Analyzing Drivers’ Mental Patterns Using Q-Methodology

Abstract

The human factor is the most important cause of road accidents. Investigating the drivers' mental patterns can lead to a better understanding of the factors that affect drivers to make a mistake and thus increase the likelihood of an accident. In this study, mental patterns of drivers as a human characteristic are determined through a questionnaire survey. To do this, 166 participants (18 -65 years) were asked to express their opinion on the possible effect of 25 factors on the occurrence of accidents. These factors were selected through the investigation of the accident database during the last three years in different areas of the case study. To analyze the data extracted from the survey, Q-methodology was used. The results of the factor analysis showed that there are 5 mental patterns among the participants. Based on the driver's opinion, human factors and road conditions were the most and least influential accident-generating items, respectively. The most significant reason for accidents determined by drivers was human errors including 1) unauthorized overtaking, 2) unauthorized speed, 3) driver distractions (such as cell phone), and 4) driver physical disability (such as visual impairment). Moreover, the failure of the vehicle was mostly reported as another influential contributor to accidents. It is worth mentioning that the results of this study can be used to minimize accidents resulted from drivers' behavioral errors by suggesting strategies for enhancing their performance through new manuals which is a step towards a safer road.

Safety Margin Evaluation of Pedestrian Crossing through Critical Thresholds of Surrogate Measures of Safety: Area with Zebra Crossing versus Area without Zebra Crossing

“…Various research methods, such as accident databases, questionnaire research, and in-field observation-based methods, have assessed pedestrian safety (6)(7)(8)(9)(10)(11)(12)(13)(14). Despite significant achievements, there are several shortcomings, including lack of accident data, especially injuries (15,16), incomplete reports, or the high expense of in-field observation (17), incorrect questionnaire information, or an insufficient number of attendants for filling the questionnaires (18), and project time constraints (19).…”