Self-reported violations, errors and lapses for older drivers: Measuring the change in frequency of aberrant driving behaviours across five time-points

To what extent do our walking and cycling behaviours relate to each other, and do we cycle as well as we think we do? Piloting the walking and cycling behaviour questionnaires in the UK

Greater uptake of active transport has been argued as necessary for the transport system to achieve relevant sustainability and public health goals; however, the research tools used to investigate behaviour when using these modes are far less well-developed than those used to investigate driving behaviour. This study takes two self-report behavioural measures, the Walking Behaviour Questionnaire (WBQ) and the Cycling Behaviour Questionnaire (WBQ), and pilots them in the UK. Exploratory and confirmatory factor analyses with data from 428 respondents revealed factor structures different to those described in the limited number of previous studies that used the CBQ and WBQ. Across both questionnaires, scales measuring intentional behaviour differed from original descriptions to a greater extent than did the scale concerning unintentional attention or memory errors. In addition to a validation exercise, this research explored the relationships between variables, finding a correlation between the reported performance of unintentional errors when walking and cycling. Looking in more detail at cycling behaviours, we found that those who rated themselves as more proficient cyclists also reported performing fewer unintentional cycling errors. Results also showed self-reported helmet use to bear little to no relationship with other self-reported cycling behaviours or self-rated cycling proficiency. Finally, using structural equation modelling, we demonstrated that responses to the CBQ add very little (over and above age, gender, and exposure to the road environment) to the explanation of self-reported past collision involvement. In total, only 7% of the variation in past collision involvement was explained by the included variables. We urge caution when using self-report behavioural measures that have not been validated in the context of intended use, and the importance of using such measures in combination with other approaches rather than in isolation when trying to develop an understanding of overall system performance.

A Proactive Recognition System for Detecting Commercial Vehicle Driver’s Distracted Behavior

Road traffic accidents regarding commercial vehicles have been demonstrated as an important culprit restricting the steady development of the social economy, which are closely related to the distracted behavior of drivers. However, the existing driver’s distracted behavior surveillance systems for monitoring and preventing the distracted behavior of drivers still have some shortcomings such as fewer recognition objects and scenarios. This study aims to provide a more comprehensive methodological framework to demonstrate the significance of enlarging the recognition objects, scenarios and types of the existing driver’s distracted behavior recognition systems. The driver’s posture characteristics were primarily analyzed to provide the basis of the subsequent modeling. Five CNN sub-models were established for different posture categories and to improve the efficiency of recognition, accompanied by a holistic multi-cascaded CNN framework. To suggest the best model, image data sets of commercial vehicle driver postures including 117,410 daytime images and 60,480 night images were trained and tested. The findings demonstrate that compared to the non-cascaded models, both daytime and night cascaded models show better performance. Besides, the night models exhibit worse accuracy and better speed relative to their daytime model counterparts for both non-cascaded and cascaded models. This study could be used to develop countermeasures to improve driver safety and provide helpful information for the design of the driver’s real-time monitoring and warning system as well as the automatic driving system. Future research could be implemented to combine the vehicle state parameters with the driver’s microscopic behavior to establish a more comprehensive proactive surveillance system.

Comparative analysis of data reduction techniques for questionnaire validation using self-reported driver behaviors

INTRODUCTION

Exploratory data reduction techniques, such as Factor Analysis (FA) and Principal Component Analysis (PCA), are widely used in questionnaire validation with ordinal data, such as Likert Scale data, even though both techniques are indicated to metric measures. In this context, this study presents an e-survey, conducted to obtain self-reported behaviors between Brazilian drivers (N = 1,354, 55.2% of males) and Portuguese drivers (N = 348, 46.6% of males) based on 20 items from the Driver Behavior Questionnaire (DBQ) on a five-point Likert Scale. This paper aimed to examine DBQ validation using FA and PCA compared to Categorical Principal Component Analysis (CATPCA) which is more indicative to use with Likert Scale data.

RESULTS

The results from all techniques confirmed the most replicated factor structure of DBQ, distinguishing behaviors as errors, ordinary violations, and aggressive violation. However, after Varimax rotation, CATPCA explained 11% more variance compared to FA and 2% more than PCA. We identified cross-loadings among the component of the techniques. An item changed its dimension in the CATPCA results but did not change the structural interpretability. Individual scores from dimension 1 of CATPCA were significantly different from FA and PCA. Individual scores from factor 1 of CATPCA were significantly different from FA and PCA. Practical applications: The CATPCA seems to be more advantageous in order to represent the original data and considering data constrains. In addition to finding an interpretable factorial structure, the representation of the original data is regarded as relevant since the factor scores could be used for crash prediction in future analyses.

Macro-level accident modeling in Novi Sad: A spatial regression approach

In this study, a macroscopic analysis was conducted in order to identify the factors which have an effect on traffic accidents in traffic analysis zones. The factors that impact accidents vary according to the characteristics of the observed area, which in turn leads to a discrepancy between research and practice. The total number of accidents was observed in this paper, along with the number of motorized and non-motorized mode accidents within a three-year period in the city of Novi Sad. The models used for this analysis were spatial predictive models comprised of the classical predictive space model, spatial lag model and spatial error model. The spatial lag model showed the best performances concerning the total number of accidents and number of motorized mode accidents, whereas the spatial error model was prominent within the number of non-motorized mode accidents. The results found that increasing Daily Vehicle-Kilometers Traveled, parking spaces, 5-legged intersections and signalized intersections increased all types of accidents. The other demographic, traffic, road and environment characteristics showed that they had a different effect on the observed types of accidents. The results of this research can be benefitial to reserachers who deal with traffic engineering, space planning as well as making decisions with the aim of preparing countermeasures necessary for road safety improvement in the analysed area.