Road sign vision and driver behaviour in work zones

Vignali, Valeria; Bichicchi, Arianna; Simone, Andrea; Lantieri, Claudio; Dondi, Giulio

doi:10.1016/j.trf.2018.11.005

Cited by 35 publications

(13 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These findings confirm previous studies that found that subjects directed a high percentage of their fixations onto objects that were pertinent or task relevant to the street crossing. According to recent studies [59][60][61][62][63], drivers spend the majority of their time looking center of the carriageway, because this represents the direction of travel and the danger zone. Roadside vertical signs instead fall outside the driver's foveal visual field and require a saccadic movements or peripheral vision to be seen.…”

Section: Discussionmentioning

confidence: 99%

Urban Mid-Block Bicycle Crossings: The Effects of Red Colored Pavement and Portal Overhead Bicycle Crossing Sign

et al. 2022

Self Cite

View full text Add to dashboard Cite

This paper aims to investigate the effectiveness of some mid-block bicycle crossing elements by analyzing the drivers’ behavior, when approaching the bicycle crossings in a real road experiments with 18 participants. The eye-tracking instrument has been used to monitor the driver’s visual behavior during the test in an instrumented vehicle with GPS (global positioning system) and an inertial measurement unit (IMU). In particular, the drivers’ gaze was investigated frame by frame while approaching the mid-block bicycle crossings. The results showed that the red colored pavement increased the visibility of the mid-block crossing zone to 65.3% with respect to zebra crossing 59.6%. The drivers’ visual field was also narrowed by the portal overhead bicycle crossing sign and, consequently, drivers reduced their velocity and looked more to the vertical signs by 28%. The drivers’ speed reduction helped drivers to see the mid-block crossing elements from a greater distance with a higher fixation duration.

show abstract

Section: Discussionmentioning

confidence: 99%

Urban Mid-Block Bicycle Crossings: The Effects of Red Colored Pavement and Portal Overhead Bicycle Crossing Sign

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…The paper [39] used the Next Generation Simulation (NGSIM) dataset to propose an LSTM-based car tracking model that captures realistic traffic flow features and detect asymmetric driver behavior (which is a critical feature of human driver behavior). The effectiveness of road signs on driver safety is studied in [40] using GPS and video data. The Logistic Regression (LR) algorithm was trained to detect visible and non-visible driver activity.…”

Section: A Abnormal Driver Behaviormentioning

confidence: 99%

“…Accident Analysis and Prevention [58], [66], [83], [87], [89], [93], [94], [105], [116] Transportation Research Part F: Traffic Psychology and Behavior [40], [46], [104], [109], [111], [113], [115], [122] IEEE Access [61], [91], [97], [100], [101], [103] Journal of Safety Research [112], [114], [118], [119] Type of behavior Papers Abnormal driver behavior [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51],…”

Section: Source Publication Venue References Journal Articlementioning

confidence: 99%

Driver Behavior Classification: A Systematic Literature Review

2023

View full text Add to dashboard Cite

Driver behavior is receiving increasing attention as a result of the staggering number of road accidents. Many road safety reports regard human behavior as the most important factor in the likelihood of accidents. The detection and classification of aggressive or abnormal driver behavior is an essential requirement in the real world to avoid deadly road accidents and to protect road users. The automatic detection of the driver's behavior aids in the prevention of dangerous situations for the driver and all other participants in the driving environment, as well as the implementation of corrective measures. This paper presents a systematic literature review (SLR) of the classification of driver behavior. The study aim is to highlight and analyze the different types of driver behavior, data sources, datasets, features, and artificial intelligence techniques used to classify driver behavior and its performance. Based on the results obtained from the analysis of the selected works, we aim to identify the key contributions and challenges of studying driver behavior classification and propose potential avenues for further directions for practitioners and researchers.INDEX TERMS Driver behavior, intelligent transport system, systematic literature review, machine learning, deep learning.

show abstract

“…Secondly, they carried out a risk assessment based on the theory of traffic conflicts using the nonaccident data (e.g., the time to collision and deceleration rate to avoid the crash) [9][10][11][12]. In addition to the above two dominating research directions, some researchers have carried out related research from the perspective of psychology and physiology (e.g., speed preference and driver perception) [13][14][15].…”

Section: Literature Reviewmentioning

confidence: 99%

The Driving Risk Analysis and Evaluation in Rightward Zone of Expressway Reconstruction and Extension Engineering

Zhang

Wang

Yang

et al. 2020

Journal of Advanced Transportation

View full text Add to dashboard Cite

In the expressway reconstruction and expansion engineering, the form of rightward zone is increasingly adopted, and its complicated traffic conditions can easily cause traffic accidents. In order to quickly and effectively grasp the traffic risk of the right diversion section, this study employs average speed, the coefficient of variation, the equivalent minimum safety distance, and the deceleration as evaluation indexes of driving risk, and then analyses the influence rules of traffic volume, the proportion of large vehicles, and the length of the transition section on each evaluation index by using Vissim simulation software. On the basis of this, we determine the weight of each evaluation index by the entropy method and establish the driving risk index evaluation model of the work zone with multiple linear regression. The results show that the partial regression coefficients of traffic volume, the proportion of large vehicles, and the length of the transition section to the driving risk index are 0.059, 0.317, and 0.15, respectively. Finally, in this paper, we analyze the traffic risk of example section based on the driving risk evaluation model. The results of evaluation are consistent with the number of measured conflicts. This study proposes a new method for predicting the traffic risk of the expressway reconstruction and extension engineering, which can provide a reference for the development of safety management measures in the rightward zone.

show abstract

Road sign vision and driver behaviour in work zones

Cited by 35 publications

References 30 publications

Urban Mid-Block Bicycle Crossings: The Effects of Red Colored Pavement and Portal Overhead Bicycle Crossing Sign

Urban Mid-Block Bicycle Crossings: The Effects of Red Colored Pavement and Portal Overhead Bicycle Crossing Sign

Driver Behavior Classification: A Systematic Literature Review

The Driving Risk Analysis and Evaluation in Rightward Zone of Expressway Reconstruction and Extension Engineering

Contact Info

Product

Resources

About