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Evaluating the feasibility of a passive travel survey in a complex urban environment 
November 9, 2012 
Cynthia Chen 
Fall...
Background 
• 
Declining sample sizes 
• 
Increasing non-response rates 
• 
Non-representative samples 
• 
Missing activit...
Research question 
• 
Is it feasible to have a completely passive travel survey? 
– 
Can we detect travel modes? 
– 
Can w...
Travel survey 
• 
Two datasets: 
– 
25 MPO employees, one weekday 
– 
24 students and staff at CCNY, five weekdays 
– 
Man...
Multi-modal network database 
• 
Roadway networks 
• 
Bus routes and stops 
• 
Subway routes 
• 
Subway entrances and exit...
Four steps to detect modes 
• 
Prepare GPS table 
• 
Divide GPS data into trips 
• 
Divide trips into segments 
• 
Detect ...
Prepare GPS table 
• 
Clean GPS data points 
• 
Identify stops and gaps
Divide GPS data into trips 
• 
Identify activity stops and trips
Divide trips into segments 
• 
Identify start and end points of a segment 
– 
First and last points of a gap 
– 
First and...
Detect walking 
• 
Distance of GPS points to a street link 
• 
Similarity between the street link and the theoretical line...
Detecting subway/rail 
• 
Distance of the first GPS point to nearest subway/rail entrance/exit 
• 
Distance of the last GP...
Detecting bus/car 
• 
Distance of the first GPS point to the nearest bus station 
• 
Distance of the last GPS point to the...
Challenges in urban areas 
• 
Urban canyon effect 
• 
Cold/warm start 
• 
Complex transportation network 
• 
Traffic conge...
Trip purpose identification 
• 
Trip end identification 
• 
Trip purpose identification 
– 
HB vs NHB 
• 
Mandatory 
• 
Pe...
Home 
Work 
Activity 
Travel 
Unknown 
Row Total 
Producer accuracy 
Home 
163,833 
2,252 
2,038 
1,988 
2,493 
172,604 
0...
Distance comparison 
• 
To home: 214 devices have a distance of less than 50 meters; 27 devices have distance between 50 a...
Evaluating the Feasibility of Passive Travel Survey collection in a Complex Urban Environment: Lessons Learned from the Ne...
Evaluating the Feasibility of Passive Travel Survey collection in a Complex Urban Environment: Lessons Learned from the Ne...
Evaluating the Feasibility of Passive Travel Survey collection in a Complex Urban Environment: Lessons Learned from the Ne...
Evaluating the Feasibility of Passive Travel Survey collection in a Complex Urban Environment: Lessons Learned from the Ne...
Evaluating the Feasibility of Passive Travel Survey collection in a Complex Urban Environment: Lessons Learned from the Ne...
Evaluating the Feasibility of Passive Travel Survey collection in a Complex Urban Environment: Lessons Learned from the Ne...
Evaluating the Feasibility of Passive Travel Survey collection in a Complex Urban Environment: Lessons Learned from the Ne...
Evaluating the Feasibility of Passive Travel Survey collection in a Complex Urban Environment: Lessons Learned from the Ne...
Evaluating the Feasibility of Passive Travel Survey collection in a Complex Urban Environment: Lessons Learned from the Ne...
Evaluating the Feasibility of Passive Travel Survey collection in a Complex Urban Environment: Lessons Learned from the Ne...
Evaluating the Feasibility of Passive Travel Survey collection in a Complex Urban Environment: Lessons Learned from the Ne...
Evaluating the Feasibility of Passive Travel Survey collection in a Complex Urban Environment: Lessons Learned from the Ne...
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Evaluating the Feasibility of Passive Travel Survey collection in a Complex Urban Environment: Lessons Learned from the New York City Case Study

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Cynthia Chen, University of Washington

The combination of increasing challenges in administering household travel surveys as well as advances in global positioning systems (GPS) and geographic information systems (GIS) technologies motivated this project. It tests the feasibility of using a passive travel data collection methodology in a complex urban environment, by developing GIS algorithms to automatically detect travel modes and trip purposes. The study was conducted in New York City where the multi-dimensional challenges include urban canyon effects, an extremely dense and diverse set of land use patterns, and a complex transit network. Our study uses a multi-modal transportation network, a set of rules to achieve both complexity and flexibility for travel mode detection, and develops procedures and models for trip end clustering and trip purpose prediction. The study results are promising, reporting success rates ranging from 60% to 95%, suggesting that in the future, conventional self-reported travel surveys may be supplemented, or even replaced, by passive data collection methods.

Published in: Engineering
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Evaluating the Feasibility of Passive Travel Survey collection in a Complex Urban Environment: Lessons Learned from the New York City Case Study

  1. 1. Evaluating the feasibility of a passive travel survey in a complex urban environment November 9, 2012 Cynthia Chen Fall 2012 PSU Transportation Seminar Series
  2. 2. Background • Declining sample sizes • Increasing non-response rates • Non-representative samples • Missing activities and trips
  3. 3. Research question • Is it feasible to have a completely passive travel survey? – Can we detect travel modes? – Can we detect trip purposes?
  4. 4. Travel survey • Two datasets: – 25 MPO employees, one weekday – 24 students and staff at CCNY, five weekdays – Manual diary survey for one day
  5. 5. Multi-modal network database • Roadway networks • Bus routes and stops • Subway routes • Subway entrances and exits • Commuter rail routes and stops
  6. 6. Four steps to detect modes • Prepare GPS table • Divide GPS data into trips • Divide trips into segments • Detect mode
  7. 7. Prepare GPS table • Clean GPS data points • Identify stops and gaps
  8. 8. Divide GPS data into trips • Identify activity stops and trips
  9. 9. Divide trips into segments • Identify start and end points of a segment – First and last points of a gap – First and last points of a walk segment • Identify walk segments – Speed – Duration
  10. 10. Detect walking • Distance of GPS points to a street link • Similarity between the street link and the theoretical line connecting two GPS points
  11. 11. Detecting subway/rail • Distance of the first GPS point to nearest subway/rail entrance/exit • Distance of the last GPS point to the nearest subway/rail entrance/exit • Distance of each GPS point to the subway/rail segment
  12. 12. Detecting bus/car • Distance of the first GPS point to the nearest bus station • Distance of the last GPS point to the nearest bus station • Speed and acceleration
  13. 13. Challenges in urban areas • Urban canyon effect • Cold/warm start • Complex transportation network • Traffic congestion • Mixed land use
  14. 14. Trip purpose identification • Trip end identification • Trip purpose identification – HB vs NHB • Mandatory • Personal business • Social recreation – HB: 67% – NHB: 78%
  15. 15. Home Work Activity Travel Unknown Row Total Producer accuracy Home 163,833 2,252 2,038 1,988 2,493 172,604 0.95 Work 11,667 63,878 2,751 1,277 1,357 80,930 0.79 Activity 12,177 11,058 20,861 3,991 966 49,053 0.43 Travel 9,537 6,269 5,749 31,720 477 53,752 0.59 Unknown 0 0 0 0 0 0 0 Column Total 197,214 83,457 31,399 38,976 5,293 356,339 User accuracy 0.83 0.77 0.66 0.81 0 0.79
  16. 16. Distance comparison • To home: 214 devices have a distance of less than 50 meters; 27 devices have distance between 50 and 200 meters; 9 devices have a distance over 1000 meters • To work: 160 devices have a distance of less than 50 meters; 80 devices have distance over 200 meters

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