Traffic Crash Evolution Characteristic Analysis and Spatiotemporal Hotspot Identification of Urban Road Intersections

Road traffic safety is a key concern of transport management as it has severely restricted Chinese economic and social development. With the objective to prevent and reduce road traffic crashes, this study proposes a comprehensive spatiotemporal analysis method that integrates the time-space cube analysis, spatial autocorrelation analysis, and emerging hot spot analysis for exploring the traffic crash evolution characteristics and identifying crash hot spots. These analyses are all conducted by the corresponding toolbox of ArcGIS 10.5. Then, a small sized-city of China (i.e., Wujiang) is selected as the case study, and the historical traffic crash data occurring at the road intersections of Wujiang for the year 2016 are analyzed by the proposed method. The analysis process identifies the high incidence locations of traffic crashes, then presents the spatial change trend and statistical significance of the crash locations. Finally, different types of crash hotspots, as well as their evolution patterns over time, are determined. The results illustrate that the traffic crash hotspots of road intersections are primarily distributed in the Northeast area of Wujiang’s major urban area, while the crash cold spots are concentrated in the Southwest of Wujiang, which points out the direction for crash prevention. In addition, the finding has a potential engineering application value, and it is of great significance to the sustainable development of Wujiang.

Download Full-text

Revealing the Spatial Pattern of Weather-Related Road Traffic Crashes in Slovenia

Applied Sciences ◽

10.3390/app11146506 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6506

Author(s):

Danijel Ivajnšič ◽

Nina Horvat ◽

Igor Žiberna ◽

Eva Konečnik Kotnik ◽

Danijel Davidović

Keyword(s):

Traffic Safety ◽

Spatial Information ◽

Road Traffic ◽

Road Traffic Crash ◽

Navigation Systems ◽

Traffic Crashes ◽

Road Traffic Crashes ◽

Traffic Crash ◽

Road Traffic Safety ◽

Adverse Weather

Despite an improvement in worldwide numbers, road traffic crashes still cause social, psychological, and financial damage and cost most countries 3% of their gross domestic product. However, none of the current commercial or open-source navigation systems contain spatial information about road traffic crash hot spots. By developing an algorithm that can adequately predict such spatial patterns, we can bridge these still existing gaps in road traffic safety. To that end, geographically weighted regression and regression tree models were fitted with five uncorrelated (environmental and socioeconomic) road traffic crash predictor variables. Significant regional differences in adverse weather conditions were identified; Slovenia lies at the conjunction of different climatic zones characterized by differences in weather phenomena, which further modify traffic safety. Thus, more attention to speed limits, safety distance, and other vehicles entering and leaving the system could be expected. In order to further improve road safety and better implement globally sustainable development goals, studies with applicative solutions are urgently needed. Modern vehicle-to-vehicle communication technologies could soon support drivers with real-time traffic data and thus potentially prevent road network crashes.

Download Full-text

First-aid Training and Bystander Actions at Traffic Crashes — A Population Study

Prehospital and Disaster Medicine ◽

10.1017/s1049023x00000352 ◽

2002 ◽

Vol 17 (3) ◽

pp. 134-141 ◽

Cited By ~ 27

Author(s):

Eva M. Larsson ◽

Niklas L. Mártensson ◽

Kristina A.E. Alexanderson

Keyword(s):

Road Traffic ◽

Public Health Problem ◽

First Aid ◽

Economic Losses ◽

Traffic Crashes ◽

Traffic Crash ◽

The Public ◽

Lay Persons ◽

First Aid Training ◽

The Impact

AbstractIntroduction:Traffic crashes constitute a major, worldwide public-health problem that cause disabilities, life-long suffering, and huge economic losses. When a person is injured in a traffic crash, actions taken by bystanders often are of crucial importance. To perform first-aid actions in a correct manner, bystanders, often lay persons, need both the courage and the knowledge to do so. For preventive purposes, society spends large resources to inform and educate the public in order to enhance people's ability to take correct actions. However, there only is little information on the rate in a population of persons who have had first-aid training, have been bystanders at a traffic crash, on the actions taken by such persons, and on effects of first-aid training on patient care.Objective:The aim of this study was to acquire knowledge about: (1) the prevalence of first-aid training; (2) the incidence of being a bystander and of the first aid provided at traffic crashes and other emergencies; and (3) the impact of first-aid training on the risks people take in road traffic.Methods:A questionnaire was administered to 2,800 randomly selected persons aged 18–74 years.Results:The response rate was 67.5%. During the previous five years, 39% of the population had received first-aid training, with a higher rate among younger individuals and those with a higher education. After training, 30% of the respondents had used their skills, and 41% took fewer risks in traffic, particularly those who were older or had a lower level of education. Fourteen percent of those with training (significantly more men) had been bystanders at a traffic crash. At 20% of the crashes, a bystander had administered first aid, and one-third of those who provided such assistance had had use of their training. Conclusion: Intensified first-aid training of the general public could lead to citizens who are more cautious in traffic and to bystanders who provide more immediate and adequate first aid at traffic crashes and other emergencies.

Download Full-text

Identification of potential traffic accident hot spots based on accident data and GIS

MATEC Web of Conferences ◽

10.1051/matecconf/202032501005 ◽

2020 ◽

Vol 325 ◽

pp. 01005

Author(s):

Hongge Zhu ◽

Yuntong Zhou ◽

Yanyan Chen

Keyword(s):

Traffic Safety ◽

Hot Spots ◽

Traffic Accident ◽

Traffic Accidents ◽

Road Traffic ◽

Hot Spot ◽

Geographic Information ◽

Causality Analysis ◽

Road Traffic Safety ◽

Accident Data

The problem of road traffic safety has been widely concerned in recent years. The identification of traffic accident hot spots can effectively improve the road traffic safety and let the traffic managers formulate targeted improvement measures and suggestions. The traditional identification method of accident hot spot does not consider the spatial attribute of the accident, so it has some limitations in the identification of traffic accident hot area. Therefore, this paper first proposes a method to identify the hot spot of traffic accidents based on geographic information system (GIS). The mathematical model and machine learning model are used to explore the correlation between traffic accidents and spatial characteristics from macro and micro aspects. Finally, taking Beijing as an example, the feasibility of the research method is proved by using the accident data of Beijing in 2015 and the geographic information of Beijing. The research results of this paper can realize the spatial effective transformation of accident records, comprehensively consider the micro and macro attributes of the accident itself, realize the automatic and efficient identification of the accident hot spot. In addition, the causality analysis results between each attribute and the distribution of accident hot spots can help decision makers to formulate safety and sustainable road strategies.

Download Full-text

Road traffic crashes and built environment analysis of crash hotspots based on local police data in Galle, Sri Lanka

International Journal of Injury Control and Safety Promotion ◽

10.1080/17457300.2018.1431932 ◽

2018 ◽

Vol 25 (3) ◽

pp. 311-318 ◽

Cited By ~ 4

Author(s):

Vijitha De Silva ◽

Hemajith Tharindra ◽

João Ricardo Nickenig Vissoci ◽

Luciano Andrade ◽

Badra Chandanie Mallawaarachchi ◽

...

Keyword(s):

Sri Lanka ◽

Built Environment ◽

Road Traffic ◽

Traffic Crashes ◽

Environment Analysis ◽

Road Traffic Crashes ◽

Local Police ◽

Police Data ◽

Crash Hotspots

Download Full-text

Analysis of Pedestrian-Vehicle Crashes Using Artificial Learning Methods: City of Sakarya Case Study

Academic Perspective Procedia ◽

10.33793/acperpro.04.02.54 ◽

2021 ◽

Vol 4 (2) ◽

pp. 221-230

Author(s):

Zeliha Cagla Kuyumcu ◽

Suhrab Ahadi ◽

Hakan Aslan

Keyword(s):

Low Income ◽

Traffic Accidents ◽

Road Traffic ◽

Low Income Countries ◽

Road Traffic Crash ◽

Traffic Crashes ◽

Road Traffic Crashes ◽

Traffic Crash ◽

Artificial Learning

The lives of approximately 1.3 million people are cut short every year as a result of road traffic crashes. Between 20 and 50 million people suffer non-fatal injuries, with many incurring a disability as a result of their injury. The risk of dying in a road traffic crash is more than 3 times higher in low-income countries than in high-income countries [1]. In Turkey, 18% of traffic accidents was related to pedestrian-vehicle collisions in urban roads in 2020. In addition, 20% of death toll caused by accidents is pedestrians in 2020 [2]. This study deals with the some of classifiers to forecast the number of injuries as a result of traffic accidents. The classifier’s performance ratios were also examined.

Download Full-text

Approaching autonomous driving with cautious optimism: analysis of road traffic injuries involving autonomous vehicles based on field test data

Injury Prevention ◽

10.1136/injuryprev-2019-043402 ◽

2020 ◽

pp. injuryprev-2019-043402

Author(s):

Wanbao Ye ◽

Chuanlin Wang ◽

Fuxiang Chen ◽

Shuzhen Yan ◽

Liping Li

Keyword(s):

Traffic Safety ◽

Autonomous Vehicles ◽

Road Traffic ◽

Statistical Significance ◽

Autonomous Driving ◽

Road Traffic Injuries ◽

Road Traffic Safety ◽

Road Users ◽

Vulnerable Road Users ◽

Poor Lighting

ObjectivesTo examine the patterns and associated factors of road traffic injuries (RTIs) involving autonomous vehicles (AVs) and to discuss the public health implications and challenges of autonomous driving.MethodsData were extracted from the reports of traffic crashes involving AVs. All the reports were submitted to the California Department of Motor Vehicles by manufacturers with permission to operate AV test on public roads. Descriptive analysis and χ2 analysis or Fisher’s exact test was conducted to describe the injury patterns and to examine the influencing factors of injury outcomes, respectively. Binary logistic regression using the Wald test was employed to calculate the OR, adjusted OR (AOR) and 95% CIs. A two-tailed probability (p<0.05) was adopted to indicate statistical significance.Results133 reports documented 24 individuals injured in 19 crashes involving AVs, with the overestimated incidence rate of 18.05 per 100 crashes. 70.83% of the injured were AV occupants, replacing vulnerable road users as the leading victims. Head and neck were the most commonly injured locations. Driving in poor lighting was at greater risk of RTIs (AOR 6.37, 95% CI 1.47 to 27.54). Collisions with vulnerable road users or incidents happening during commute periods led to a greater number of victims (p<0.05). Autonomous mode cannot perform better than conventional mode in road traffic safety to date (p=0.468).ConclusionsPoor lighting improvement and the regulation of commute-period traffic and vulnerable road users should be strengthened for AV-related road safety. So far AVs have not demonstrated the potential to dramatically reduce RTIs. Cautious optimism about AVs is more advisable, and multifaceted efforts, including legislation, smarter roads, and knowledge dissemination campaigns, are fairly required to accelerate the development and acceptance.

Download Full-text

Predictors of Self-reported Crashes among Iranian Drivers: Exploratory Analysis of an Extended Driver Behavior Questionnaire

PROMET - Traffic&Transportation ◽

10.7307/ptt.v30i1.2412 ◽

2018 ◽

Vol 30 (1) ◽

pp. 35-43 ◽

Cited By ~ 7

Author(s):

Amin Mohamadi Hezaveh ◽

Trond Nordfjærn ◽

Amir Reza Mamdoohi ◽

Özlem Şimşekoğlu

Keyword(s):

Traffic Safety ◽

Road Traffic ◽

Driver Behaviour ◽

Factor Solution ◽

Traffic Crashes ◽

Traffic Fatality ◽

Road Traffic Safety ◽

Traffic Violations ◽

Safety Countermeasures ◽

Iranian Context

More than 16,500 people lose their lives each year due to traffic crashes in Iran, which reflects one of the highest road traffic fatality rates in the world. The aim of the present study is to investigate the factors structure of an extended Driver Behaviour Questionnaire (DBQ) and to examine the gender differences in the extracted factors among Iranian drivers. Further, the study tested the association between DBQ factors, demographic characteristics, and self-reported crashes. Based on Iranian driving culture, an extended (36 items) Internet-based version of the DBQ was distributed among Iranian drivers. The results of Exploratory Factor Analysis based on a sample of 632 Iranians identified a five-factor solution named “Speeding and Pushing Violations”, “Lapses and Errors”, “Violations Causing Inattention”, “Aggressive Violations” and “Traffic Violations” which account for 44.7 percent of the total variance. The results also revealed that females were more prone to Lapses and Errors, whereas males reported more violations than females. Logistic regression analysis identified Violations Causing Inattention, Speeding and Pushing Violations as predictors of self-reported crashes in a three-year period. The results were discussed in line with road traffic safety countermeasures suitable for the Iranian context.

Download Full-text

Severity, Spatial Pattern and Statistical Analysis of Road Traffic Crash Hot Spots in Ethiopia

Applied Sciences ◽

10.3390/app11198828 ◽

2021 ◽

Vol 11 (19) ◽

pp. 8828

Author(s):

Alamirew Mulugeta Tola ◽

Tamene Adugna Demissie ◽

Fokke Saathoff ◽

Alemayehu Gebissa

Keyword(s):

Spatial Pattern ◽

Spatial Autocorrelation ◽

Hot Spots ◽

Road Traffic ◽

Hot Spot ◽

Economic Effect ◽

Road Traffic Crash ◽

Spatial Pattern Analysis ◽

Crash Severity ◽

Traffic Crash

The reduction of traffic crashes, as well as their socio-economic consequences, has captivated the attention of safety professionals and transportation agencies. The most important activity for an effective road safety practice is to identify hazardous roadway areas based on a spatial pattern analysis of crashes and an evaluation of crash spatial relations with neighboring areas and other relevant factors. For decades, safety researchers have adopted several techniques to analyze historical road traffic crash (RTC) information using the advanced GIS-based hot spot analysis. The objective of this study is to present a GIS technique for identifying crash hot spots based on spatial autocorrelation analysis using a four-year (2014–2017) crash data across Ethiopian regions, as well as zones and towns in the Oromia region. The study considered the corresponding severity values of RTCs for the analysis and ranking of crash hot spot areas. The spatial autocorrelation tool in ArcGIS 10.5 was used to analyze the spatial patterns of RTCs and then the Getis Ord Gi* statistics tool was used to identify high and low crash severity cluster zones. The results showed that the methods used in this analysis, which incorporated Moran’s I spatial autocorrelation of crash incidents, Getis Ord Gi* and crash severity index, proved to be a fruitful strategy for identifying and ranking crash hot spots. The identified crash hot spot areas are along the entrance to and exit from Addis Ababa, Ethiopia’s capital city, so the responsible bodies and traffic management agencies should give top priority attention and conduct a thorough study to reduce the socio-economic effect of RTCs.

Download Full-text