reconciliation of regional travel model and passive device tracking data
DESCRIPTION
Reconciliation of regional travel model and passive device tracking data. 14 th TRB Planning Applications Conference. Leta F. Huntsinger Rick Donnelly. Introduction. Passively collected mobile phone data has shown promise as a low cost option for obtaining travel data: - PowerPoint PPT PresentationTRANSCRIPT
Reconciliation of regional travel model and passive device tracking data
14th TRB Planning Applications Conference
Leta F. HuntsingerRick Donnelly
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Introduction Passively collected mobile phone data has
shown promise as a low cost option for obtaining travel data: Speed data (Using Cell Phone Technology to
Collect Travel Data, Kyle Ward) Trip tables (Origin Destination Study using Cellular
Technology for Mobile, Al, Kevin Harrison) Freight Data (Freight Data Collection Technique
and Algorithm using Cellular Phone and GIS Data, Ming-Heng Wang, et. al.)
other Comparison of passively collected data
against traditionally collected survey data
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Challenges Household surveys
behaviorally rich, but small sample size at TAZ to TAZ level
Small TAZ to TAZ observations limit our understanding of flows at the sub-district level
Many small MPOs cannot afford household surveys Trip distribution parameters are the most
challenging to transfer Passively collected data
Large sample size, but lacks behavioral richness
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Data – Air Sage
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Triangle Regional Model
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Process
Disagg to TAZ
Apply factors to AirSage
matrix
Add IE, EI, and EE trips to AirSage
matrix
Develop AM factors from
TRM data
Apply AM factors to AirSage matrix
Convert AirSage
person trips to vehicle
trips
AM peak hour
assignment of AirSage
AM peak hour
assignment of TRM
Summarize MOEs and compare
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Results – travel time comparisons
1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103 109 115 1210.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
Trip Length Distribution (CongTT mins)
TRM Percent AirSage Percent Average Trip Length (TT)TRM 14.42Air Sage 15.51
TRM – slightly higher % of shorter trips
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Results – district to district flows
1 2 3 4 5 6 7 8 9 10 11 12123456789101112
District Map
District Trip Table Color Coded by Absolute and Relative Error
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Results – Assignment MOEs
Functional Classification 23 – 26 are rural facilities
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Results – Assignment MOEs
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Results – Assignment MOEs
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Results – Assignment MOEs
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Findings and Recommendations Early data set – includes Sprint data only Great source of validation data Low cost option Lacks behavioral richness of household survey Larger sample than household survey Continuing improvements are needed Useful to validate an estimated trip table Add to toolbox
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Acknowledgements Co-author – Rick Donnelly Kyle Ward, CAMPO Air Sage
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