RESUMEN

Speeding is the factor that usually associated with fatal accident. However, riders have tendency to exceed their vehicle's speed above the regulated speed. Therefore, the likelihood of traffic accidents is significantly influenced by braking ability. Unfortunately, the braking capability has not been accommodated properly in the accident risk management, such as riding license obtaining mechanism. This paper focuses on the possibility of the development of riding licensing criteria based on rider's braking capability. The parameters used in the analysis are the safety factor and margin of safety, due to the differences in riders' braking capability. All the input data were collected from the result of previous related studies. Although the sample size is varied but data source was taken from relevant objects studies. The result of this study showed that impact speed and/or rider's involvement in fatal crashes could be reduced by increasing their braking ability. It strongly indicates that each rider should realize that their speed choices should be suited to their braking ability which could be increased during the riding licensing practical test. The utilization of a rider's braking abilities, which could provide a minimum margin of safety, should therefore be taken into consideration as a basis for the criteria used to obtain a riding license.

RESUMEN

The random parameters Generalized Linear Model (GLM) is frequently used to model speeding characteristics and capture the heterogenous effects of factors. However, this statistical approach is seldom employed for prediction and generalization due to the challenge of transferring its predefined errors. Recently, the emergence of explainable AI techniques has illuminated a new path for analyzing factors associated with risky driving behaviors. Despite this, there remains a gap that comparing results from machine and deep learning (ML/DL) approaches with those from random parameters GLM. This study aims to apply the random parameter GLM and explainable deep learning to evaluate the heterogenous effects of factors on the taxis' high-range speeding likelihood. Initially, a Beta GLM with random parameters (BGLM-RP) is developed to model the high-range speeding likelihood among taxi drivers. Additionally, XGBoost, a simple convolutional neural network (Simple-CNN), a deeper CNN (DCNN), and a deeper CNN with self-attention (DCNN-SA) are developed. The quantified explanations and illustrations of the factors' heterogenous effects from ML/DL models are derived from pseudo coefficients by decomposing factors' SHapley Additive exPlanations (SHAP) values. All the developed statistical, ML, and DL models are compared in terms of mean absolute errors and mean square errors on testing and full data. Results show that DCNN-SA excels in prediction on testing data, indicating its superior generalization capabilities, while BGLM-RP outperforms other models on full data. The DCNN-SA can reveal the heterogenous effects of factors for both in-sample and out-of-sample data, which is not possible for the random parameter GLM. However, BGLM-RP can reveal larger magnitudes of the factors' heterogenous effects for in-sample data. The signs and significances are identical between the varying coefficients from BGLM-RP and the pseudo coefficients from the ML/DL models, demonstrating the validity and rationale of using the proposed explanation framework to quantify the factors' effects in ML/DL models. The study also discusses the contributions of various factors to the high-range speeding likelihood of taxi drivers.

Asunto(s)

RESUMEN

As electric bikes (e-bikes) rapidly develop in China, their traffic safety issues are becoming increasingly prominent. Accurately detecting risky riding behaviors and conducting mechanism analysis on the multiple risk factors are crucial in formulating and implementing precise management policies. The emergence of shared e-bikes and the advancements in interpretable machine learning present new opportunities for accurately analyzing the determinants of risky riding behaviors. The primary objective of this study is to examine and analyze the risk factors related to speeding behavior to aid urban management agencies in crafting necessary management policies. This study utilizes a large-scale dataset of shared e-bike trajectory data to establish a framework for detecting speeding behavior. Subsequently, the extreme gradient boosting (XGBoost) model is employed to identify the level of speeding risk by leveraging its excellent identification ability. Moreover, based on measuring the degree of interaction among road, traffic, and weather characteristics, the investigation of the complex interactive effects of these risk factors on high-risk speeding is conducted using bivariate partial dependence plots (PDP) by its superior parsing ability. Feature importance analysis results indicate that the top five ranked variables that significantly affect the identified results of speed risk levels are land use density, rainfall, road level, curbside parking density, and bike lane width. The interaction analysis results indicate that higher levels of road and bike lane width correspond to an increased possibility of high-risk speeding among riders. Land use density, curbside parking density, and rainfall display a nonlinear effect on high-risk speeding. Introducing road level, bike lane width, and time interval could change the patterns of nonlinear effects in land use density, curbside parking density, and rainfall. Finally, several policy recommendations are proposed to improve e-bike traffic safety by utilizing the extracted feature values associated with a higher probability of high-risk speeding.

Asunto(s)

RESUMEN

Despite awareness campaigns and legal consequences, speeding is a significant cause of road accidents and fatalities globally. To combat this issue, understanding the impact of a driver's visual surroundings is crucial in designing roadways that discourage speeding. This study investigates the influence of visual surroundings on drivers in 15 US cities using 3,407,253 driver view images from Lytx, covering 4,264 miles of roadways. By segmenting and analyzing these images along with vehicle-related variables, the study examines factors affecting speeding behavior. After filtering the images, to ensure an accurate representation of the driver's view, 1,340,035 driver view images were used for analysis. Statistical models, including hurdle beta and bivariate probit models with random driver effects as well as Machine Learning's eXtreme Gradient Boosting (XGBoost), were employed to estimate speeding behavior. The results indicate that factors within the driver's visual environment, weather conditions, and driver heterogeneity significantly impact speeding. Speeding behavior also varies across geographic locations, even within the same city, suggesting a connection between local context and speeding. The study highlights the importance of the driver's environment, showing that more open spaces encourage speeding, while areas with trees and buildings are associated with reduced speeding. Notably, this research differs from previous studies by utilizing real-time data from dash cameras, providing a dynamic and accurate representation of the driver's visual surroundings. This approach enhances the reliability of the findings and empowers transportation engineers and planners to make informed decisions when designing roadways and implementing interventions to address effectively excessive speeding. In addition to examining speeding behavior, the study also analyzes time-headway, a key factor affecting safety and risky driver behavior, to explore its relationship with speeding. The findings offer valuable insights into the factors influencing speeding and the driver's visual environment. These insights can inform efforts to create environments that discourage speeding (and close car following) and ultimately reduce severe accidents caused by excessive speed (and tailgating).

Asunto(s)

RESUMEN

Regular speeders are those who commit speeding recidivism during a period. Among their speeding behaviors, some occurring in specific scenarios may cause more hazards to road users. Therefore, there is a need to evaluate the driving risks if the regular speeders have different speeding propensities. This study considers speeding-related hard-braking events (SHEs) as a safety surrogate measure and recognizes the regular speeders who encounter at least one SHEs during the study period as risky individuals. To identify speeding behaviors and hard-braking events from low-frequency GPS trajectories, we compare the average travel speed between pairwise adjacent GPS points to the posted speed limit and examine the speed curve and the corresponding travel distance between these GPS points, respectively. Thereafter, a logistic model, XGBoost, and three 1D Convolutional Neural Networks (CNNs) including AlexNet CNN, Mini-AlexNet CNN, and Simple CNN are respectively developed to recognize the regular speeders who encountered SHEs based on their speeding propensities. The proposed Mini-AlexNet CNN achieves a global F1-score of 91% and recall of 90% on the testing data, which are superior to other models. Further, the study uses the Shapley Additive exPlanation (SHAP) framework to visually interpret the contribution of speeding propensities on SHE likelihood. It is found that speeding by 50% or greater for no more than 285 m is the most dangerous kind among all the speeding behaviors. Speeding on roads without bicycle lanes or on roads with roadside parking and excessive accesses increases the probability of encountering SHEs. Based on the analyses, we put forward tailored recommendations that aim to restrict hazard-related speeding behaviors rather than speeding behaviors of all kinds.

Asunto(s)

RESUMEN

OBJECTIVE: Speeding behavior is recognized as one of the three main causes of risk on the roads and the most frequent ordinary violation among truck drivers. The study reported in this article focused on the driving anger personality trait and its role in predicting the observed speeding behavior in a sample of truck drivers. METHODS: A longitudinal study design was implemented, where 93 Serbian truck drivers filled in an adapted form of the Driving Anger Scale designed to assess the amount of driving anger evoked by specific traffic situations. The drivers' second-by-second speeding behavior data were captured using GPS tracking and recording devices installed in the vehicles over a period of six months. Hierarchical multiple linear regression was used to examine the predictive performance of driving anger. RESULTS: The results showed that more than three fourths of the exceeded speed ranged from 1 to 9 km/h and the highest percentage of speed events occurred in urban areas. The findings suggest that truck drivers experienced the highest level of anger in driving because of the discourteous and illegal behavior of other drivers. Younger truck drivers are more likely to drive fast compared to older drivers. Truck drivers who perform driving tasks more often and spend less time driving are more likely to participate in speed events. The results revealed that the subscales of anger, hostile gestures and traffic obstructions have a positive effect, while slow driving has a negative effect on the observed speeding behavior of truck drivers. CONCLUSIONS: This study confirms the predictive performance of driving anger facets. The results of the research can help us improve our understanding of the mechanisms of speeding behavior of truck drivers.

Asunto(s)

RESUMEN

Traffic accidents and related fatalities have become a major public health problem in the world. This study aims to identify the significant factors that play an important role in the speeding behavior of drivers. A comprehensive questionnaire was designed and conducted with the students and employees of the University of Nizwa. The questionnaire items were designed considering the theoretical background of the theory of planned behavior (TPB) and local socio-cultural aspects of the driving environment. A total of 303 usable samples were obtained. The collected data were analyzed using factor analysis and structural equation modelling technique. The extracted factors of speeding passion and culture, speeding attitudes, subjective norms and perceived behavioral control are significant determinants of drivers speeding behavior in Oman. The driver's speeding attitudes, speeding culture, and passion have positive association with the drivers' speeding intentions and behavior, and driver's perceived behavioral control forms negative relationship with the speeding behavior. This study confirms the application of the TPB in predicting the drivers' speeding behavior in the context of Oman.

Asunto(s)

RESUMEN

Objective: Speed has been identified as a key risk factor in road crashes, influencing the occurrence of a road crash and its severity. Excessive speeding is particularly dangerous on highway curves, and under critical traffic and environmental conditions. Various measures have been identified to be effective in managing and controlling vehicle speed. Among these, low-cost perceptual measures have been considered to be effective tools, as they generally increase the risks perceived by drivers, or alter speed perception, and consequently lead the drivers to reduce their speeds. The overall aim of this study is 1) to investigate the effectiveness of a set of perceptual treatments in reducing the driver's speed along a sharp curve of an existing rural road that is characterized by high crash rates, and 2) to identify the most effective measure(s) to implement in the field to counteract the problem of speeding.Methods: A driving simulator study was developed and four speed-reducing measures (white and red peripheral transverse bars (PTB), optical speed bars (OSB) and chevrons) were tested on a sample of forty-two drivers. The driving speeds recorded using the treatments tests were compared to a baseline condition (treatments were not applied). Subjective measures were also collected; these included the driver's evaluation of the desired speed, risk perception, road legibility, and markings comprehension, as based on screenshot pictures that represented the simulated configurations of the treatments.Results: The outcomes demonstrated an overall effectiveness of the perceptual treatments. Particularly, red PTB were associated with a speed-reduction of up to 12 km/h along the curve under study. An analysis of the questionnaires revealed that drivers did not fully comprehend the relevant message of PTB. Despite that, the drivers did unconsciously reduce their speeds; this confirms the effectiveness of such perceptual treatments based on the optical illusion that entices drivers to reduce their speeds.Conclusions: The findings demonstrated the effectiveness of the perceptual treatments, especially red PTB, in enticing drivers to reduce their speeds whilst approaching the sharp curve under study or driving through the curve.

Asunto(s)

RESUMEN

PURPOSE: Further explore the utility of protection motivation theory (PMT) in developing effective roadside anti-speeding messages. METHOD: Via an electronic link, 81 participants holding a current Australian driver's license rated all possible pairs of 18 PMT-derived anti-speeding messages in terms of their perceived effectiveness in reducing speed for themselves, and for drivers in general. RESULTS: While some messages revealed third-person effects (perceived as being more relevant to drivers-in-general than to self-as-driver), others showed reverse third-person effects (perceived as being more relevant to self-as-driver than to drivers-in-general). Compared with messages based on coping appraisal components, those derived from threat appraisal PMT components (perceived severity, counter-rewards, vulnerability) were rated as being more effective, both for participants themselves as driver, and for drivers-in-general. Compared with females, males reported threat appraisal messages as being more effective for reducing speed in themselves (reverse third-person effect). Aggregate scores for the 18 messages derived from this ipsative methodology correlated modestly with those from a normative study using similarly-worded items. DISCUSSION: As jurisdictions globally recognize speeding as a major road safety issue, effective anti-speeding campaigns are essential. Findings added to current knowledge of PMT's efficacy as a basis for generating effective anti-speeding messages and indicated areas for future research and application.

Asunto(s)

RESUMEN

Objective: The increasing number of road crashes and fatalities on Malaysian federal roads is a cause for concern. The main causes of road crashes and fatalities on federal roads have been attributed to the speeding behavior among drivers. As such, this research investigates the possible predictors from sociodemographic characteristics and attitudes in predicting speeding behavior among drivers on Malaysia federal roads. Methods: A face-to-face survey was conducted via purposive sampling on 300 drivers at rest areas at 6 crash hotspots on Malaysian federal roads. A set of questions related to speeding behavior was developed. The questionnaire consisted of 10 questions related to sociodemographic characteristics of the participants, 37 questions regarding attitudes toward speeding, and 1 question regarding speeding behavior. Subsequently, the sociodemographics were analyzed using binary logistic regression and the attitude variable was analyzed using factor analysis and binary logistic regression. Results: The findings from the sociodemographic data revealed that male gender, single/separated status, broad driving experience, crash experience, and leisure/vacation trip purposes emerged as significant predictors for speeding behavior. Additionally, the results of factor analysis for the attitude variable revealed 3 components: Affective, behavioral, and cognitive. These 3 components are originally derived from the ABC model of attitude (affective, behavioral, and cognitive) that was adapted in this study. Furthermore, the findings from binary logistic regression appeared consistent with the model assumption, signifying that behavioral aspects significantly influenced speeding behavior among drivers. Nevertheless, affective and cognitive components were insignificant predictors. Furthermore, strong agreement with speeding countermeasures was observed among the participants. Conclusion: In conclusion, sociodemographic characteristics and attitude have been proven to affect speeding behavior among drivers on Malaysian federal roads. The findings have important implications in designing driver risk profiles on federal roads to develop suitable countermeasures based on the 4E approach (engagement, education, enforcement, and engineering) to enhance road safety.

Asunto(s)

RESUMEN

PURPOSE: Analyze qualitative and quantitative data to determine the relative effectiveness of theoretically-developed anti-speeding messages, as judged by relatively inexperienced and experienced drivers, both for themselves as a driver, and for drivers in general. METHOD: Eight focus groups and three individual interviews were conducted. Participants initially completed a questionnaire, ranking sets of three anti-speeding messages representing each of the six components of protection motivation theory (PMT). Participants were encouraged to write down the reasons for their rankings. During group and individual facilitation sessions, the rankings and reasons for them were discussed to identify salient reasons for participants' judgments. The ranking data were analyzed quantitatively, with individual and group-based comments being analyzed thematically. RESULTS: Quantitative analyses of message pairs revealed five third-person effects (TPEs). Three messages were perceived as more relevant to drivers in general than to the participant-as-driver while two were associated with reverse TPEs, which participants perceived as more relevant to themselves-as-driver than for drivers in general. For four PMT components (rewards, self-efficacy, response efficacy, response costs), one or more messages received significantly higher rankings than one or more other messages representing the same component. Substantial variation was found within the individual and group discussion comments in respect of nearly all the messages, reflecting different driver perspectives and demographics. DISCUSSION: A general preference for shorter messages was evident, leading to a revision of most of the messages comprising the stimuli for this study. On the basis of the focus group and interview responses, consideration was given as to which messages would be recommended for a pilot field study.

Asunto(s)

RESUMEN

OBJECTIVE: The speed selection behavior of drivers has been reported to vary across driver demographics, psychological attributes, and vehicle-specific factors. In contrast, the effects of roadway geometric, traffic characteristics, and site-specific factors on speed selection are less well known. In addition, the relative degree of speeding has received little attention and thus remains relatively unexplored. This study aims to investigate the effects of roadway geometrics, traffic characteristics, and site-specific factors on speeding behavior of drivers. METHODS: A panel mixed logit fractional split model is estimated to analyze the proportion of speed limit violations across highway segments. To account for possible unobserved heterogeneity, the suitability of latent class model specification is also tested. Speeding data were collected from speed cameras along major arterials and highways in Queensland, Australia, and were merged with several other data sources including roadway geometric characteristics, spatial features of the surrounding environment, and driver behavioral factors. RESULTS: The results of the panel mixed logit fractional split model suggest a tendency among drivers to commit minor speed limit violations irrespective of causal factors. Among potential road geometric and traffic factors, radius of horizontal curves, percentage of heavy vehicle traffic on segments with divided median, posted speed limit, and road functional classification are factors that influence speeding behavior. Additionally, the deployment of covert speed cameras is found to decrease the likelihood of major speed limit violations along arterials or highways. CONCLUSIONS: An understanding of the influence of roadway geometrics and traffic characteristics on speeding behavior of drivers will inform the design of targeted countermeasures in order to reduce speed limit violations along highways.

Asunto(s)

RESUMEN

OBJECTIVE: This study examined whether reward and punishment sensitivities, as conceptualized by Gray and McNaughton's revised reinforcement sensitivity theory (RST), influenced young female drivers' attention toward a series of positive and negative antispeeding advertisement images. Young females' increasing crash risk is associated with their engagement in risky behaviors, which, in turn, has been associated with a stronger behavioral approach system (BAS; sensitive to rewards). It was predicted that individuals with a stronger BAS would elicit larger N100 and N200 mean amplitudes (reflecting greater attention) toward the positive images. Similar associations were predicted in relation to the fight-flight-freeze system (FFFS; sensitive to punishments) for negative images. METHOD: Twenty-four female drivers (17-25 years; final N = 16) completed Corr-Cooper's RST-Personality Questionnaire, prior to undergoing an event-related potential computerized visual task (i.e., oddball paradigm) that included positive, negative, and neutral images as targets against checkerboard image distractors. RESULTS: Contrary to expectations, individuals with a stronger BAS (Reward Reactivity and Impulsivity) demonstrated significantly larger N200 mean amplitudes at the Cz electrode site on presentation of the negative images than those with a weaker BAS. No other significant RST effects were found. CONCLUSIONS: These findings provide some preliminary objective support for the use of negative emotion-based road safety advertisements for young females. Further, this study provides support for using psychophysiological measures to enhance understanding of traffic injury persuasion.

Asunto(s)

RESUMEN

Speeding is a key contributing factor in roadway crashes in the United Arab Emirates (UAE) and elsewhere. Understanding how drivers behave towards speed management devices (i.e., speed cameras, radars, speed limits and speed warning signs) as well as factors affecting drivers' involvement in speed-related crashes might help in improving traffic safety. This study aims to identify and quantify the factors that affect drivers' compliance with speed enforcement and management devices as well as drivers' involvement in at-fault speed-related crashes in the Emirate of Abu Dhabi (AD), UAE. Two different datasets were collected from the same drivers' population in AD to provide different valuable information regarding the speeding problem. The first dataset was obtained from crashes' reports while, the second dataset was obtained from a self-reported questionnaire survey that was carried out among a total of 442 drivers in AD. Three logistic regression models were developed to identify the significant variables that affect (1) the occurrence of speed related crash (using crashes reports data), (2) drivers' compliance with speed limits (using questionnaire data), and (3) involvement in at-fault speed related crashes (using questionnaire data). The findings revealed that drivers' factors (gender, age, and nationality), vehicle factor (vehicle type), roads and environment factors (weather, road type and speed limit) were the significant factors that affect the occurrence of speed-related crashes in AD. The questionnaire findings revealed that running late, low values of posted speed limits and no sufficient police enforcement were the three main reasons that make motorists drive over the speed limits. In addition, the results indicated that drivers' characteristics (i.e., gender, education and income), drivers' responses to speed enforcement and management devices, and drivers' awareness about the importance of such devices in improving traffic safety were the main factors that affecting both drivers' compliance with speed enforcement devices and drivers' involvement in at-fault speed-related crashes. A comparison between the analysis results of traffic crashes and questionnaire datasets as well as a comparison between the findings of this study and existing literature are also provided.

Asunto(s)

RESUMEN

PURPOSE: Using a young driver sample, this experimental study sought to identify which combinations of threat-appraisal (TA) and coping-appraisal (CA) messages derived from protection motivation theory (PMT) participants would judge as most effective for themselves, and for other drivers. METHOD: The criterion variable was reported intention to drive within a signed speed limit. All possible TA/CA combinations of 18 previously highly-rated anti-speeding messages were presented both simultaneously and sequentially. These represented PMT's three TA components: severity, vulnerability, and rewards, and three CA components: self-efficacy, response efficacy, and response costs. Eighty-eight young drivers (34 males) each rated 54 messages for perceived effectiveness for self and other drivers. RESULTS: Messages derived from the TA severity component were judged the most effective. Response cost messages were most effective for females. Reverse third-person effects were found for both females and males, which suggested that combining TA and CA components may increase the perceived relevance of anti-speeding messages for males. DISCUSSION: The findings have potential value for creating effective roadside anti-speeding messages, meriting further investigation in field studies.

Asunto(s)

RESUMEN

According to the MODE model of attitude-to-behavior processes, attitude accessibility augments attitude-behavior correspondence, reflecting an automatic influence of attitudes on behavior. We therefore tested whether attitude accessibility moderates the attitude-behavior relationship in a context that is governed by characteristically automatic behavior, namely driving. In study 1 (correlational design), participants (N=130) completed online questionnaire measures of the valences and accessibilities of their attitudes towards speeding. Two weeks later, online questionnaire measures of subsequent speeding behavior were obtained. Attitude valence was a significantly better predictor of behavior at high (mean+1SD) versus low (mean-1SD) levels of attitude accessibility. In study 2 (experimental design), attitude accessibility was manipulated with a repeated attitude expression task. Immediately after the manipulation, participants (N=122) completed online questionnaire measures of attitude valence and accessibility, and two weeks later, subsequent speeding behavior. Increased attitude accessibility in the experimental (versus control) condition generated an increase in attitude-behavior correspondence. The findings are consistent with the MODE model's proposition that attitudes can exert an automatic influence on behavior. Interventions to reduce speeding could usefully increase the accessibility of anti-speeding attitudes and reduce the accessibility of pro-speeding attitudes.

Asunto(s)