Investigating characteristics of cellphone distraction with significance tests and association rule mining

Rahman, M. Ashifur; Sun, Xiaoduan; Sun, Ming; Shan, Donghui

doi:10.1016/j.iatssr.2020.09.001

Cited by 18 publications

(8 citation statements)

References 31 publications

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“…Association rules were generated by running an apriori algorithm using the “arules” package ( 61 ) in R statistical software ( 62 ). The selections of minimum support and minimum confidence are optimized using an iterative approach ( 23 , 52 ), as σ = 0 . 05 and δ = 0 . 2 , respectively. Some studies used rules with lift values of L > 1; however, by setting up a lift condition of a higher value of L > 1 . 2 , a total of 1,014 strongly associated rules were identified after pruning.…”

Section: Resultsmentioning

confidence: 99%

“…Association rules were generated by running an apriori algorithm using the ''arules'' package (61) in R statistical software (62). The selections of minimum support and minimum confidence are optimized using an iterative approach (23,52), as s = 0:05 and d = 0:2, respectively. Some studies used rules with lift values of L .…”

Section: Resultsmentioning

confidence: 99%

“…Cellphone-affected distracted driving crashes in the U.S. are still high ( 13 ); however, the substantial underreporting of these crashes has been implicated in previous studies ( 20 , 23–25 ) as distracted drivers are often seriously injured or deceased, or are suspected to not be forthcoming about reporting cellphone use ( 26 ). Insufficient reporting of cellphone distraction fails to show its significance in run-off road (ROR) crashes ( 27 ) and could lead to biased results and misinterpretations ( 28 , 29 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Association rule mining (ARM), a rule-based data mining method, has become popular in transportation safety research for exploring the collective association of multiple crash characteristics ( 23 , 50–56 ). ARM has also been identified as a potential decision-making tool for road safety agencies ( 50 , 57 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The combination of nighttime driving and cellphone use appears to be strongly influential for SVROR crashes. Poor visibility because of unlighted (that is, absence of streetlights) dark conditions poses a serious risk, especially away from intersections (rules 10,12,16,19,25,27) on two-way roads with no physical separation (rules 10,12,19,23,24,30). Even with the presence of streetlights at intersections (rules 3,5,6,8,11,20), crashes occurred within 500 ft of intersections.…”

Section: Patterns Of Associative Crash Characteristicsmentioning

confidence: 99%

See 4 more Smart Citations

Single-Vehicle Run-Off Road Crashes Because of Cellphone Distraction: Finding Patterns with Rule Mining

Rahman

Das

2022

Transportation Research Record: Journal of the Transportation R

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A wide array of literature strongly indicates a higher likelihood of roadway departure because of cellphone use; however, patterns of associative attributes in single-vehicle run-off-road (SVROR) crashes because of cellphone distraction remained unexplored. Using the association rule mining (ARM) method, this study aimed to identify the variable categories that concurrently occur in such crashes, visualize the structures representing those concurrent associations, and discuss the crash patterns associated with different severity types. The SVROR crashes with cellphone use by the driver at fault were highlighted to be strongly connected to non-usage of safety restraints, weekends, both lighted and unlighted dark conditions, two-lane highways without physical separation, roadway curves, and so forth. Alongside several attributes, non-usage of restraints was strongly associated with fatal and both incapacitating and non-incapacitating injury crashes. The findings of this study can benefit the determination of suitable countermeasures to prevent cellphone-related SVROR crashes.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Patterns Of Associative Crash Characteristicsmentioning

confidence: 99%

See 3 more Smart Citations

Single-Vehicle Run-Off Road Crashes Because of Cellphone Distraction: Finding Patterns with Rule Mining

Rahman

Das

2022

Transportation Research Record: Journal of the Transportation R

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Identification of aggressive driving behavior of online car‐hailing drivers based on association classification

Wu,

Chen,

et al. 2023

Hum Ftrs & Erg Mfg Svc

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With the rapid development of online car‐hailing, the related crashes have become a key issue with public concern. Identifying and predicting aggressive driving behaviors is critical to reduce traffic crashes. In this study, we propose a method to recognize aggressive driving behavior based on association classification, with multisource features being employed, including driver emotion, vehicle kinematic characteristics, and road environment. The model performs best in a 10‐fold cross‐test when the minimum support and minimum confidence are set as 0.01 and 0.8, respectively. Besides, we also compare the performance of aggressive driving behavior recognition classifiers constructed using association classification with other rule‐based classification methods, including ID3, C4.5, CART, and Random Forest. The results show that association classification performs better than other classification competitors. Thirty‐six if–then rules generated by the association classification are used to analyze the influencing factors and associated mechanisms of aggressive driving behavior. It is found that aggressive driving behavior is highly correlated with driver anger and disgust emotions. Aggressive driving behavior is more likely to occur when no passengers are in the car than the case with passengers. Driver entertainment behavior and passenger interference also affect driving behavior. Moreover, drivers are prone to aggressive driving when making a U‐turn. This research not only proposed a new identification method for aggressive driving behavior but also provided a comprehensive understanding of the associated influencing factors which thus benefit the further research and development of safety assistance driving devices.

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Data mining approach to explore emergency vehicle crash patterns: A comparative study of crash severity in emergency and non-emergency response modes

Hossain

Zhou

Das

2023

Accident Analysis & Prevention

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Investigating characteristics of cellphone distraction with significance tests and association rule mining

Cited by 18 publications

References 31 publications

Single-Vehicle Run-Off Road Crashes Because of Cellphone Distraction: Finding Patterns with Rule Mining

Single-Vehicle Run-Off Road Crashes Because of Cellphone Distraction: Finding Patterns with Rule Mining

Identification of aggressive driving behavior of online car‐hailing drivers based on association classification

Data mining approach to explore emergency vehicle crash patterns: A comparative study of crash severity in emergency and non-emergency response modes

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