Development of rural curve driving models using lateral placement and prediction of lane departures using the SHRP 2 naturalistic driving data

Oneyear, Nicole

doi:10.31274/etd-180810-4184

Cited by 1 publication

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Oneyear et al compared driver braking behavior at different rural stopped controlled intersections with different traffic control devices (TCDs) using the SHRP2 NDS data (14). They developed a linear mixed effects regression model and observed that overhead flashing beacons and on-pavement signing increase the distance at which drivers begin braking.…”

Section: Studies At Intersections Using Shrp2 Nds Datamentioning

confidence: 99%

“…They developed a linear mixed effects regression model and observed that overhead flashing beacons and on-pavement signing increase the distance at which drivers begin braking. They attempted to build a model of gap acceptance but were ultimately unsuccessful because of the small data size and video quality (14). Kim et al compared different driver distractions in carfollowing models based on driver eye-glancing behavior from a 100-car NDS database using decision tree analysis (15).…”

Section: Studies At Intersections Using Shrp2 Nds Datamentioning

confidence: 99%

See 1 more Smart Citation

A Study of Driver Eye-Glancing Behavior at Two Different Types of Unsignalized Intersections Based on SHRP2 NDS Data

Zhang

Zhou

et al. 2022

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

The study aimed to analyze driver visual workload at unsignalized intersections with wide medians on high-speed divided highways. The study focused on two types of traffic movements: direct left-turns from minor roads at conventional intersections and right-turns followed by U-turn movements at restricted crossing U-turn (RCUT) intersections. A total of 430 left-turn trips and 40 right-turn followed by U-turn trips were collected from the Second Strategic Highway Research Program Naturalistic Driving Study database. Researchers analyzed the data from driver eye-glancing videos, demographic information, and driving history. The entropy rate of each trip was calculated as an indicator of the driver's visual workload and was treated as the dependent variable for the statistical analysis. Entropy rate is the metric of the randomness associated with drivers’ scanning patterns. The higher the entropy rate, the higher the workload. A comparative study of these two movements at these two types of intersections was conducted. Statistical analyses indicated that drivers at RCUT intersections engaged in less random scanning and longer average fixation and spent more than 70% of the time looking forward during the whole movement. Younger drivers at both types of intersections had higher entropy rates. Additionally, drivers at conventional intersections with higher annual average daily traffic (≥30,000) had higher entropy rates. These results could provide a better understanding of driver workload and its safety implications at different types of unsignalized intersections.

show abstract

Section: Studies At Intersections Using Shrp2 Nds Datamentioning

confidence: 99%

Section: Studies At Intersections Using Shrp2 Nds Datamentioning

confidence: 99%