Driving behaviors and accident risk under lifetime license revocation

This study explored the driving behaviors and crash risk of 768 drivers who were under administrative lifetime driver's license revocation (ALLR). It was found that most of the ALLR offenders (83.2%) were still driving and only a few (16.8%) of them gave up driving completely. Of the offenders still driving, 67.6% experienced encountering a police roadside check, but were not detained or ticketed by the police. Within this group, 50.6% continued driving while encountering a police check, 18.0% of them made an immediate U-turn and 9.5% of them parked and exited their car. As to crash risk, 15.2% of the ALLR offenders had at least one crash experience after the ALLR had been imposed. The results of the logistic regression models showed that the offenders’ crash risk while under the ALLR was significantly correlated with their personal characteristics (personal income), penalty status (incarceration, civil compensation and the time elapsed since license revocation), annual distance driven, and needs for driving (working, commuting and driving kids). Low-income offenders were more inclined to have a crash while driving under the ALLR. Offenders penalized by being incarcerated or by paying a high civil compensation drove more carefully and were less of a crash risk under the ALLR. The results also showed there were no differences in crash risk under the ALLR between hit-and-run offences and drunk driving offences or for offenders with a professional license or an ordinary license. Generally, ALLR offenders drove somewhat more carefully and were less of a crash risk (4.3 crashes per million km driven) than legal licensed drivers (23.1 crashes per million km driven). Moreover, they seemed to drive more carefully than drivers who were under short-term license suspension/revocation which previous studies have found.     

Is rigorous punishment effective? A case study of lifetime license revocation in Taiwan

This study investigated the effectiveness of administrative lifetime driver's license revocation (ALLR) and its impact on offenders, based on a two-stage survey of 768 offenders. It was found that after ALLR had been imposed, 23.4% of these offenders were still driving almost the same as before, 59.8% drove significantly less frequently, and only 16.8% of the offenders gave up driving completely. The results of logistic regression models showed that offenders' compliance with ALLR was significantly correlated with their personal characteristics (age, income), penalty status (incarceration, duration of ALLR), and the need to drive for working, commuting and shopping. Elderly and low-income offenders were more likely to abide by the ALLR restriction. The application of the generalized estimating equations (GEE) model was used to identify the determinant factors affecting offenders' driving mileage, and to effectively estimate the driving mileage reduction as a result of the ALLR. It was found that ALLR is fairly effective in keeping offenders off the road, but that it may reduce their ability to make a living, resulting in the less fortunate becoming more helpless.     

Examining driver injury severity in two vehicle crashes – A copula based approach

A most commonly identified exogenous factor that significantly affects traffic crash injury severity sustained is the collision type variable. Most studies consider collision type only as an explanatory variable in modeling injury. However, it is possible that each collision type has a fundamentally distinct effect on injury severity sustained in the crash. In this paper, we examine the hypothesis that collision type fundamentally alters the injury severity pattern under consideration. Toward this end, we propose a joint modeling framework to study collision type and injury severity sustained as two dimensions of the severity process. We employ a copula based joint framework that ties the collision type (represented as a multinomial logit model) and injury severity (represented as an ordered logit model) through a closed form flexible dependency structure to study the injury severity process. The proposed approach also accommodates the potential heterogeneity (across drivers) in the dependency structure. Further, the study incorporates collision type as a vehicle-level, as opposed to a crash-level variable as hitherto assumed in earlier research, while also examining the impact of a comprehensive set of exogenous factors on driver injury severity. The proposed modeling system is estimated using collision data from the province of Victoria, Australia for the years 2006 through 2010.     

Motorcycle riding under the influence of alcohol: Results from the SARTRE-4 survey

Riding a motorcycle under the influence of alcohol is a dangerous activity, especially considering the high vulnerability of motorcyclists. The present research investigates the factors that affect the declared frequency of drink-riding among motorcyclists in Europe and explores regional differences. Data were collected from the SARTRE-4 (Social Attitudes to Road Traffic Risk in Europe) survey, which was conducted in 19 countries. A total sample of 4483 motorcyclists was interviewed by using a face-to-face questionnaire. The data were analyzed by means of multilevel ordered logit models. The results revealed significant regional differences (between Northern, Eastern and Southern European countries) in drink-riding frequencies in Europe. In general, declared drinking and riding were positively associated with gender (males), increased exposure, underestimation of risk, friends’ behaviour, past accidents and alcohol ticket experience. On the other hand, it was negatively associated with underestimation of the amount of alcohol allowed before driving, and support for more severe penalties.     

Effects of excessive speeding and falling asleep while driving on crash injury severity in Ethiopia: A generalized ordered logit model analysis

The severity of injury from vehicle crash is a result of a complex interaction of factors related to drivers’ behavior, vehicle characteristics, road geometric and environmental conditions. Knowing to what extent each factor contributes to the severity of an injury is very important. The objective of the study was to assess factors that contribute to crash injury severity in Ethiopia. Data was collected from June 2012 to July 2013 on one of the main and busiest highway of Ethiopia, which extends from the capital Addis Ababa to Hawassa. During the study period a total of 819 road crashes was recorded and investigated by trained crash detectors. A generalized ordered logit/partial proportional odds model was used to examine factors that might influence the severity of crash injury. Model estimation result suggested that, alcohol use (Coef. = 0.5565; p-value = 0.017), falling asleep while driving (Coef. = 1.3102; p-value = 0.000), driving at night time in the absence of street light (Coef. = 0.3920; p-value = 0.033), rainfall (Coef. = 0.9164; p-value = 0.000) and being a minibus or vans (Coef. = 0.5065; p-value = 0.013) were found to be increased crash injury severity. On the other hand, speeding was identified to have varying coefficients for different injury levels, its highest effects on sever and fatal crashes. In this study risky driving behaviors (speeding, alcohol use and sleep/fatigue) were a powerful predictor of crash injury severity. Therefore, better driver licensing and road safety awareness campaign complimented with strict police enforcement can play a pivotal role to improve road safety. Further effort needed as well to monitor speed control strategies like; using the radar control and physical speed restraint measures (i.e., rumble strips).     

Impact of mandating a driving lesson for older drivers at license renewal in Japan

In Japan, a driving lesson consisting of a lecture, a driver aptitude test, on-road driving assessment and a discussion session was added to the driving license renewal procedure for drivers aged 75 years or older in 1998 and for drivers aged 70 years or older in 2002. We investigated whether these additions contributed to a reduction in at-fault motor vehicle collisions (MVCs) by examining the trend of the at-fault MVC rates per licensed driver and the rate ratios of the older drivers relative to those aged 65–69 years for the years 1986–2011. All data were derived from nationwide traffic statistics. If the introduction of the lesson was effective in reducing at-fault MVCs of older drivers, the rate ratio should have declined, given that the lesson targeted only the older drivers. We found this was not the case, i.e., there was no declining trend in the at-fault MVC rate ratios of both drivers aged 75 years or older and drivers aged 70 years or older, relative to drivers aged 65–69 years, after the driving lesson at license renewal became mandatory for these older drivers. Therefore, the mandatory lesson for the older drivers at license renewal needs to be reconsidered.     

A model of Beijing drivers' scrambling behaviors

A major, but unstudied, cause of crashes in China is drivers that “scramble” to gain the right of way in violation of traffic regulations. The motivation of this study is to explore the features of drivers’ scrambling behaviors and the attitudes and driving skills that influence them. In this study, we established a scrambling behavior scale, and developed a driving attitude scale and a driving skill scale using factor analysis of an Internet survey of 486 drivers in Beijing. A structural equation model of scrambling behavior toward cars and pedestrians/cyclists was developed with attitudes and skills as predictors of behavior. Skills and attitudes of approval toward violations of traffic rules did not predict scrambling behaviors, while the motivation for safety and attitudes against violating traffic rules led to reduced scrambling behaviors. The current work highlights this peculiar aspect of Chinese roads and suggests methods to reduce the behavior.     

Does smart city implementation improve the subjective quality of life? Evidence from China

Understanding how smart city implementation influences quality of life (QOL) is of major importance to the goal of improving the citizens’ QOL within a smart city. This study adopted the generalized ordered logit model to explore the impact of core smart city investments—Information and Communication Technologies (ICT) and human capital—on subjective QOL using cross-sectional data from the 2018 China Family Panel Studies. Subjective QOL was measured by three indicators: life satisfaction, frequency of happy emotions, and frequency of depressed emotions. Results show that ICT is negatively associated with life satisfaction and the frequency of happy (positive) emotions, but not associated with depressed (negative) emotions. Human capital, by contrast, has a positive impact on life satisfaction and the frequency of happy emotions but has a negative impact on the frequency of depressed emotions. Further, ICT and human capital can affect subjective QOL through perceived government corruption and government performance. In addition, the impact of smart city investments on subjective QOL varies greatly according to age and education level. Policy implications are proposed to improve subjective QOL by making full use of smart investments.     

Analyzing the relationship between car generation and severity of motor-vehicle crashes in Denmark

While the number of fatalities on Danish roads has decreased in the last 40 years, research has not investigated the contribution of legislation changes, enforcement measures, technological enhancements, infrastructural improvements and human factors to this reduction. In the context of a Danish car market with remarkably high registration tax that causes potential buyers to hold longer onto old cars, the relationship between technological enhancements of vehicles and severity of crashes requires particular attention.     

基于计划行为理论的网络消费行为研究

为了探索中国网络消费行为,本研究以计划行为理论为基础,围绕网络消费中获得信息和购买产品行为,联系便利状况的影响因素,对270个样本进行网络问卷调查并提出对应的理论模型,最后利用结构方程模型软件(AMOS)进行验证性因素分析.研究结果显示感知行为控制(PBC)能够决定意图,但对行为没有直接影响,两个前行变量自我效能和可控...     

The effect of conformity tendency on pedestrians’ road-crossing intentions in China: An application of the theory of planned behavior

This paper presents a survey investigating the effects of age, gender and conformity tendency on Chinese pedestrians’ intention to cross the road in potentially dangerous situations. A sample of 426 respondents completed a demographic questionnaire, a scale measuring their tendency towards social conformity, and a questionnaire based on the theory of planned behavior (TPB). This questionnaire measured people's intentions to cross the road in two different road crossing situations, their attitude towards the behavior, subjective norms, perceived behavioral control, anticipated affect, moral norms, and perceived risk. The two scenarios depicted (i) a situation where the crossing was consistent with other pedestrians’ behavior (Conformity scenario) and (ii) a situation where the road crossing was inconsistent with other pedestrians (Non-Conformity scenario). Pedestrians reported greater likelihood in crossing the road when other pedestrians were crossing the road. People who showed greater tendencies towards social conformity also had stronger road crossing intentions than low conformity people for both scenarios. The predictive model explained 36% and 48% of the variance in the Non-Conformity and Conformity scenarios, respectively. Attitude, subjective norm, perceived behavioral control, and perceived risk emerged as the common predictors for both situations. The results have a number of theoretical and practical implications. In particular, interventions should focus on perceptions of risk that inform road users that crossing with other pedestrians against the signal is also unsafe and prohibited, and may lead to negative outcomes.     

Texting while driving: Psychosocial influences on young people's texting intentions and behaviour

Despite the dangers and illegality, there is a continued prevalence of texting while driving amongst young Australian drivers. The present study tested an extended theory of planned behaviour (TPB) to predict young drivers’ (17-24 years) intentions to [1] send and [2] read text messages while driving. Participants (n = 169 university students) completed measures of attitudes, subjective norm, perceived behavioural control, intentions, and the additional social influence measures of group norm and moral norm. One week later, participants reported on the number of texts sent and read while driving in the previous week. Attitude predicted intentions to both send and read texts while driving, and subjective norm and perceived behavioural control determined sending, but not reading, intentions. Further, intention, but not perceptions of control, predicted both texting behaviours 1 week later. In addition, both group norm and moral norm added predictive ability to the model. These findings provide support for the TPB in understanding students’ decisions to text while driving as well as the inclusion of additional normative influences within this context, suggesting that a multi-strategy approach is likely to be useful in attempts to reduce the incidence of these risky driving behaviours.     

Estimation with the nested logit model: specifications and software particularities

The paper discusses the nested logit model for choices between a set of mutually exclusive alternatives (e.g. brand choice, strategy decisions, modes of transportation, etc.). Due to the ability of the nested logit model to allow and account for similarities between pairs of alternatives, the model has become very popular for the empirical analysis of choice decisions. However the fact that there are two different specifications of the nested logit model (with different outcomes) has not received adequate attention. The utility maximization nested logit (UMNL) model and the non-normalized nested logit (NNNL) model have different properties, influencing the estimation results in a different manner. This paper introduces distinct specifications of the nested logit model and indicates particularities arising from model estimation. The effects of using various software packages on the estimation results of a nested logit model are shown using simulated data sets for an artificial decision situation. Financial support by the German Research Foundation (DFG) through the research project #BO1952/1 and the SFB 649 “Economic Risk” is gratefully acknowledged. The authors would like to thank two anonymous reviewers for their helpful and constructive comments.     

Aberrant driving behaviors: A study of drivers in Beijing

The addition of massive numbers of new drivers with varied driving experience to roads in China suggests it is important to understand the nature of aberrant driving behaviors for this new set of drivers. A paper-based and an Internet survey were administered. Factor analysis produced a five-factor structure for each survey. The distinction between violations and errors indicated in previous studies was confirmed. The violations included emotional violations, risky violations and self-willed violations, and the errors included inexperience errors and distraction errors. In contrast to previous work, age was not found to be a good predictor of violations though driving experience was. Contrary to expectations, non-automotive (bicycle) roadway experience or level of driving training failed to predict poor driving behavior. On-road experience is the key to risk for China's drivers. Good agreement between the paper-based and Internet surveys indicate online surveys to be a feasible way to conduct research of driving behavior at low cost.     

The theory of planned behavior,materialism, and aggressive driving

Purpose

Aggressive driving is a growing problem worldwide. Previous research has provided us with some insights into the characteristics of drivers prone to aggressiveness on the road and into the external conditions triggering such behavior. Little is known, however, about the personality traits of aggressive drivers. The present study proposes planned behavior and materialism as predictors of aggressive driving behavior.

Design/methodology

Data was gathered using a questionnaire-based survey of 220 individuals from twelve large industrial organizations in Israel. Our hypotheses were tested using structural equation modeling.

Findings

Our results indicate that while planned behavior is a good predictor of the intention to behave aggressively, it has no impact on the tendency to behave aggressively. Materialism, however, was found to be a significant indicator of aggressive driving behavior.

Research limitations

Our study is based on a self-reported survey, therefore might suffer from several issues concerning the willingness to answer truthfully. Furthermore, the sampling group might be seen as somewhat biased due to the relatively high income/education levels of the respondents.

Originality/value

While both issues, aggressive driving and the theory of planned behavior, have been studied previously, the linkage between the two as well as the ability of materialism to predict aggressive behavior received little attention previously. The present study encompasses these constructs providing new insights into the linkage between them.