Exploring Crowdsourced Monitoring Data for Safety - Travel Patterns using GPS Waypoint Data [supporting datasets]

Das, Subasish; Wang, Ruihong; Safety through Disruption (Safe-D) University Transportation Center (UTC); Texas A & M Transportation Institute

doi:10.15787/VTT1/81SKJW

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Exploring Crowdsourced Monitoring Data for Safety - Travel Patterns using GPS Waypoint Data [supporting datasets]

[PDF-157.85 KB]

English

Details Supporting Files Publications You May Also Like

Details:

Creators:

Das, Subasish ; Wang, Ruihong

Das, Subasish ; Wang, Ruihong Less -
Corporate Creators:

Safety through Disruption (Safe-D) University Transportation Center (UTC) ; Texas A & M Transportation Institute

Safety through Disruption (Safe-D) University Transportation Center (UTC) ; Texas A & M Transportation Institute Less -
Corporate Contributors:

United States. Department of Transportation. University Transportation Centers (UTC) Program ; Texas (State)

United States. Department of Transportation. University Transportation Centers (UTC) Program ; Texas (State) Less -
Subject/TRT Terms:

[+]

Accuracy Bicycle Counts Crash Data Crash Risk Forecasting Crowdsourcing Cyclists Data Analysis Data And Information Technology High Risk Locations Highways Human Factors Information Technology Origin And Destination Pedestrian Counts Pedestrians Pedestrians And Bicyclists Safety And Human Factors Safety Factors Travel Patterns
DOI:

https://doi.org/10.15787/VTT1/81SKJW
Resource Type:

Dataset
Geographical Coverage:

Texas
Corporate Publisher:

Safety through Disruption (Safe-D) University Transportation Center (UTC) ; Texas A&M Transportation Institute ; United States. Dept. of Transportation. Office of the Assistant Secretary for Research and Technology

Safety through Disruption (Safe-D) University Transportation Center (UTC) ; Texas A&M Transportation Institute ; United States. Dept. of Transportation. Office of the Assistant Secretary for Research and Technology Less -
Abstract:

The third effort (Travel Patterns using GPS Waypoint Data) of this project explored the use of INRIX trip trace data to determine the patterns of origin-destination distributions and developed forty decision rules to define the origin-destination patterns. The research team used three separate databases to perform the analysis: Maryland INRIX waypoint trip data, Census block group level American Community Survey (ACS) 2013-2017 data, and Census block level Longitudinal Employer-Household Dynamics (LEHD) 2015 data.
Format:

ZIP
Funding:

69A3551747115/TTI-Student-05
Collection(s):

University Transportation Centers US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:ed52230ac7e463a8573f993bbfce78c75d7f246ac50706ec35b7aca3bdf6c092
Download URL:

https://rosap.ntl.bts.gov/view/dot/53577/dot_53577_DS1.pdf
File Type:

Dataset Safe-D Metadata file

bin

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