Application of Spatial Data Mining in Accident Analysis System

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

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 & Sons, Ltd.

Identifying the (Hot Spots) for traffic accidents in Erbil City A study of the problems of the Urban Environment

Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks

Traditional models of usability assume that usability is a quality that can be designed into a particular artifact. Yet constructivist theory implies that usability cannot be located in a single artifact; rather, it must be conceived as a quality of the entire activity in which the artifact is used. This article describes a distributed approach to usability, based on activity theory and genre theory. It then illustrates the approach with a four-decade examination of a traffic accident location and analysis system (ALAS). Using the theoretical framework of genre ecologies, the article demonstrates how usability is distributed across the many official and unofficial (ad hoc) genres employed by ALAS users.

A traffic accident analysis method based on the accident risk index according to the combination of alignment elements is developed and tested applying geographic information systems (GIS). This study is based on crash experience on curves of 4lane expressways in Korea from 1996 2000, and that finding relates only to curves. Impacts of alignment elements on traffic accidents have been identified by examining accidents that occurred under different alignment conditions and by investigating traffic accident risk indices (TARI). Traffic accident rates are estimated more reasonably by considering the interaction between alignment factors and the design consistency. Traffic accident risk indices and risk grades are suggested in the traffic accident data-based approach. Finally, estimating traffic accident rates, evaluating the level of risk and then visualizing information graphically by means of GIS are combined into one system called the risk rating system. This risk rating system is expected to play a major role in designing expressways and developing countermeasures for expressway sections susceptible to traffic accidents.

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.

The application of reliability models in traffic accident frequency analysis

Introduction. The ever-increasing global road traffic accidents is caused by several risk factors: human factors, vehicle factors, and road environmental factors. In the Philippines, 12,000 road users die annually due to road crashes and almost one-third are alcohol-related crashes.Objectives. This study aims to investigate the effects of suspected alcohol intoxication on the severity of injuries among drivers and passengers in a major urban region in the Philippines using the Metro Manila Accident Recording and Analysis System (MMARAS) of the Metropolitan Manila Development Authority from 2005 to 2020.Methods. The Metro Manila Accident Recording and Analysis System (MMARAS) was used to determine the data on trends and association for alcohol-related road crashes, while descriptive and inferential statistics such as logistic and multinomial regression were used to determine trends and associations.Results. Suspected alcohol intoxication road crash cases in Metro Manila have decreased from 45 cases in 2005 to 31 cases in 2020. The year 2011 recorded the highest cases of alcohol-suspected road crashes. The years 2010, 2011, and 2012 recorded the highest number of driver’s death while 2005, 2006, 2014, and 2019 recorded higher deaths for passengers. Cars and motorcycles are the most commonly involved vehicles in alcohol-suspected road crashes, while Quezon City (n=307) and Marikina City (n=267) are the common places of incidence for drunk-driving accidents. On the other hand, alcohol intoxication increases the risk of driver’s death (OR=9.16; 95% CI [5.388-15.574]) and injury (OR=5.22; 95% CI [4.487-6.074]). While intoxicated, hitting an object (OR = 12.557; 95% CI [1.478-106.694]) and truck collisions (OR=7.176; 96% CI [1.261-40.817]) can increase the risk for driver’s death. Meanwhile, side swipe (OR=2.330; 95% CI [1.029-5.277]) and angle impact (OR= 7.972; 95% CI [1.804-35.227]), increase the odds for driver injuries.Conclusion. Suspected alcohol intoxication road crashes can be prevented; however, a collaborative effort is needed. The government is responsible for road safety education as well as effectively enforcing road safety-related laws, while the road users shall use the roadways responsibly.

In recent years, we have very often encountered increasing accident statistics where pedestrians are involved in traffic accidents. These collisions between pedestrians and vehicles are often associated with inattention on the part of the pedestrians and, from the drivers’ point of view, the reason was insufficient attention to driving the vehicle. Pedestrians are among the most vulnerable road users. Compared to other types of traffic accidents, traffic accidents involving motor vehicles and pedestrians are specific in that, even at relatively low impact speeds, relatively serious injuries or even death can occur. The analysis of traffic accidents involving the most vulnerable road users is of particular social importance. The paper deals with the use of simulation programs in the analysis of an accident event. In the analysis of the accident event, a mathematical graphic analysis of the mutual positions of vehicles and pedestrian by means of 3D visualization, the driving technique of vehicle drivers and the possibilities of avoiding a traffic accident, are evaluated.KeywordsAnalysisRoad accidentPedestrianVehiclesSimulation

Autonomous vehicles operate without direct human intervention, which introduces safety risks that differ from those of conventional vehicles. Although many studies have examined safety issues related to autonomous driving, high-risk situations have often been defined using single indicators, making it difficult to capture the complex and evolving nature of accident risk. To address this limitation, this study proposes a structured framework for defining and analyzing high-risk situations throughout the traffic accident process. High-risk situations are described using three complementary indicators: accident likelihood, accident severity, and accident duration. These indicators explain how risk emerges, increases, and persists over time. Based on this concept, a framework for traffic accident visualization analysis is developed to support phase-specific risk assessment and visualization. The framework combines accident-phase information with factor-level risk contributions, allowing systematic identification of key factors and their interactions across different accident stages. Using combinations of the three indicators, high-risk situations are classified into twenty-seven distinct types, providing a clear typology for complex accident scenarios involving autonomous vehicles. The applicability of the proposed framework is demonstrated through two representative accident scenarios with different risk characteristics. The results show that the framework effectively captures interactions among multiple risk factors, explains how risk levels change from pre-crash to post-crash phases, and identifies contributing factors that are difficult to detect using conventional traffic accident investigation methods. Overall, the proposed framework offers a practical basis for autonomous vehicle accident analysis, safety evaluation, and policy-related decision-making.

Abstract. Problem. Cities around the world are increasingly facing challenges related to congestion, delays and the environmental impact of those problems. The paper examines the critical need for innovative solutions to improve the efficiency of traffic flow management, offering a comprehensive study of the role of data arrays in solving the problem of urban mobility. The main goal is to explore and find out how the use of data sets can improve traffic management, contributing to the development of new traffic management systems and informed decision-making. By summarizing information from various sources, the article aims to provide a holistic understanding of the theoretical foundations, practical applications and new trends in the use of datasets for traffic optimization. Drawing on a range of sources, ranging from dynamic algorithms to real-world case studies in Los Angeles, the paper examines the theoretical foundations, applications of dynamic arrays, and the development of traffic data management. The results demonstrate the transformative effect of dynamic arrays on traffic flow management. Real-world implementations demonstrate tangible benefits, including improved signal synchronization, increased security, and data-driven decision-making. New trends such as 5G, edge computing and advanced sensor networks are considered to be the main elements shaping the future of traffic data management. The practical value of the work lies in its potential to inform urban planners and technology developers about the transformative possibilities of data sets. By understanding practical applications, challenges and emerging trends, stakeholders can make informed decisions to improve urban mobility, reduce congestion and shape the future of intelligent traffic solutions. The work provides a structured study of data sets in the management of traffic flows, offering ideas that contribute to the possible integration of intelligent solutions for traffic and a more comfortable urban life.

The responsiveness of motor vehicle death rates of youths aged fifteen through twenty-four to variations in the cost of beers as reflected by differences in State excise tax rates on beer is investigated. Evidence for this age group is provided on the extent to which declining real beer excise taxes have contributed to increases in fatal motor vehicle crashes and on the extent to which increases in real beer taxes can serve as a potent instrument in the antidrinking campaign. The effect of an increase in the legal drinking age on youth motor vehicle deaths is also examined.

This study analyzes how the number of traffic accidents can be effectively reduced by detailed study over the documented accidents. In this paper, we propose a new method called the traffic accident causes analysis based on dominance rough set theory. This method improvises on the limitations of Pawlak's rough set model in analyzing the causes of accidents, such as using discrete continuous attributes to obtain unreasonable rules. Furthermore, the paper combines facts about traffic accidents and the objective data processing ability of rough set theory. Specifically, every single cause of accidents is sorted according based on our experience and then relevant algorithms are applied to illustrate the relationship. Lastly, an example is demonstrated that shows that this method reduces the uncertainty of discretization of continuous attributes and excludes the causes that rule against human empirical knowledge.

Comparison of Multivariate Regression Models and Artificial Neural Networks for Prediction Highway Traffic Accidents in Spain: A Case Study