Factors influencing the patterns of wrong-way driving crashes on freeway exit ramps and median crossovers: Exploration using ‘Eclat’ association rules to promote safety

Das, Subasish; Dutta, Anandi; Jalayer, Mohammad; Bibeka, Apoorba; Wu, Lingtao

doi:10.1016/j.ijtst.2018.02.001

Cited by 59 publications

(24 citation statements)

References 23 publications

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“…In order to decrease the number of traffic crashes, fundamental reasons, which are the basis for promoting measures, need to be systematically analyzed. A large number of researchers have made efforts to identify the vital factors that influence the severity and frequency of traffic crashes during recent years, in order to formulate effective safety countermeasures to enhance traffic sustainability [47].…”

Section: Discussionmentioning

confidence: 99%

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Identifying Factors that Influence the Patterns of Road Crashes Using Association Rules: A case Study from Wisconsin, United States

Jia

Sun

2019

Sustainability

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Road traffic injury is currently the leading cause of death among children and young adults aged 5–29 years all over the world. Measures must be taken to avoid accidents and promote the sustainability of road safety. The current study aimed to identify risk factors that are significantly associated with the severity in crash accidents; therefore, traffic crashes could be reduced, and the sustainable safety level of roadways could be improved. The Apriori algorithm is carried out to mine the significant association rules between the severity of the crash accidents and the factors influencing the occurrence of crash accidents. Compared to previous studies, the current study included the variables more comprehensively, including environment, management, and the state of drivers and vehicles. The data for the current study comes from the Wisconsin Transportation crash database that contains information on all reported crashes in Wisconsin in the year 2016. The results indicate that male drivers aged 16–29 are more inclined to be involved in crashes on roadways with no physical separation. Additionally, fatal crashes are more likely to occur in towns while property damage crashes are more likely to occur in the city. The findings can help government to make efficient policies on road safety improvement.

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Section: Discussionmentioning

confidence: 99%

“…Male drivers are more prone to fail to keep the vehicle under control. Das et al also found a higher number of males are associated with crashes [47].…”

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confidence: 92%

Identifying Factors that Influence the Patterns of Road Crashes Using Association Rules: A case Study from Wisconsin, United States

Jia

Sun

2019

Sustainability

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“…In contradiction, algorithmic modeling focuses on the process of understanding the unknown by minimizing the error rate through a black box algorithmic model. Although deep learning has not been widely used in past studies, many studies have used machine learning and data mining algorithms in transportation safety analysis (28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40). Deep learning is a branch of artificial intelligence that attempts to model complex information through a series of processing layers.…”

Section: Overview Of Deep Learning Methods Analysismentioning

confidence: 99%

Using Deep Learning in Severity Analysis of At-Fault Motorcycle Rider Crashes

Das

Dutta

Dixon

et al. 2018

Transportation Research Record

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Motorcyclists are vulnerable highway users. Unlike passenger vehicle occupants, motorcycle riders do not have either protective structural surrounding or the advanced restraints that are mandatory safety features in cars and light trucks. Per vehicle mile traveled, motorcyclist fatalities occurred 27 times more frequently than passenger car occupant fatalities in traffic crashes. In addition, there were 4,976 motorcycle crash-related fatalities in the U.S. in 2014—more than twice the number of motorcycle rider fatalities that occurred in 1997. It shows that, in addition to current efforts, research needs to be conducted with additional resources and in newer directions. This paper investigated five years (2010–2014) of Louisiana at-fault motorcycle rider-involved crashes by using deep learning, which is a competent tool for mapping a high-multidimensional input into a smaller multidimensional output. The current study contributes to the existing injury severity modeling literature by developing a deep learning framework, named as DeepScooter, to predict motorcycle-involved crash severities. The final deep learning model can predict severity types with 100% accuracy with training data, and with 94% accuracy with test data, which is not attainable by using a statistical method or machine learning algorithm. The intensity of severities was found to be more likely associated with rider ejection, two-way roadways with no physical separation, curved aligned roadways, and weekends. It is anticipated that the DeepScooter framework and the findings will provide significant contributions to the area of motorcycle safety.

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“…Researchers have developed models to estimate the total number of crashes, the number of injury crashes, and the number of property damages and proved that the models are statistically meaningful and closer to real-world data [22]. In addition, association rules have been used for the identification of accident situations that frequently occur together [9,14,15]. Gomes proposed a model for accident frequency estimation which takes the influence of the road characteristics into account.…”

Section: Related Workmentioning

confidence: 99%

Attentional Road Safety Networks

Gupta

Srivatsav

Subramanyam

et al. 2019

2019 IEEE International Conference on Image Processing (ICIP)

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Road safety mapping using satellite images is a cost-effective but a challenging problem for smart city planning. The scarcity of labeled data, misalignment and ambiguity makes it hard for supervised deep networks to learn efficient embeddings in order to classify between safe and dangerous road segments. In this paper, we address the challenges using a region guided attention network. In our model, we extract global features from a base network and augment it with local features obtained using the region guided attention network. In addition, we perform domain adaptation for unlabeled target data. In order to bridge the gap between safe samples and dangerous samples from source and target respectively, we propose a loss function based on within and between class covariance matrices. We conduct experiments on a public dataset of London to show that the algorithm achieves significant results with the classification accuracy of 86.21%. We obtain an increase of 4% accuracy for NYC using domain adaptation network. Besides, we perform a user study and demonstrate that our proposed algorithm achieves 23.12% better accuracy compared to subjective analysis.

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Factors influencing the patterns of wrong-way driving crashes on freeway exit ramps and median crossovers: Exploration using ‘Eclat’ association rules to promote safety

Cited by 59 publications

References 23 publications

Identifying Factors that Influence the Patterns of Road Crashes Using Association Rules: A case Study from Wisconsin, United States

Identifying Factors that Influence the Patterns of Road Crashes Using Association Rules: A case Study from Wisconsin, United States

Using Deep Learning in Severity Analysis of At-Fault Motorcycle Rider Crashes

Attentional Road Safety Networks

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