Predicting Changes in Driving Safety Performance on an Individualized Level Under Naturalistic Driving Conditions

Cavuoto, Lora; Megahed, Fadel

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Predicting Changes in Driving Safety Performance on an Individualized Level Under Naturalistic Driving Conditions

2019-02-25
By Cavuoto, Lora ; Megahed, Fadel

File Language:

English

Details

Creators:

Cavuoto, Lora ; Megahed, Fadel
Corporate Creators:

University at Buffalo, Buffalo ; Miami University
Corporate Contributors:

Transportation Informatics University Transportation Center ; United States. Department of Transportation. University Transportation Centers (UTC) Program
Subject/TRT Terms:

Automatic Data Collection Systems Driver Performance Forecasting Hours Of Labor Mirrors Safety Sensors Traffic Safety Truck Drivers Wearables Workload
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Final Report August 2017 – December 2018
Corporate Publisher:

Transportation Informatics University Transportation Center
Abstract:

The goal of this project is to examine how driver safety performance varies by location, time of day, hours on duty, and driver workload and to model changes in performance. The following tasks will be completed: 1) model input parameters for characterizing workload; 2) quantify changes in driving performance based on mirror checks and system alerts and evaluate these changes with respect to gold standard guidelines; and 3) investigate data-driven modeling approaches for performance prediction.
Format:

PDF
Funding:

DTRT13-G-UTC48
Collection(s):

University Transportation Centers
Main Document Checksum:

urn:sha256:bbc04732e9d2c8e9d51472f98414e98f3c1a198c0486b203d3a0ed29e6c60e05
Download URL:

https://rosap.ntl.bts.gov/view/dot/62523/dot_62523_DS1.pdf
File Type:

[PDF - 1.41 MB ]

File Language:

English

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