Title: Experimental research on the threshold of traffic signs information quantity in mountainous roads

Authors: Yunwei Meng; Lei Wang; Zixiao Wang; Shibao Li; Zhenyu Quan; Guangyan Qing

Addresses: College of Traffic and Transportation, Chongqing Jiaotong University, 66 Xuefu Avenue, Nan'an District, Chongqing, 400074, China ' College of Traffic and Transportation, Chongqing Jiaotong University, 66 Xuefu Avenue, Nan'an District, Chongqing, 400074, China ' College of Civil Engineering, Chongqing Jiaotong University, 66 Xuefu Avenue, Nan'an District, Chongqing, 400074, China ' College of Traffic and Transportation, Chongqing Jiaotong University, 66 Xuefu Avenue, Nan'an District, Chongqing, 400074, China ' College of Traffic and Transportation, Chongqing Jiaotong University, 66 Xuefu Avenue, Nan'an District, Chongqing, 400074, China ' China Merchants Highway Information Technology (Chongqing) Co., Ltd., No. 33, Xuefu Avenue, Nan'an District, Chongqing, 400060, China

Abstract: The complexity of mountain roads requires drivers to maintain heightened attention and quick reaction times. Insufficient recognition of traffic signs is a major cause of traffic risks. To enhance driving safety on mountain roads, a simulated experiment combining information theory and psychological tests was conducted with 45 selected drivers. Data on drivers' response time (RT) and accuracy (ACC) under different traffic sign information densities (TSID) were collected, and a mathematical fitting model was established. The study found that when TSID ≤ 13.64 bits/m2, the average RT ≤ 1300 ms. When a single traffic sign displays up to 6 information items and TSID ≤ 15.5 bits/m2, ACC ≥ 80%. When multiple traffic signs are used together, the total number of information items displayed should not exceed 7. These findings provide theoretical guidance for the placement of traffic signs on mountain roads.

Keywords: traffic sign; mountainous roads; traffic sign information quantity; cognitive load.

DOI: 10.1504/IJVSMT.2025.150167

International Journal of Vehicle Systems Modelling and Testing, 2025 Vol.19 No.4, pp.309 - 334

Received: 29 Apr 2024
Accepted: 23 Aug 2024

Published online: 02 Dec 2025 *

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