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

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.

Evaluation of Traffic Signs Information Volume at Highway Tunnel Entrance Zone Based on Driver’s Visual Characteristics

“…The analytic hierarchy process was used to determine the importance of different information elements included in traffic signs in accordance with the drivers’ own individual assessments of their significance. The present study also adopts the same method to finally determine the weight of seven types of information elements in traffic signs ( 23 ). Table 1 displays the information volume and weight of each information element.…”

“…When the TSIV was more than 642 bits, there was an excess of information. To assure the driver’s safety and comfort, Han et al ( 23 ) examined the participant’s visual characteristics at varying driving speeds and TSIV levels, and found that the driver’s fixation duration, saccade duration, and saccade amplitude were most responsive to the change in TSIV. Using simulated driving tests and questionnaires, Xu et al ( 50 ) assessed the information threshold of graphical VMS and discovered that the number of roads displayed on the VMS negatively affected drivers’ legibility speed, legibility time, and subjective scores, with a significant drop in accuracy when the VMS displayed six road numbers.…”

“…At the same time, drivers’ eye movements can characterize their information preferences and mental workload levels when faced with various traffic scenarios ( 22 ). Therefore, analyzing the changes of drivers’ eye movements affected by TSIV is one of the methods used to evaluate whether TSIV is reasonable or not ( 23 ).…”

Evaluation of apparent effectiveness of safety sign group in underground cavern construction

“…From the perspective of human factors, the increase in the volume of sign information does not affect the cognitive ability of drivers due to their age [27] . The most crucial human factor in improving driver cognitive ability is the driver's driving experience [29] . Meanwhile, as the level of information increases, gender will begin to affect drivers' cognitive abilities [30] .…”

The Traffic Characteristic Analysis of Jalan Ciater Raya South Tangerang, Indonesia

“…Especially the driving pattern of two-wheeled vehicles is stochastic in the absence of lane discipline or turning motion discipline at intersections in traffic conditions, which affects the safety and overall characteristics of traffic flow. Arbitrary placement of traffic signs is becoming more common as a result of the acceleration of road construction and the increasing congestion of the road network (Yunita & Rifai, 2019); (Calvert & Snelder, 2018); (Nurinda, Priyanto, & Malkamah, 2019); (Singh, Barhmaiah, Kodavanji, & Santhakumar, 2020); (Han, Wang, & Chen, 2022).…”