Dangerous driving can cause accidents, injuries and loss of life. An efficient assessment helps to identify the absence or degree of dangerous driving to take the appropriate decisions while driving. Previous studies assess dangerous driving through two approaches: (i) using electronic devices or sensors that provide objective variables (acceleration, turns and speed), and (ii) analyzing responses to questionnaires from behavioral science that provide subjective variables (driving thoughts, opinions and perceptions from the driver). However, we believe that a holistic and more realistic assessment requires a combination of both types of variables. Therefore, we propose a three-phase fuzzy system with a multidisciplinary (computer science and behavioral sciences) approach that draws on the strengths of sensors embedded in smartphones and questionnaires to evaluate driver behavior and social desirability. Our proposal combines objective and subjective variables while mitigating the weaknesses of the disciplines used (sensor reading errors and lack of honesty from respondents, respectively). The methods used are of proven reliability in each discipline, and their outputs feed a combined fuzzy system used to handle the vagueness of the input variables, obtaining a personalized result for each driver. The results obtained using the proposed system in a real scenario were efficient at 84.21%, and were validated with mobility experts’ opinions. The presented fuzzy system can support intelligent transportation systems, driving safety, or personnel selection.
|Number of pages||25|
|Early online date||11 May 2022|
|Publication status||Published (in print/issue) - 11 May 2022|
Bibliographical noteFunding Information:
Funding: This research was partially funded by CONACYT (scholarship with support number 732958).
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
- Dangerous Driving
- Driver Behavior
- Dula Dangerous Driving Index
- Fuzzy Systems
- Intelligent Transportation Systems
- Dula dangerous driving index
- dangerous driving
- intelligent transportation systems
- fuzzy systems
- driver behavior