Big Data in Transportation Program Management: Findings and Interpretations from the City of Toronto
Among North American big cities, the Toronto experiences some of the worst traffic congestion (1). Traffic congestion remains the object of policy intervention across many cities, enabling public discourse about desired future transportation services and better transportation policy. Big Data and business analytics have emerged as a potentially critical group of analyses, technologies, and means of informing program management, but what does "Big Data" really mean for program management in big cities facing the effects of traffic congestion. In this study, Big Data is defined as the proliferation of new information on transportation flow, speeds, and trip information from probe data, global positioning data, and Bluetooth technology in near-real time, all in such volumes that make conventional computing methods unable to manage the challenge. Although "Big Data" appears to be a catch phrase with a somewhat ambiguous meaning, there are reasons to believe that it may have important benefits for program management. First, this is illustrated by conceptually discussing how Big Data is different than other established analytical methods for performance monitoring. Second, empirical results from this study using archived probe speed data purchased from Inrix, Inc. for 2011, 2013, and 2014 on are shown to illustrate one initiative taken on by the City of Toronto to more tightly integrate Big Data solutions into road surface program management and performance monitoring.
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Supplemental Notes:
- This paper was sponsored by TRB committee ABJ30 Standing Committee on Urban Transportation Data and Information Systems.
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Corporate Authors:
500 Fifth Street, NW
Washington, DC United States 20001 -
Authors:
- Sweet, Matthias N
- Harrison, Carly
- Buckley, Stephen
- Kanaroglou, Pavlos
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Conference:
- Transportation Research Board 95th Annual Meeting
- Location: Washington DC, United States
- Date: 2016-1-10 to 2016-1-14
- Date: 2016
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References; Tables;
- Pagination: 20p
- Monograph Title: TRB 95th Annual Meeting Compendium of Papers
Subject/Index Terms
- TRT Terms: Network analysis (Planning); Performance measurement; Program management; Traffic congestion; Travel demand
- Uncontrolled Terms: Big data
- Geographic Terms: Toronto (Canada)
- Subject Areas: Data and Information Technology; Highways; I72: Traffic and Transport Planning;
Filing Info
- Accession Number: 01590576
- Record Type: Publication
- Report/Paper Numbers: 16-4514
- Files: TRIS, TRB, ATRI
- Created Date: Feb 18 2016 5:04PM