Evaluating the feasibility of a passive travel survey in a

complex urban environment

November 9, 2012

Cynthia Chen

Fall 2012 PSU Transportation Seminar Series

Background

• Declining sample sizes

• Increasing non-response rates

• Non-representative samples

• Missing activities and trips

Research question

• Is it feasible to have a completely passive travel survey? – Can we detect travel modes?

– Can we detect trip purposes?

Travel survey

• Two datasets: – 25 MPO employees, one weekday

– 24 students and staff at CCNY, five weekdays

– Manual diary survey for one day

Multi-modal network database

• Roadway networks

• Bus routes and stops

• Subway routes

• Subway entrances and exits

• Commuter rail routes and stops

Four steps to detect modes

• Prepare GPS table

• Divide GPS data into trips

• Divide trips into segments

• Detect mode

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 last points of a walk segment

• Identify walk segments – Speed

– Duration

Detect walking

• Distance of GPS points to a street link

• Similarity between the street link and the theoretical line connecting two GPS points

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

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

Challenges in urban areas

• Urban canyon effect

• Cold/warm start

• Complex transportation network

• Traffic congestion

• Mixed land use

Trip purpose identification

• Trip end identification

• Trip purpose identification – HB vs NHB

• Mandatory

• Personal business

• Social recreation

– HB: 67%

– NHB: 78%

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

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