Identification of Accident Hotspots Using Clustering Algorithms in Machine Learning

Ravikanth, K.; Shekar, K. Chandra; Kethana, K. Shashi; Praveena, None V. Sai; Rachel, CH. Sharon

doi:10.1007/978-981-16-8987-1_65

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…Traffic accidents have always been one of the most essential public security issues. Statistics from the World Health Organization (WHO) show that more than 1.25 million people die from traffic accidents annually [1]. Currently, traffic accidents have become the main cause of casualties.…”

Section: Introductionmentioning

confidence: 99%

Improving Traffic Safety through Traffic Accident Risk Assessment

Zhou

Zhang³

2023

Sustainability

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The continuous development of sensors and the Internet of Things has produced a large amount of traffic data with location information. The improvement of traffic safety benefits from the availability of traffic accident data. Managers can patrol and control relevant areas in advance with limited police resources, according to the short-term traffic accident predictions. As a result, the possibility of accidents can be reduced, and the level of traffic safety can be improved. The traditional approach to accident prediction relies too much on the subjective experience of traffic managers. Inspired by the deep learning technology in the field of computer vision, this study first divides the road network into regular grids and takes the number of traffic accidents in each grid as the pixel value of an image. Then, a traffic accident prediction approach based on a bi-directional ConvLSTM U-Net with densely connected convolutions (BCDU-Net) is proposed. This method mines the regular information hidden in the accident data and introduces densely connected convolutions to further extract the deep spatial-temporal features contained in the traffic accident sequence. Thus, the issues of gradient disappearance and model over-fitting caused by the traditional model in model training can be avoided. Finally, the simulation experiment is carried out on the historical traffic accident data of Yinzhou District, Ningbo City. Results show that BCDU-Net has better accuracy and precision than other models in three data sets: motor vehicle accidents, non-motor vehicle accidents, and single-vehicle accidents. Therefore, the BCDU-Net is more suitable for traffic accident prediction and has good application prospects for improving road safety.

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Section: Introductionmentioning

confidence: 99%

Improving Traffic Safety through Traffic Accident Risk Assessment

Zhou

Zhang³

2023

Sustainability

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“…Traffic accidents are among the most critical global challenges due to profound human, economic, and social consequences [1,2]. According to the World Health Organization report, the death rate from traffic accidents is unacceptably high and reaches about 1.35 million people annually [3,4].…”

Section: Introductionmentioning

confidence: 99%

Helmet Use and Jaw and Tooth Injuries in Motorcyclists Admitted to a Referral Hospital

Mousavi-Shalmaei,

Hosseinnia,

Mohtasham-Amiri

et al. 2023

J. Maxillofac. Oral Surg.

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Background and Aim The use of helmets has been reported to reduce the incidence and severity of head injuries in motorcyclists. However, there remains a significant gap in knowledge regarding the effectiveness of helmets, especially in preventing jaw and tooth injuries. Patients and Methods In this retrospective analytical study record, all traffic accidents that occurred continuously from 2017 to 2019 in which the motorcyclists were on at least one side of the collision based on inclusion criteria. Data from injury variables in medical records were merged with data related to helmet use in the trauma registration system in Excel software. Then, the final analysis was performed with STATA software version 14 at a significance level of < 0.05. Results In total, 1807 people participated in the study, and 160 (9.37) people used helmets. The incidence rate of jaw and tooth injuries was 86 (5.04%). Logistic regression implied that the odds ratio for helmet use was 0.7, which considering the p value of 0.419, shows no significant relationship between wearing helmets and jaw and tooth injuries. Conclusion Helmets alone cannot prevent jaw and tooth injuries, and designers should design helmets that protect the head and the jaw and tooth. In prevention programs and campaigns for motorcyclists, every age, gender, job, grade group and marital status are target groups.

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“…Road tra c crashes are one of the most serious health problems, accounting for the ninth leading cause of death worldwide [1]. Nearly 1.35 million people are killed and 20 to 50 million people are injured in road tra c accidents each year around the world [2]. Over the past decade, the travel demand has increased dramatically by the population growth and urban infrastructure developments [3].…”

Section: Introductionmentioning

confidence: 99%

Spatial‐Temporal Analysis of Crash Severity: Multisource Data Fusion Approach

Taheri

Rasaizadi

Seyedabrishami

2022

Discrete Dynamics in Nature and Society

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High severity crashes are one of the negative consequences of suburban transportation for a range of factors. Fatalities, injuries, and medical costs, as well as road and car damage and mental side effects, are more important consequences of severe crashes. The goal of this research is to figure out what factors contribute to different crash severity levels, in order to reduce the likelihood of such crashes. This study is unique in that it tries to investigate the capabilities of various discrete choice methods in order to explore which one performs best given the current database and research restrictions. Furthermore, the data fusion approach allows this study to take advantage of a wide range of characteristics that influence crash severity. To achieve this objective, the current study used several types of discrete choice models, such as ordered logit (OL), multinominal logit (MNL), and mixed logit (ML) models, to examine the factors influencing the severity of crashes in the suburban highway area. The data are related to crashes and traffic counters in Khorasan Razavi province in the northeast of Iran. Spatial-temporal analysis of crash data with a data fusion approach has been conducted to prepare a multisource data set with a wide spectrum of independent variables to acquire reliable results using logit models. Independent descriptive variables include geometric design, time-related, weather and environmental conditions, land use, traffic attributes, vehicle characteristics, and driver characteristics. ML provided the best fit with the available data set when compared to other discrete choice techniques. In addition, in all three logit models, coefficients of geometric design, vehicle characteristics, driver characteristics, land use, and weather and environmental conditions are significant, demonstrating the significance of using multisource data in defining factors impacting crash severity.

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Identification of Accident Hotspots Using Clustering Algorithms in Machine Learning

Cited by 4 publications

References 8 publications

Improving Traffic Safety through Traffic Accident Risk Assessment

Improving Traffic Safety through Traffic Accident Risk Assessment

Helmet Use and Jaw and Tooth Injuries in Motorcyclists Admitted to a Referral Hospital

Spatial‐Temporal Analysis of Crash Severity: Multisource Data Fusion Approach

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