hybrid method, with a final accuracy of 92.1% and a macro-averaged F1-Score of 0.915. This performance markedly exceeds single-modal baseline models (e.g., XGBoost baseline F1-Score 0.829), validating the concept that decision-level data fusion is crucial for accurate accident severity prediction and facilitating the development of highly reliable proactive safety systems. https://doi.org/10.65470/james.6

Monitoring of street surface conditions for different conditions like black-top, water, ice, and snow is required for most transportation offices are in charge of street support. Data on street surface conditions can be utilized to survey the requirement for upkeep benefit, think about the adequacy of various treatment techniques, and assess the nature of the support administrations conveyed by contractual workers crosswise over various upkeep yards. The road sensor utilizes three wavelengths and one photograph finder to decide the powers that are reflected from the street surface and is then ready to evaluate the street condition. By connecting this sensor to a GPS and a smaller than normal remote embedded Internet framework, the street conditions can be related to the right street position, making it conceivable to utilize the data in various applications.

Road accidents are still a prominent urban issue, worsened by a growing population and uncertain weather patterns. Conventional accident prediction models use static historical data, which does not enable them to be responsive to real-time traffic patterns. This research is responding to the demand for predictive models that are adaptive, smart, and can facilitate smart city infrastructure through real-time data integration. Machine learning models—Multilayer Perceptron, Gradient Boosting, Random Forest, Support Vector Machine, and K-Nearest Neighbors—were tested with both historical and real-time traffic data. The models were optimized and trained on varied datasets to improve prediction accuracy. Of these, Gradient Boosting recorded the highest accuracy at 88.1%, followed by Random Forest at 83.73%, showing the power of ensemble learning techniques in predicting accidents. This research emphasizes the significance of real-time data integration for accident prediction and prevention. Merging environmental elements like weather conditions and traffic congestion improves prediction quality, allowing proactive prevention of accidents. By leveraging these models, cities can shift towards data-based road management, infrastructure planning, and congestion control. The results show that real-time models of accident prediction have the potential to enhance urban safety, opening the door to smarter and more efficient traffic management systems.

The sidewalks we use in our daily lives are made of various surface materials, such as asphalt pavement and concrete pavement. In terms of barrier-free access, Braille blocks are provided on the sidewalks. Barrier-free maps are also available to assist wheelchair users in their daily lives. The Barrier-Free Map shows the location of wheelchair-accessible restrooms and the status of barrier-free facilities at stations and stores. However, it does not indicate the type of road surface, deterioration conditions and other information necessary for wheelchair users to travel. Wheelchair users may experience problems in riding and driving due to uneven road surfaces, bumps, and joints between blocks. However, it is difficult to select a comfortable travel route in advance because the road surface condition cannot be determined without visiting the road. In addition, wheelchair users have the problem of not knowing whether they can drive on their own or need assistance without directly checking the road surface conditions. If the road conditions can be provided to wheelchair users in advance, they can take the most appropriate route. In this study, we report on a road surface estimation method that uses the vibrations generated during wheelchair driving. The following six types of road surfaces were used in the experiment. These road surfaces were wood flooring, two types of asphalt pavement, two types of interlocking block pavement, and a wood deck floor. The wheelchair used was equipped with four vibration sensors for data collection. The vibration sensors were mounted on a frame with casters and tires on its axis. The vibration data collected in the experiment was the result of a 20 [m] straight run on each of the road surfaces. For road surface estimation, a learning model was built for deep learning using spectrum images for the vibration data. As a result, five types of road surfaces could be discriminated with an accuracy of more than 80%.

A study on road accident prediction and contributing factors using explainable machine learning models: analysis and performance

A review on neural network techniques for the prediction of road traffic accident severity

Domain-specific data service models can retrieve critical features from frequently occurring road accident patterns (RAPs). The aim of this research is to propose scan efficient association rules’ mining-based pattern analysis which provides more accurate RAP prediction in frequent accident locations with the fastest matching pattern search from a RAP database (RAP DB). Association rules’ mining technique derives a correlation between frequent RAP and association among various attributes of a road accident. While the clustering technique discriminates different RAPs, Naïve Bayes Classification classifies and then predicts the severity of accident using Fuzzy Inference Engine (FIE) interfaced with RAP Case Library (RAP CL) using hybrid intelligence. The results of the proposed road accident data service model prove a significant increase in the accuracy of accident prediction compared to the reported results. A novel hybrid learning algorithm, interfaced with Scan Efficient Apriori (SEA) algorithm implemented, leads the fast RAP search from the first scan through RAP CL and retain new RAP in the RAP CL using case-based reasoning (CBR) during subsequent scanning. Thus, the RAP miner built proves road accident prediction using SEA, FIE and CBR with the highest accuracy and fast RAP set processing.

BackgroundGlobal health is shifting gradually from a limited focus on individual communicable disease goals to the formulation of broader sustainable health development goals. A major impediment to this shift is that most low- and middle-income countries (LMICs) have not established adequate sustainable funding for health promotion and health infrastructure.ObjectiveIn this article, we analyze how Thailand, a middle-income country, created a mechanism for sustainable funding for health.DesignWe analyzed the progression of tobacco control and health promotion policies over the past three decades within the wider political-economic and sociocultural context. We constructed a parallel longitudinal analysis of statistical data on one emerging priority – road accidents – to determine whether policy shifts resulted in reduced injuries, hospitalizations and deaths.ResultsIn Thailand, the convergence of priorities among national interest groups for sustainable health development created an opportunity to use domestic tax policy and to create a semi-autonomous foundation (ThaiHealth) to address a range of pressing health priorities, including programs that substantially reduced road accidents.ConclusionsThailand's strategic process to develop a domestic mechanism for sustainable funding for health may provide LMICs with a roadmap to address emerging health priorities, especially those caused by modernization and globalization.

Road surface conditions affect transport safety and driving comfort, particularly in snowy areas. This paper proposes a new method for detecting road surface conditions based on crowdsourced mobile sensing technology. The method can efficiently detect road surface conditions using motion sensors embedded in smartphones mounted on vehicles. Detecting road conditions using such sensors, which are usually loosely placed in the vehicle, nonetheless poses a challenge. Our approach comprises two modules: a clustering module and classification module. The clustering module uses a K-means algorithm to distribute the vehicle travel mode into a proper cluster. The classification module uses the random forest classifier, which is assigned to each group of vehicle travel mode, to classify the road surface conditions. We defined new road surface conditions as the estimation target, considering both the substance that covers the road surface and the shape of the road surface itself. The results show that our approach can detect road surface conditions with accuracy as high as 90%.

Neurotrauma Care: A Worldwide Perspective.

Road information services (RIS) is a major component of the information and communication technologies with the main purpose of RIS-based systems is to monitor road health conditions, weather information and traffic congestion. Considering the road conditions, there are various kinds of road surface types and anomalies with lack of efficient analysis of their behavior on the vehicle sensor measurements. Consequently, there are difficulties in detecting and categorizing the different road types and anomalies. This paper demonstrates road test results for the measurements of inertial sensors mounted in land vehicles while monitoring various road surface types and anomalies. In addition, a wavelet-based feature extraction together with statistical approach for the road types and anomalies are explored in this study. Two road test experiments on two different vehicles performed in Kingston, ON, Canada together with in-depth analysis are discussed in this paper.