Exploring Crowdsourced Monitoring Data for Safety - Evaluation of Miovision Pedestrian Count Data [supporting datasets]

Le, Minh; Safety through Disruption (Safe-D) University Transportation Center (UTC)

doi:10.15787/VTT1/351GZJ

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ROSA P serves as an archival repository of USDOT-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by USDOT or funded partners. As a repository, ROSA P retains documents in their original published format to ensure public access to scientific information.

i

Exploring Crowdsourced Monitoring Data for Safety - Evaluation of Miovision Pedestrian Count Data [supporting datasets]

2019-11-04
See Associated Publication(s)
By Le, Minh

[PDF-156.66 KB]

English

Details Supporting Files Publications You May Also Like

Details:

Creators:

Le, Minh
Corporate Creators:

Safety through Disruption (Safe-D) University Transportation Center (UTC)
Subject/TRT Terms:

[+]

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

https://doi.org/10.15787/VTT1/351GZJ
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 data represent one week of selected hourly weekday and weekend pedestrian counts at two intersections in Austin, Texas. The pedestrian counts were produced from manually reducing video files from Miovision’s TrafficLink Multimodal Detection and Counts system. Eighty hours of video were gathered at each intersection between June 18 and July 14, 2019. However, only 40 hours at each intersection were reduced and evaluated. The manual counts were compared to Miovision’s count data across different combinations of lighting conditions and pedestrian volumes. Overall, Miovision system performed fairly well with accuracy results of 15% error for daytime and 24% for nighttime for the combined intersection legs.
Format:

ZIP
Funding:

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

University Transportation Centers US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:fcc705ef0375c36c076e92280f21ee7f2c39c9d8a77d0b6fa16739030c99a2b8
Download URL:

https://rosap.ntl.bts.gov/view/dot/54626/dot_54626_DS1.pdf
File Type:

Exploring_Crowdsourced_Monitoring_Data_for_Safety_Pedestrian_Metadata.json

json

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