Data mining approach to model bus crash severity in Australia

Samerei, Seyed Alireza; Aghabayk, Kayvan; Mohammadi, Amin; Shiwakoti, Nirajan

doi:10.1016/j.jsr.2020.12.004

Cited by 55 publications

(18 citation statements)

References 53 publications

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“…The suggested model also gives some tips on how to drive safely. A similar task was performed by S.A. Samerei [35] on bus accidents. They have collected bus accident data over the period of 2006-2019 in the State of Victoria, Australia.…”

Section: Integrated Accident Detection Systemmentioning

confidence: 99%

AI Enabled Accident Detection and Alert System Using IoT and Deep Learning for Smart Cities

et al. 2022

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As the number of vehicles increases, road accidents are on the rise every day. According to the World Health Organization (WHO) survey, 1.4 million people have died, and 50 million people have been injured worldwide every year. The key cause of death is the unavailability of medical care at the accident site or the high response time in the rescue operation. A cognitive agent-based collision detection smart accident alert and rescue system will help us to minimize delays in a rescue operation that could save many lives. With the growing popularity of smart cities, intelligent transportation systems (ITS) are drawing major interest in academia and business, and are considered as a means to improve road safety in smart cities. This article proposed an intelligent accident detection and rescue system which mimics the cognitive functions of the human mind using the Internet of Things (IoTs) and the Artificial Intelligence system (AI). An IoT kit is developed that detects the accident and collects all accident-related information, such as position, pressure, gravitational force, speed, etc., and sends it to the cloud. In the cloud, once the accident is detected, a deep learning (DL) model is used to validate the output of the IoT module and activate the rescue module. Once the accident is detected by the DL module, all the closest emergency services such as the hospital, police station, mechanics, etc., are notified. Ensemble transfer learning with dynamic weights is used to minimize the false detection rate. Due to the dataset’s unavailability, a personalized dataset is generated from the various videos available on the Internet. The proposed method is validated by a comparative analysis of ResNet and InceptionResnetV2. The experiment results show that InceptionResnetV2 provides a better performance compared to ResNet with training, validation, and a test accuracy of 98%, respectively. To measure the performance of the proposed approach in the real world, it is validated on the toy car.

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Section: Integrated Accident Detection Systemmentioning

confidence: 99%

AI Enabled Accident Detection and Alert System Using IoT and Deep Learning for Smart Cities

et al. 2022

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“…Several authors have discussed accident severity analysis during the last decade (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20). Statistical models are typically used to address accident severity prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient Histogram-Based Gradient Boosting Approach for Accident Severity Prediction With Multisource Data

Kashifi

Ahmad

2022

Transportation Research Record: Journal of the Transportation R

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Many people lose their lives in road accidents because they do not receive timely treatment after the accident from emergency medical services; providing timely emergency services can decrease the fatality rate as well as the severity of accidents. In this study, we predicted the severity of car accidents for use by trauma centers and hospitals for emergency response management. The predictions of our model could be used to decide whether an ambulance unit should be dispatched to the crash site or not. This study used histogram-based gradient boosting (HistGBDT), a modification of the gradient boosting (GBDT) classifier that accelerates the learning process and increases a model’s prediction power. The HistGBDT model was compared with seven state-of-the-art machine learning models: logistic regression, multilayer perceptron, random forest, extremely randomized trees, bagging, AdaBoost, and GBDT. The experiments were conducted on French accident data from 2005 to 2018. The HistGBDT model, with an overall accuracy of 82.5%, recall of 76.7%, and precision of 81.9%, outperformed other models. An analysis of feature importance indicated that safety equipment was the most important feature and vehicle category, department, localization, and region were other significant features. The Fβ measure (i.e., the weighted harmonic mean of recall and precision) was optimized with different weights on recall for the four best performing models to compare the tradeoff between the two crucial performance measures.

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“…To formulate appropriate policies towards lessening the considerable repercussions of crashes in emerging countries, earlier authors have researched factors associated with the severity of bus-related crashes (i.e., Bangladesh [15] and Ghana [17]). Notwithstanding, as argued by Sam et al [17], the extant literature about the factors in developing countries is much less than that in developed countries [18][19][20][21][22]. This would cause difficulties in identifying crucial factors determining the bus crash severity in developing nations-in other words, knowledge on the determinants of bus crash severity has remained inadequately handled in such countries.…”

Section: Introductionmentioning

confidence: 99%

Bus Crash Severity in Hanoi, Vietnam

Nguyen

Armoogum

et al. 2021

Safety

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Recently, there has been an increasing interest in targeting the safety of bus operations worldwide; however, little is known about the determinants of the bus crash severity in developing countries. By estimating an ordered logit model using the bus-involved collision data in Hanoi (Vietnam), spanning the period from 2015 to 2019, this study investigates various factors associated with the crash severity. The results reveal that the severity risk increases for (1) large buses, (2) raining conditions, (3) evening or night, (4) sparse traffic, (5) non-urban areas, (6) roads with at least three lanes, (7) curved roads, (8) two-way roads without a physical barrier, (9) head-on collision, and (10) pedestrian-related crashes. Aside from confirming the crucial roles of a wide range of factors, this research has examined the effects of two determinants (traffic density and crash area) that have not been considered for the cases of developing countries previously. Based on the findings on the impacts of factors, a series of policy recommendations regarding improving road conditions in non-urban areas, promoting walking infrastructure, reminders of high-risk situations for drivers, safety notes when improving bus service quality, and recording bus-related crashes are proposed.

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Data mining approach to model bus crash severity in Australia

Cited by 55 publications

References 53 publications

AI Enabled Accident Detection and Alert System Using IoT and Deep Learning for Smart Cities

AI Enabled Accident Detection and Alert System Using IoT and Deep Learning for Smart Cities

Efficient Histogram-Based Gradient Boosting Approach for Accident Severity Prediction With Multisource Data

Bus Crash Severity in Hanoi, Vietnam

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