Patterns Of Traffic Accidents Research Articles

Comprehensive Evaluation of Resilience for Qinling Tunnel Group Operation Safety System Based on Combined Weighting and Cloud Model

An extensive network of tunnels has recently been constructed in the Qinling Mountains. Characterized by high and steep terrain, this network has led to frequent traffic accidents. To address this issue, this paper introduces the theory of resilience into the evaluation system of safety systems during the operation period of highway tunnel groups. Based on this, this paper establishes a resilience evaluation index system for the operation safety system of highway tunnel groups, including a human system, vehicle system, and road system. To address both qualitative and quantitative issues concerning the indicators, this paper employs the analytic hierarchy process (AHP) and entropy weight method to combine and assign weights to the resilience evaluation indicators. Subsequently, the cloud model method is utilized to quantify the level of resilience of the highway tunnel group safety system during the operation period. The study results unveiled the patterns of traffic accidents within the Qinling Tunnel Group from the perspectives of vehicle, road, and human factors. The final weight allocation reveals that the road system has the highest proportion, exerting the greatest influence as a primary level index. Moreover, by taking the Qinling Tunnel Group on the Xihan Expressway as an engineering example, the resilience level of the case project was analyzed and obtained. Proposals for enhancing resilience were put forth, taking into account the project’s unique attributes, encompassing adaptability, resistance, and recovery. Overall, this study validates the feasibility and reliability of the proposed method for assessing the resilience of highway networks, offering empirical support for transportation administrators in the implementation of resilience-enhancing strategies.

Spatial-temporal and trend analysis of traffic accidents in AP Vojvodina (North Serbia)

Abstract The objective of this study is to analyse the spatial-temporal patterns of traffic accidents using data from 2011 to 2020 for the AP Vojvodina. The spatial-temporal patterns of traffic accident hotspots were identified at the municipality level using the Getis–Ord G i ⁎ {G}_{i}^{\ast } statistic in ArcGIS Pro software. Trends in traffic accidents were analysed using the Mann-Kendall (MK) statistic. A certain trend in the number of traffic accidents, fatalities, and injuries was detected in 15 out of 45 municipalities. In two municipalities, the trend in the number of traffic accidents is decreasing, while in seven, it is increasing. In three municipalities, a decreasing trend in the number of traffic fatalities was observed. A decreasing trend in traffic injuries was identified in five municipalities. Hotspot analysis on the number of traffic accidents and traffic injuries revealed hotspots in seven municipalities, while no hotspots were detected in the number of traffic fatalities. In the traffic casualties weighted number, persons in tractors, hotspots were identified in two municipalities. This research has the potential to enhance traffic safety by directing targeted safety measures towards identified hotspots. Such measures could ultimately lead to a reduction in traffic accidents, consequently curbing the number of both fatalities and injuries.

Spike of Motorbike Accidents in Bangladesh: An Emerging Public Health Concern.

The on-road traffic accident is a significant public health concern for Bangladesh, impeding the country's way to building safe, resilient, and sustainable cities and human settlements. On-road accidents and causalities doubled in the country between 2011 and 2020, and it increased by 30% in 2021, a shifting pattern of traffic accidents with an increasing number of motorbike accidents. The fatalities by motorcycle traffic accidents were only 4% in 1995 and rose substantially to 35% in 2021, and the situation has been more alarming during the festivals, especially Eid in Bangladesh.

Exploring Spatiotemporal Patterns of Expressway Traffic Accidents Based on Density Clustering and Bayesian Network

Exploring spatiotemporal patterns of traffic accidents from historic crash databases is one essential prerequisite for road safety management and traffic risk prevention. Presently, with the emergence of GIS and data mining technologies, numerous geospatial analysis methods have been successfully adopted for traffic accident analysis. As characterized by high driving speeds, diverse vehicle types, and isolated traffic environments, expressways are confronted with more serious accident risks than urban roads. In this paper, we propose a combined method based on improved density clustering and the Bayesian inference network to explore spatiotemporal patterns of expressway accidents. Firstly, the spatiotemporal accident neighborhood is integrated into the DBSCAN clustering algorithm to discover multi-scale expressway black spots. Secondly, the Bayesian network model is separately employed in both local-scale black spots and regional-scale expressway networks to fully explore spatially heterogenous accident factors in various black spots and expressways. The experimental results show that the proposed method can correctly extract spatiotemporal aggregation patterns of multi-scale expressway black spots and meanwhile efficiently discover diverse causal factors for various black spots and expressways, providing a comprehensive analysis of accident prevention and safety management.

Spatiotemporal Analysis of Traffic Accidents Hotspots Based on Geospatial Techniques

This paper aims to explore the spatiotemporal pattern of traffic accidents using five years of data between 2015 and 2019 for the Irbid Governorate, Jordan. The spatial pattern of traffic-accident hotspots and their temporal evolution were identified along the internal and arterial roads network in the study area using spatial autocorrelation (Global Moran I index) and local hotspot analysis (Getis–Ord Gi*) techniques within the GIS environment. The study showed a gradual increase in the reported traffic accidents of approximately 38% at the year level. The analysis of traffic accidents at the severity level showed a distinguished spatial distribution of hotspot locations. The less severe traffic accidents (~95%) occurred on the internal road network in the Irbid Governorate’s towns where the highest traffic volume exist. The spatial autocorrelation analysis and the Getis–Ord Gi* statistics with 99% of significance level showed clustering patterns of traffic accidents along the internal and the arterial road network segments. Between 2015 and 2019, a notable evolution of the traffic-accident hotspots clusters was pronounced. The results can be used to guide traffic managers and decision makers to take appropriate actions for enhancing the hotspot locations and improving their traffic safety status.

Penerapan Algoritma Aprioti Pada Riwayat Data Kecelakaan Lalu Lintas

With the increase of vehicle users, traffic accidents tend to happen more often. One of many ways to minimize the occurrence of traffic accidents is to process accident data history using data mining techniques. This technique is utilized in order to gain information regarding the relational pattern of traffic accidents. The data mining technique used is the association rule technique with the Apriori algorithm. One of the stages of analysis that has attracted the eyes of many researches to produce an efficient Apriori algorithm is analyzing the frequency pattern of an association that can be identified with two benchmarks; Support and Confidence. Currently, the determination of the minimum support value will be repeated by the user until it reaches a positive correlation value. This study applies a certain method to determine the minimum support value with the final result of achieving positive correlation on all datas as a reference for the lift ratio value >1 and getting 6 of the most frequent traffic accident patterns

Implementasi Data Mining Untuk Menentukan Pola Penyebab Kecelakaan Lalu Lintas Di Wilayah Kota Palembang Menggunakan Algoritma FP-Growth

Palembang City is one of the big cities in Indonesia. Along with the increasing population and the increasing number of motorized vehicles, it will certainly have an impact on the increasing number of traffic accidents in the city of Palembang. In this study, the writer will determine the pattern of traffic accidents by using the fp-growth algorithm and using various variables. The variables that will be used consist of weather, time of incident, road geometry, profession, level of injury. This research is expected to be a reference for the police to be able to take anticipatory measures in order to reduce the number of traffic accidents in the Palembang City area. The fp-growth algorithm can be applied properly to determine the pattern of the causes of traffic accidents in the city of Palembang by using 2 minimum support of 40% and 50% and 2 minimum confidence of 70% and 90%. Based on the resulting rules, there are rules with the highest confidence value of 98% with these rules: When an accident occurs with a Side-Side accident type, the accident occurs in sunny weather conditions

Spatial analysis of traffic accidents based on WaveCluster and vehicle communication system data

The frequent occurrence of traffic accidents has always been an important problem troubling traffic safety management, so exploring the law and characteristics of case occurrence in a space area has profound significance for the prevention of traffic accidents. Starting from the space-time angle and based on the traffic accident data, this article firstly carries out the wavelet decomposition of the incident data of time series to realize the problem optimization of sparse matrix and then studies the spatial differentiation pattern of traffic accidents through the k-means clustering method. And under the formed differentiation pattern, the spatial and temporal laws of the incident are deeply analyzed. Finally, accident causes based on vehicle information system data are analyzed. The results show that the traffic accident space in Beijing is divided into 5 categories, among which, the hot spot space is the area with large traffic volume, diverse driver quality, or the junction of urban and rural roads, and the vehicle information system distracting the driver’s attention is also the cause of accidents from a micro view through vehicle information system data.

A space-time varying graph for modelling places and events in a network

ABSTRACTModelling topological relationships between places and events is challenging especially because these relationships are dynamic, and their evolutionary analysis relies on the explanatory power of representing their interactions across different temporal resolutions. In this paper, we introduce the Space-Time Varying Graph (STVG) based on the whole graph approach that combines directed and bipartite subgraphs with a time-tree for representing the complex interaction between places and events across time. We demonstrate how the proposed STVG can be exploited to identify and extract evolutionary patterns of traffic accidents using graph metrics, ad-hoc graph queries and clustering algorithms. The results reveal evolutionary patterns that uncover the places with high incidence of accidents over different time resolutions, reveal the main reasons why the traffic accidents have occurred, and disclose evolving communities of densely connected traffic accidents over time.

Morbilidad y mortalidad por accidentes de tránsito según componentes temporales, Ecuador

Introducción: en Ecuador los accidentes de tránsito son considerados como un problema de salud pública, por lo que ocupan los primeros puestos en tasa de fallecimientos a nivel mundial. Objetivo: describir el componente temporal como factor incidente de la morbilidad y mortalidad por accidentes de tránsito en el año 2016. Material – Métodos: análisis descriptivo de corte transversal con representación temporal de los siniestros, víctimas con lesión y personas fallecidas a través de datos estadísticos oficiales. Se calcularon estadísticos descriptivos, tasas de morbilidad y mortalidad, Coeficiente de Correlación de Pearson (p<0.05; p<0.01) y Riesgo Relativo (RR; IC95 %). Resultados: se registraron un total de 30.269 (2.522± 233) siniestros, 21.388 víctimas con lesión (1.782 ±150) y 1.967 (164 ±16) personas fallecidas por accidentes de tránsito Las tasas de morbilidad mensual presentan estacionalidad a diferencia de la mortalidad (p<0.05): junio (71,6 x 1.000), octubre (74,7 x 1.000) y noviembre (71,3 x 1.000). Los días sábados y domingos la probablidad de sufrir lesiones (RR: 1,05; lC95 %: 1,03-1,07) y fallecer (RR: 1,46; lC95 %: 1,34-1,59) es mayor respecto de lunes a viernes. En especial, en horario nocturno a partir de las 20:00 h. (p<0.05). Conclusiones: los accidentes de tránsito siguen un patrón temporal de agrupamiento a largo de todo el año. Los resultados hallados constituyen una valiosa información para establecer estrategias y campañas puntuales en cuanto al mes, día y franjas horarias. Es necesario continuar con estudios que incluyan períodos más amplios para conocer los cambios en los patrones temporales de los accidentes de tránsito, promover una cultura de seguridad vial y peatonal en la sociedad ecuatoriana y mejorar los sistema estadística en el país.

Spatial and Temporal Analysis of Seasonal Traffic Accidents

This paper presents an approach to analyze spatial and temporal (spatiotemporal) patterns of traffic accidents and to organize them according to their level of significance. This approach was tested using three years (2011-2013) of traffic accident data for Sherbrooke. The spatiotemporal patterns of traffic accidents were analyzed using kernel density estimation (KDE) for four different seasons. Two different crash measures were compared: simple crash counts and severity-weighted crash counts. The results show that severity-weighted crash counts reveal the effect of a single fatal/severe injury or light injury crash on the patterns. However, the lack of a significance test is the main drawback of the KDE. Therefore, this paper integrates the KDE with local Moran’s I to identify clusters of statistical significance for traffic accidents within each area. Thus, after the density is calculated by the KDE, it is then applied as the attribute (input value) for calculating local Moran’s I. Our findings show that the method was successful to detect traffic accident clusters and hazardous areas in Sherbrooke.

Improvement of the country safe community program in view of changing the pattern of traffic accidents in the under-19 age group in Iran

Improvement of the country safe community program in view of changing the pattern of traffic accidents in the under-19 age group in Iran

Benefit of Spatial Pattern of Traffic Accidents in Municipal Scale as Geography Teaching Materials of School

Benefit of Spatial Pattern of Traffic Accidents in Municipal Scale as Geography Teaching Materials of School

Patterns of Traffic Accidents Among Elderly Pedestrians in Sweden

The objective of this study is to characterize the nature and space-time patterns of traffic accidents involving elderly pedestrians in Sweden, in order to suggest preventive measures. The analysis is based on elderly pedestrian accidents from 2010 to 2014 using an age adjusted standardized elderly accidents ratios (ASEAR), Geographical Information Systems (GIS) and spatial statistics techniques. Findings show that the geography of elderly traffic accidents is far from being homogenous across the country: although most accidents happen in urban municipalities, 30 per cent of municipalities classified as accessible rural exhibit relatively high-standardized accidents ratios. They happen often in daylight hours, on weekdays and in the coldest months of the year. Most of the cases are single accidents (e.g. self-inflicted fall); they happen in street segments/intersections and pedestrian/bicycle path, some affected by environment conditions such as icy or uneven surfaces. Findings of the study call for preventive actions that are sensitive to the nature of these accidents in different temporal and spatial contexts.

Acute aortic transection caused by traffic accident: As a forensic case

Traffic accidents are one of the most common causes of blunt traumatic injuries and also significant causes of mortality and morbidity. To know injury patterns of traffic accidents will ensure early medical intervention and will reduce mortality and morbidity and also will facilitate the assessment of prevailing findings in criminal and compensation cases. In this case report, it was aimed to draw attention to the forensic subjects and the features of forensic cases via a 46-year-old male patient who had acute aortic transection due to traffic accidents.

TEMPORAL STATISTIC OF TRAFFIC ACCIDENTS IN TURKEY

Abstract. Traffic accidents form clusters in terms of geographic space and over time which themselves exhibit distinct spatial and temporal patterns. There is an imperative need to understand how, where and when traffic accidents occur in order to develop appropriate accident reduction strategies. An improved understanding of the location, time and reasons for traffic accidents makes a significant contribution to preventing them. Traffic accident occurrences have been extensively studied from different spatial and temporal points of view using a variety of methodological approaches. In literature, less research has been dedicated to the temporal patterns of traffic accidents. In this paper, the numbers of traffic accidents are normalized according to the traffic volume and the distribution and fluctuation of these accidents is examined in terms of Islamic time intervals. The daily activities and worship of Muslims are arranged according to these time intervals that are spaced fairly throughout the day according to the position of the sun. The Islamic time intervals are never been used before to identify the critical hour for traffic accidents in the world. The results show that the sunrise is the critical time that acts as a threshold in the rate of traffic accidents throughout Turkey in Islamic time intervals.

Spatio-temporal accident analysis for accident prevention in relation to behavioral factors in driving: The case of South Anatolian Motorway

Analyzing the pattern of traffic accidents on road segments can highlight the hazardous locations where the accidents occur frequently and help to determine problematic parts of the roads. The objective of this paper is to utilize accident hotspots to analyze the effect of different measures on the behavioral factors in driving. Every change in the road and its environment affects the choices of the driver and therefore the safety of the road itself. A spatio-temporal analysis of hotspots therefore can highlight the road segments where measures had positive or negative effects on the behavioral factors in driving. In this paper 2175 accidents resulted in injury or death on the South Anatolian Motorway in Turkey for the years between 2006 and 2009 are considered. The network-based kernel density estimation is used as the hotspot detection method and the K-function and the nearest neighbor distance methods are taken into account to check the significance of the hotspots. A chi-square test is performed to find out whether temporal changes on hotspots are significant or not. A comparison of characteristics related driver attributes like age, experience, etc. for accidents in hotspots vs. accidents outside of hotspots is performed to see if the temporal change of hotspots is caused by structural changes on the road. For a better understanding of the effects on the driver characteristics, the accidents are analyzed in five groups based on three different grouping schemes. In the first grouping approach, all accident data are considered. Then the accident data is grouped according to direction of the traffic flow. Lastly, the accident data is classified in terms of the vehicle type. The resultant spatial and temporal changes in the accident patterns are evaluated and changes on the road structure related to behavioral factors in driving are suggested.

Joshua D. Angrist and Jörn-Steffen Pischke: Mastering metrics

This is a microeconometrics textbook quite out of the ordinary. First, it tackles problems starting strictly from applications, using everyday language and putting themajor obstacle which has been facing modern microeconometrics in the forefront quite at the beginning: the fact that there are almost no controlled experiments, which in an ideal world would allow for simple causal inference, and what to do about it. Second, it mentions technical problems and formula only in passing, if at all. My favorite is the definition of mathematical expectation on page 14: “The mathematical expectation of a Variable, Yi, written E(Yi), is the population average of this variable.” While more mathematically inclined readers will wince at this, this approach will certainly help economists not familiar with mathematical statistics to still capture some of the basic techniques and ideas. The book is obviously written for an Anglo-Saxon audience and addresses problems which are mostly of interest to students from this cultural environment: whether it pays in later earnings to accept a costly offer from Harvard rather than attend a heavily subsidized minor State University, whether differences in legal drinking age across American States induce different patterns of traffic accidents, or whether more money for public health insurance produces better health. If one does not mind this rather parochial point of view, there is certainly no better introduction to modern microeconometrics than this book.

Urban development and traffic accidents in Brazil

Brazil started to experience high traffic accident rates since the 1960s, when road transportation began to be dominant and the number of motorized vehicles increased sharply. The severity of the problem was also related to the fast and uncontrolled urban growth, which allowed for the organization of an inherently dangerous circulation space, characterized by a complex pattern of traffic conflicts. With respect to traffic accidents, the results have been highly negative: national statistics report about 28 000 fatalities a year, with more than 340 000 injured people. The paper analyses current patterns of traffic accidents in urban areas, using the data from two of the three largest cities in the country. Hospital data is analyzed to understand the true social impacts of such accidents. Several causal factors are identified, at the political, cultural and technical sides. Challenges to change current conditions are also identified. Practical measures are suggested, especially the change of the dangerous environment and the need to improve the technical approach to the problem, education programs and enforcement logistics.

Patterns of Traffic Accidents and Violations Related to Drinking and to Some Medical Conditions

A comparison was made of the types of accidents and violations incurred by 256 drivers with alcoholism as a primary diagnosis, 126 drivers with another medical condition and a drinking problem as a...