Analysis of Traffic Signs Information Volume Affecting Driver’s Visual Characteristics and Driving Safety

Lei Han, Zhigang Du (), Shoushuo Wang and Ying Chen
Additional contact information
Lei Han: School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
Zhigang Du: School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
Shoushuo Wang: School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China
Ying Chen: School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China

IJERPH, 2022, vol. 19, issue 16, 1-23

Abstract: To study the influence of traffic signs information volume (TSIV) on drivers’ visual characteristics and driving safety, the simulation scenarios of different levels of TSIV were established, and 30 participants were recruited for simulated driving tests. The eye tracker was used to collect eye movement data under three-speed conditions (60 km/h, 80 km/h, and 100 km/h) and different levels of TSIV (0 bits/km, 10 bits/km, 20 bits/km, 30 bits/km, 40 bits/km, and 50 bits/km). Principal component analysis (PCA) was used to select indicators sensitive to the influence of TSIV on the drivers’ visual behavior and to analyze the influence of TSIV on the drivers’ visual characteristics and visual workload intensity under different speed conditions. The results show that the fixation duration, saccade duration, and saccade amplitude are the three eye movement indicators that are most responsive to changes in the TSIV. The driver’s visual characteristics perform best at the S3 TSIV level (30 bits/km), with the lowest visual workload intensity, indicating that drivers have the lowest psychological stress and lower driving workload when driving under this TSIV condition. Therefore, a density of 30 bits/km is suggested for the TSIV, in order to ensure the security and comfort of the drivers. The theoretical underpinnings for placing and optimizing traffic signs will be provided by this work.

Keywords: traffic signs; TSIV; simulation; visual characteristics; visual workload intensity; driving safety (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/16/10349/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/16/10349/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:16:p:10349-:d:892800

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().