Deterrent effects of demerit points and license sanctions on drivers’ traffic law violations using a proportional hazard model

Lee, Jae-Yeong; Park, Byung-Jung; Lee, Chungwon

doi:10.1016/j.aap.2018.01.028

Cited by 20 publications

(11 citation statements)

References 19 publications

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“…In particular, the study observed that while elderly drivers have fewer accidents than younger drivers, their safety record is worse and in fact are more often found to be at fault in accidents. Along these lines, a recent study identified the "deterrent effects" of Demerit Points System (DPS) by using variables related to accumulated demerit points (Lee et al, 2018). The study by Lee et al (2018) quantified the connection between relative violation hazards and accumulated demerit points while controlling for drivers who had no license sanctions, who had license suspension experience, and those who had license revocation experience (Lee et al, 2018).…”

Section: Previous Traffic Convictionsmentioning

confidence: 99%

A heterogeneity based case-control analysis of motorcyclist’s injury crashes: Evidence from motorcycle crash causation study

Wali

Khattak

2018

Accident Analysis & Prevention

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The main objective of this study is to quantify how different "policy-sensitive" factors are associated with risk of motorcycle injury crashes, while controlling for rider-specific, psycho-physiological, and other observed/unobserved factors. The analysis utilizes data from a matched case-control design collected through the FHWA's Motorcycle Crash Causation Study. In particular, 351 cases (motorcyclists involved in injury crashes) are analyzed vis-à-vis similarly-at-risk 702 matched controls (motorcyclists not involved in crashes). Unlike traditional conditional estimation of relative risks, the paper presents heterogeneity based statistical analysis that accounts for the possibility of both within and between matched case-control variations. Overall, the correlations between key risk factors and injury crash propensity exhibit significant observed and unobserved heterogeneity. The results of best-fit random parameters logit model with heterogeneity-in-means show that riders with partial helmet coverage (U.S. DOT compliant helmets with partial coverage, least intrusive covering only the top half of the cranium) have a significantly lower risk of injury crash involvement. Lack of motorcycle rider conspicuity captured by dark (red) upper body clothing is associated with significantly higher injury crash risk (odds ratio 3.87, 95% CI: 1.63, 9.61). Importantly, a rider's motorcycle-oriented lower clothing (e.g., cannot easily get stuck in the machinery) significantly lowers the odds of injury crash involvement. Regarding the effectiveness of training, formal motorcycle driving training in recent years was associated with lower injury crash propensity. Finally, riders with less sleep prior to crash/interview exhibited 1.97 times higher odds of crash involvement compared to riders who had more than 5 h of sleep. Methodologically, the conclusion is that the correlations of several rider, exposure, apparel, and riding history related factors with crash risk are not homogeneous and in fact vary in magnitude as well as direction. The study results indicate the need to develop appropriate countermeasures, such as refresher motorcycle training courses, prevention of sleep-deprived/fatigued riding, and riding under the influence of alcohol and drugs.

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Section: Previous Traffic Convictionsmentioning

confidence: 99%

A heterogeneity based case-control analysis of motorcyclist’s injury crashes: Evidence from motorcycle crash causation study

Wali

Khattak

2018

Accident Analysis & Prevention

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“…What has been done by the Magelang City Police Station is in line with the theory of integrative law where law enforcement is not done through enforcement but with non-penalism through the inculcation of values that develop in society to make and enforce the law (Lee, Park, & Lee, 2018). In the implementation of law enforcement in the field of traffic before carrying out an action must first instill the values that develop in the community so that later the community can realize the importance of the rule of law, especially the rule of traffic law so that later the goal of traffic activity and road transportation will be in accordance with listed in Law No.22 of 2009 concerning Road Traffic and Transportation.…”

Section: ) Preventive Stepmentioning

confidence: 85%

Law Enforcement of Motorized Vehicles with Muffler Racing by Magelang City Police

Kurniadi

2019

JLLR

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Muffler is a facility to dispose of exhaust gases that result from combustion in a motorcycle engine when the engine is started. Motorcycle riders often replace the standard muffler with a harder or racing muffler. Basically, muffler modification is considered a violation of the law. Based on the description above, the formulation of the problem that will be discussed in this research is how to implement regulations regarding the use of motor racing exhausts on public roads?, And how the actions taken by the Magelang city police resort in dealing with the use of mufflers racing? The results of research and discussion indicate that law enforcement in the case of the use of motorcycle racing mufflers is included in law enforcement in the field of traffic. Law enforcement in the field of traffic can be grouped into preventive and repressive law enforcement. The conclusion drawn from this study is that the implementation of regulations regarding the use of motorbikes with racing mufflers on public roads has been carried out in accordance with the law. The action taken by Police is to carry out traffic and seizure actions taking into account the effects of pollution on the use of mufflers racing which not only damage human health but also damage the environment.

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“…Environmental factors include traffic condition [42], road geometry features [26], road type/grades [19], visibility [19], [43], weather condition [43], [44], presence of passenger [25]. Furthermore, in addition to the individual attributes and environment factors, the effects of traffic law and enforcement system cannot be ignored in the analysis of drivers' violation behavior, such as exemption from paying toll [26], Penalty point or license sanctions system [45], [46], and traffic enforcement program [47]. Although driver's violation behavior is a key risk factor affecting traffic crash, it has received little attention concerning analysis and prediction of the truck driver's violation frequency.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Although driver's violation behavior is a key risk factor affecting traffic crash, it has received little attention concerning analysis and prediction of the truck driver's violation frequency. On the one hand, some existing researches focused on the likelihood or time distribution of car drivers' violation behavior using various approaches such as Logistic regression model [24], proportional hazard model [45]. In order to quantify the effects of variables, such as age, education, income, driving exposure, on the likelihood of drivers' speeding violation, Tseng [24] employed logistic model and explored driver's compliance/or non-compliance in the context of speed limit.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In order to quantify the effects of variables, such as age, education, income, driving exposure, on the likelihood of drivers' speeding violation, Tseng [24] employed logistic model and explored driver's compliance/or non-compliance in the context of speed limit. Lee et al [45] used Cox's proportional hazard model to estimate different driver's duration of compliance from acquisition of a driving license to conviction or from conviction to reconviction. On the other hand, there are limited number of researches focused on evaluating the effects of risk factors on car drivers' violation behavior frequency.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

A Zero-Inflated Ordered Probit Model to Analyze Hazmat Truck Drivers’ Violation Behavior and Associated Risk Factors

Fan

Wang³

2020

IEEE Access

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There are few studies on the violation of truck drivers, especially the hazmat truck driver, although truck driver's violation may cause serious casualties. This paper aims to investigate hazmat truck drivers' violation behavior and identify associated risk factors. Different data sources in intelligent transportation system (ITS) including hazmat transportation management system and traffic safety management system are extracted and emerged together. Three years (2016-2018) of violation data that comprised 11612 trip record in China are employed in this research. Based on Bayesian theory, this study proposes zero-inflated ordered probit (ZIOP) model and three alternative models to exploring the relationship between hazmat truck drivers' violation frequency and the key risk factors. The results show that ZIOP model can handle excessive zero observation problem of violation data properly and differentiate between 'alwayszero group' drivers and drivers who did not violate the traffic rules during research period but would do so in different surroundings. The results also indicate that the violation probability and the violation frequency level of hazmat truck drivers are influenced by driver characteristics, freight order attributes, and drivers' violation records. This research provides guidance for driving training and safety education of hazmat truck drivers, and will be helpful in building better driving simulation models.

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Deterrent effects of demerit points and license sanctions on drivers’ traffic law violations using a proportional hazard model

Cited by 20 publications

References 19 publications

A heterogeneity based case-control analysis of motorcyclist’s injury crashes: Evidence from motorcycle crash causation study

A heterogeneity based case-control analysis of motorcyclist’s injury crashes: Evidence from motorcycle crash causation study

Law Enforcement of Motorized Vehicles with Muffler Racing by Magelang City Police

A Zero-Inflated Ordered Probit Model to Analyze Hazmat Truck Drivers’ Violation Behavior and Associated Risk Factors

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