Applying interpretable machine learning to classify tree and utility pole related crash injury types

Das, Subasish; Datta, S. P.; Zubaidi, Hamsa; Obaid, Ihsan

doi:10.1016/j.iatssr.2021.01.001

Cited by 22 publications

(14 citation statements)

References 26 publications

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“…These factors are possible with small variation across the data to have great prediction power and dominant associations with the critical near-crash events. In recent years, several studies have applied interpretable machine learning in safety analysis (42)(43)(44)(45). There is a need for additional research focus on the application of machine learning related explainability for wide adoption of machine learning based models.…”

Section: Discussionmentioning

confidence: 99%

In-Depth Understanding of Pedestrian–Vehicle Near-Crash Events at Signalized Intersections: An Interpretable Machine Learning Approach

Kong

Das

Zhang

et al. 2022

Transportation Research Record: Journal of the Transportation R

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This study used a pedestrian-involved near-crash database and adopted an interpretable machine learning framework using SHapley Additive exPlanations (SHAP) to understand the factors associated with critical pedestrian-involved near-crash events. The results indicate that pedestrians with a relatively higher walking speed are more likely to be involved in critical near-crash events. Furthermore, critical pedestrian-involved near-crash events are highly associated with vehicles with driving speeds of less than 10 mph. A higher pedestrian volume is highly associated with critical near-crash events with left-turn vehicles. It is possible that a higher pedestrian volume increases the occurrence of jaywalking behavior or encourages more pedestrians to step into the crosswalk when they should not. By contrast, a higher pedestrian volume is highly associated with non-critical near-crash events with right-turn vehicles. Right-turn vehicles often expect that there will be pedestrians crossing, and a higher volume of pedestrian traffic increases a driver’s awareness and caution while turning. The study also found that a longer signal cycle is highly associated with critical near-crash events when the pedestrian volume is low, while a relatively short signal cycle length is highly associated with critical near-crash events when the pedestrian volume is high. During non-peak hours, pedestrians have less tolerance for a relatively longer signal cycle. Moreover, a relatively shorter signal cycle length at peak hours will limit the number of pedestrians that can cross during a cycle and encourage the possibility of pedestrians jaywalking or stepping onto the crosswalk when they should not.

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Section: Discussionmentioning

confidence: 99%

In-Depth Understanding of Pedestrian–Vehicle Near-Crash Events at Signalized Intersections: An Interpretable Machine Learning Approach

Kong

Das

Zhang

et al. 2022

Transportation Research Record: Journal of the Transportation R

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“…12% to 15% of all road departure accidents and almost 22% of all fatal accidents occurred in Louisiana between 2010 and 2016, according to a study conducted in the United States of America that looked for traffic accidents that resulted in fatalities and significant losses. (Das et al, 2021) . At the unnamed crossings it supervises, research was done to look for collisions.…”

Section: Literature Reviewmentioning

confidence: 99%

A scientometric analysis and bibliometric review of driver injury severity crashes studies

Mahdi

Zubaidi

Das

2023

QJES

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One of the worst epidemics that endangers our life and has an impact on the future is the epidemic of road accidents. The goal of this study is to define traffic accidents, comprehend their causes, pinpoint the key elements that affect the seriousness of driver injuries, and ultimately identify the factors that contribute to accidents, such as excessive speed, alcohol, and drug use, the driver, the road, the vehicle, and environmental factors. Understanding prior research and studies is crucial. In earlier experiments, data visualization was accomplished using the VOS viewer. The Dimension, Web of Science, and Scopus websites were used to download the information for the keywords in Excel format. By country, it lists these terms, authors, and researchers. The researcher was able to conduct a study that was identical to his or her study with the aid of the keywords " driver injury severity," " driver crash," and " crash analysis.".

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“…It also lacks pavement maintenance, and therefore the pavement deterioration, which causes great risks to traffic safety on the roads, especially in some areas, including the al-Askari district, Al-Wahda district, Al-Jamhouri district, Al-Urouba district, Al-Jazaer district. These areas are located in the center of Al-Diwaniyah City and are used in the process of transporting goods and people, and traffic crashes are frequent in them [11]. Therefore, it is important to evaluate the condition of these roads to identify the appropriate maintenance methods and treat pavement defects to maintain a high level of service across the whole network.…”

Section: Location Of the Studymentioning

confidence: 99%

Evaluation of the Pavement Distress and its Impact on the Sustainability of the Traffic Operation for Selected Roads in Al-Diwaniyah City

Al-Zubaidi,

Zubaidi,

Al-Humeidawi

2023

IOP Conf. Ser.: Earth Environ. Sci.

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Roads and transportation infrastructure are critical assets for supporting political stability as well as economic and sustainable expansion in developing countries. Yet, pavement maintenance backlogs and the high capital expenditures of road rehabilitation need the adoption of pavement evaluation methods to ensure the best value for the investment. road maintenance is considered one of the main factors for preserving the road structurally and functionally, and before carrying out maintenance, pavement defects must be identified by evaluating the condition of the pavement to perform the appropriate maintenance. In this study, the Pavement Condition Index (PCI) method was relied upon to evaluate roads in Al-Diwaniyah City. Where several data were collected for the study, including the number of crashes for each region, the number of lanes, street width, the presence of a median, and the pavement condition evaluated for the regions with the highest crash rate, which are Al-Askari District with 707 crashes, Al-Urouba District with 379 crashes, Al-Wahda District with 271 crashes, Al-Jamhouri District with 235 crashes, and Al-Jazaer District with 212 crashes. This study is considered the first study to evaluate roads in Al-Diwaniyah city. The results of the study showed that the condition of the roads ranged from poor to satisfactory and there is no road reach to serious and failed. The results showed that the worst road is Al-Askari District, which lacks a lot of maintenance, at a rate of PCI 52.1. It is followed by Al-Wahda District with a PCI rate of 57.6, Al-Jamhouri District with a PCI rate of 67.2, Al-Urouba District with a PCI rate of 70.7, and Al-Jazaer District with a PCI rate of 73.7. The deterioration of the pavement surface condition affects the rash rate, and it is more dangerous when it is correlated to other road factors. Additionally, it was noted that the most prominent defects in the pavement surface of the five evaluated roads are: potholes, rutting, longitudinal and transverse cracks, corrugation, patching, raveling, depressions, and alligator cracks. The study recommended conducting more research on evaluating more roads in Al-Diwaniyah City on an annual basis and conducting periodic maintenance in order to improve the status of this city, which suffers from neglect in all respects, measures must be taken to enhance the environment and economy, sustain the streets, reduce maintenance costs, ensure traffic safety, and protect the lives of citizens.

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Applying interpretable machine learning to classify tree and utility pole related crash injury types

Cited by 22 publications

References 26 publications

In-Depth Understanding of Pedestrian–Vehicle Near-Crash Events at Signalized Intersections: An Interpretable Machine Learning Approach

In-Depth Understanding of Pedestrian–Vehicle Near-Crash Events at Signalized Intersections: An Interpretable Machine Learning Approach

A scientometric analysis and bibliometric review of driver injury severity crashes studies

Evaluation of the Pavement Distress and its Impact on the Sustainability of the Traffic Operation for Selected Roads in Al-Diwaniyah City

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