Traffic modeling and accidental data analysis using GIS: A Review

Road traffic accident databases provide the basis for road traffic accident analysis, the data inside which usually has a radial, multidimensional, and multilayered structure. Traditional data mining algorithms such as association rules, when applied alone, often yield uncertain and unreliable results. An improved association rule algorithm based on Particle Swarm Optimization (PSO) put forward by this paper can be used to analyze the correlation between accident attributes and causes. The new algorithm focuses on characteristics of the hyperstereo structure of road traffic accident data, and the association rules of accident causes can be calculated more accurately and in higher rates. A new concept of Association Entropy is also defined to help compare the importance between different accident attributes. T-test model and Delphi method were deployed to test and verify the accuracy of the improved algorithm, the result of which was a ten times faster speed for random traffic accident data sampling analyses on average. In the paper, the algorithms were tested on a sample database of more than twenty thousand items, each with 56 accident attributes. And the final result proves that the improved algorithm was accurate and stable.

Analysis of traffic accident causes based on data augmentation and ensemble learning with high-dimensional small-sample data

In order to reduce casualties in traffic accidents, more analyses are required from the engineering and medical science viewpoints when investigating traffic accidents. A key technology in this respect is injury prediction, which reduces the response time of the emergency rescue teams through automatic collision notification, and evaluates the injury mechanisms and risks. In this study, research questions are clarified by studying the factors that influence side impact crashes in traffic accidents in Japan, and determining significant factors that reduce accident casualties by constructing an injury prediction method. In this study, statistical accident analysis is applied to a combination of data obtained from the Institute for the Traffic Accident Research and Data Analysis (ITARDA) in-depth accident investigation database (Micro Data) and ITARDA Macro Data which is the official database for traffic accidents that occur throughout Japan. First, 73 nearside and 108 farside crashes are selected from Micro Data. Second, an object variable and 22 explanatory variables from the accident information are selected and categorized. The object variable is the maximum of AIS (MAIS). Third, an ordinal logistic regression analysis is performed to construct the injury prediction models using these variables. Finally, a comparison of the obtained results with Macro Data validates these models, and the outlier cases show extraordinary accidents. The results show that the significant factors are delta-V, damage grade, and striking vehicle weight for nearside crashes; and delta-V, vehicle category, lap zone-vertical, and seat belt use for farside crashes. Thus, these two types of crashes have different factors. The MAIS predictive accuracy rate is approximately 75, and the judgment rate of fatal and serious injuries (MAIS 3+) is approximately 90 %. The prediction models can correspond to Macro Data. The accident conditions arising from ±1 MAIS residual exhibit several characteristics. Eight cases with a large degree of error in nearside crashes involved five cases of SUV or truck side impact crashes with serious chest injuries. In farside crashes, 12 cases with a large degree of error included four unbelted drivers with serious head or neck injuries. These cases indicate that these extraordinary accidents have additional factors that influence injuries. At present, there is no published research on the side impact prediction method using Japanese Micro Data and Macro Data. In the United States, several injury prediction methodologies have been studied. However, only a few studies refer to accidents with residuals. In conclusion, this study shows the results of the proposed injury prediction methods and significant factors in side impact crashes by using data on traffic accidents in Japan. These prediction models can correspond to documented accident data. Furthermore, the residual analysis conducted indicates characteristic accident factors such as SUV side impacts for nearside crashes and unbelted drivers for farside crashes. Further investigations into these outlier accidents would prove beneficial in reducing traffic accident casualties in Japan.

Analyzing accidents and developing elderly driver-targeted measures based on accident and violation records

This research develops and tests a computerized model for intersection accident data visualization and analysis. The model implemented a rule-based visualization module that automatically constructs the collision diagram according to traffic flow conflicts, where accident characteristics can be analyzed either individually or collectively. A data acquisition module was developed for accessing accident databases maintained in state transportation and public safety departments. Using a local database management system as part of the model, the user can select to analyze any accident data through Structured Query Language statements. In addition, this model has implemented a data analysis and graphing module for comparing similarities and identifying differences in accident data and for presenting the results of the analysis through charts and tables. The model was tested with accident data obtained from the Ohio Department of Public Safety in a number of scenario studies, and some of them were presented in this paper as examples. Because the model is very time efficient and user friendly, it can be developed into an effective engineering tool for field applications.

With the exponential increase in the urban population, urban transportation systems are confronted with numerous challenges. Traffic congestion is common, traffic accidents happen frequently, and traffic environments are deteriorating. To alleviate these issues and improve the efficiency of urban transportation, accurate traffic forecasting is crucial. In this study, we aim to provide a comprehensive overview of the overall architecture of traffic forecasting, covering aspects such as traffic data analysis, traffic data modeling, and traffic forecasting applications. We begin by introducing existing traffic forecasting surveys and preliminaries. Next, we delve into traffic data analysis from traffic data collection, traffic data formats, and traffic data characteristics. Additionally, we summarize traffic data modeling from spatial representation, temporal representation, and spatio-temporal representation. Furthermore, we discuss the application of traffic forecasting, including traffic flow forecasting, traffic speed forecasting, traffic demand forecasting, and other hybrid traffic forecasting. To support future research in this field, we also provide information on open datasets, source resources, challenges, and potential research directions. As far as we know, this paper represents the first comprehensive survey that focuses specifically on the overall architecture of traffic forecasting.

This report includes an analysis of traffic accident data in Kentucky for the years of 1986-1990. A primary objective of this study was to determine average accident statistics for Kentucky highways. Average and critical number and rates of accidents were calculated for various types of highways in rural and urban areas. These data can be used in Kentucky's procedure to identify locations that have abnormal rates or numbers of accidents. Another objective of this study was to provide data which can be used in the preparation of the problem identification portion of Kentucky's Annual Highway Safety Plan. County and city accident statistics were analyzed. A summary of results and recommendations in several problem identification areas is presented. These general areas include alcohol, occupant protection, speed, pedestrians, bicycles, motorcycles, and vehicle defects. Other areas included in the analysis are drugs, school bus accidents, and truck accidents.

Worldwide, road accidents have become more of a problem due to the exponential growth in vehicle traffic, which has led to a significant increase in both human mortality and economic losses. Traditional methods of accident analysis, which rely on static statistical models and clustering techniques, have limitations when it comes to accurately predicting future accidents because they fail to account for the nonlinear, dynamic, and multivariate aspects of accident causation. In order to address these constraints, this study presents a methodology for road traffic accident causation analysis and predictive modeling that is based on machine learning. After implementing thorough preprocessing steps like data cleaning, normalization, and class balancing using the Synthetic Minority Over-sampling Technique (SMOTE), the framework deploys classifiers like Decision Tree (DT), and Random Forest (RF). Analyses of comparative performance show that the Random Forest model maintains interpretability and routinely produces superior predictive accuracy, allowing for robust accident prediction. In addition, policymakers and traffic safety authorities can benefit from feature importance analysis's identification of significant causal determinants, such as weather conditions, roadway characteristics, and illumination factors. In addition to improving forecast accuracy, the results show that the suggested method connects black-box learning models with practical decision-making tools. Ultimately, our study aids in the creation of solutions that are scalable, interpretable, and deployable for intelligent transportation system design, proactive accident avoidance, and real-time traffic risk assessment.

This study analyzed traffic accident characteristics in Chungcheong Region through the factor analysis, cluster analysis and questionnaire survey using traffic accident analysis data for Chungcheong Region in order to improve Korea`s traffic safety level. Based on the analysis results, this study suggests traffic safety technologies suitable for the situation of Chungcheong Region. According to the results of the questionnaire survey carried out for transporters in Chungcheong Region, `the passage of pedestrians and aged people` and `a problem of lacking visibility` were the biggest threatening factors of major traffic accidents in Chungcheong Region. The facilities required the most were `lighting equipment in driving at night`, and the expected effect by the equipment installation was high. As a result of the factor analysis and cluster analysis using the traffic accident analysis data, traffic accident risk by city type in Chungcheong Region was higher in rural areas, compared to urban areas. Especially, this study confirmed the risks of pedestrian and agricultural machine traffic accidents were high. Therefore, this study suggests three technologies to improve traffic safety level in Chungcheong Region, based on the analysis results.

The aim of this study was to clarify the pattern of child pedestrian injury, injury severity, and its relation to collision velocity in bonnet-type-vehicle collision. In-depth data were retrospectively collected from the Institute for Traffic Accident Research and Data Analysis on pedestrians younger than 13 years old with any bodily injuries from collisions with bonnet-type vehicles between 1993 and 2004. Forty-seven patients from 43 collisions with a mean age of 6.9 ± 2.5 years were included in the study. Injury severity was not significantly different between patients who were hit by the front of the vehicle and those who were hit by the side of the vehicle. In front collisions, impact with the vehicle was associated with significantly higher Abbreviated Injury Scale (AIS) scores than those for impact with the road, especially for the lower extremities (mean: 1.2 vs 0.2, P < 0.001). Injury severity of the lower extremities and collision velocity were examined. The estimated collision velocity of the vehicle was not significantly different between patients with lower extremity AIS scores of 0 or 1 and those of 2 or 3. Some pediatric pedestrians suffer from collisions with bonnet-type vehicles without lower extremity fractures owing to the characteristics of child pedestrians. Providing injury prevention programs for children in communities and schools, developing active safety devices in the vehicle, and modifying the vehicle body to a pediatric pedestrian-friendly structure may increase pedestrian protection.

SUMMARYNetwork traffic monitoring and analysis are essential for effective network operation and resource management. In particular, multidimensional analysis for long‐term traffic data is necessary for comprehensive understanding of the traffic trend and effective quality‐of‐service provision considering the extremely dynamic behavior of the current Internet, where various types of traffic occur from high‐speed network links and greatly increasing number of applications. However, only limited analysis results are provided, as the existing network traffic analysis tools and systems are developed and deployed focusing on their own specialized analysis purposes. Consequently, it is difficult to understand the network comprehensively and deeply, which increases the necessity for multilateral analysis of long‐term traffic data. In this paper, we propose a novel traffic analysis model for large volumes of Internet traffic accumulated over a long period of time. TheNetCube, the proposed network traffic analysis model using online analytical processing (OLAP) on a multidimensional data cube, provides an easy and fast way to construct a multidimensional traffic analysis system for comprehensive and detailed analysis of long‐term traffic data by utilizing simple OLAP operations and powerful data‐mining techniques on various abstraction levels of traffic data to complete the analysis purpose. We validate the feasibility and applicability of the proposedNetCubetraffic analysis model by implementing a traffic analysis system and applying it to our campus network. Copyright © 2012 John Wiley &amp; Sons, Ltd.

Traffic accidents cause huge property and life losses, so it is important to study the characteristics of the traffic accidents to reduce the losses and prevent the accidents happening. Applying vectors' inner product method of fuzzy clustering, the traffic accidents data in 2010 in China was analyzed, and the fuzzy similar matrix was established. Then the square method was applied to calculate transitive closure, and the fuzzy equivalent matrix was obtained. Based on the different values of similarity coefficient, dynamic cluster analysis was carried on. Research results indicate that comparing the traffic accident index of the five kinds of traffic modes, namely automobile, motorcycle, tractor, non-motorized vehicle, and pedestrian, traffic accident probability of automobile is the highest, traffic accident probability of tractor, non-motorized vehicle, and pedestrian is similar and in middle level, and traffic accident probability of motorcycle is the lowest.

Traffic jam is increasingly aggravating in almost every urban area. Traffic forecast, traffic modeling, visualization can help to provide appropriate route and time for traveling and thus provides a significant impact on traffic jam reduction. For traffic forecasting, modeling and visualization, city-wide traffic data collection and analysis are needed, which is still challenging in many aspects. This paper aims to develop a tool for acquiring and processing traffic data from Google Maps that can be used for forecasting, modeling, and visualization. Dhaka city is used as a case study since there is no infrastructure available for traffic data collection. The traffic flow intensity of the road is analyzed to determine the congestion of the road. The flow intensity is used for traffic modeling, visualization, traffic prediction and many more.

The mining industry is a high-risk industrial activity. Human factors have been identified as the most common cause of major accidents in the mining industry. The Incident Management System of PT X, which is a company in exploration, mining, processing, and marketing sector of copper, gold and silver concentrate, part of the state-owned mining holding enterprise Mining Industry Indonesia, documented 322 cases of accidents within the 2018-2022 period. This research aims to analyze accident data at PT X using the Human Factor Analysis and Classification System-Mining Industry (HFACS-MI) framework. Methods. This research collected qualitative data for 322 accident cases at PT X in 2018-2022 from the incident management system database categorized as recordable injuries. Factors causing the accident were coded using the HFACS-MI framework. Accident data analysis used descriptive statistics. Results. The study findings revealed that 84% of all accidents involved contractor workers and 16% involved permanent workers at PT X. The results of the analysis using the HFACS-MI framework showed that each layer or level contributes to accidents, namely external factors by 44%, organizational influence by 68%, unsafe leadership by 90%, preconditions for unsafe acts by 99%, and unsafe acts by 99.7%. Conclusion. These findings emphasize the need to reduce the number of human errors during mining operations to reduce the current accident trend. The HFACS-MI framework has proven to be a valuable tool for robust accident analysis of human factors in mining.

More and more complicated driving environments and vehicle interactions have caused more and more crashes in China. This paper presents a method to analyze fundamental attributes for severe crashes on mountainous highways using clustering techniques. Firstly, the relationship between traffic accidents and driver behavior was discussed. Secondly, traffic accident data were collected and classified according to different crash types. Thirdly, cluster techniques were used to analyze the data and driver behavior. The results illustrated that there were four key driver behavior factors contributing to crashes on mountainous roads. These typical driver behavior factors could be used to develop effective countermeasures against traffic crashes for various scenarios in different driving environments.