An Application of Mitigating Flow Bias
from Origin/Destination Surveys in a
Transit System
Jamie Snow (AECOM)
David Schmitt (AECOM)
May 20, 2015
Addressing Flow Bias in Transit Surveys
• Accurate information on flows is critical in transportation
planning
• Observation: Expansion methods using only the origin/
destination survey typically under-represent short trips and
misrepresent flows
– Difficult to know where the biases occur
• Can flows be made more accurate using auxiliary data and
iterative proportional fitting (IPF) techniques?
2015 TRB Planning Applications
Conference
May 20, 2015
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Advanced Expansion Process (AEP) Methodology
Define route
segmentation
Develop segmentto-segment on-tooff flows using
survey and
ancillary data
Develop
origin/destination
segment-tosegment flows
using survey
Apply expansion
factors to main
survey records
Create synthetic
origin/destination
records where
necessary
Divide on-to-off
flows by
origin/destination
flows
2015 TRB Planning Applications
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May 20, 2015
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Define Route Segmentation
• Segment the transit routes
using
– Natural boundaries
– Major cross streets
– Large differences in travel
patterns
• Local routes represented by
4-6 segments
• Express and crosstown routes
represented by 2-3 segments
2015 TRB Planning Applications
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May 20, 2015
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Develop On-to-off Flows
• Automatic Passenger Counter (APC) data
– Averaged over 5 months
– Used to generate the column and row marginals for IPF
• On-to-off counts
– Collected at 20% or 100% sampling rate, depending on route
– Used to generate the “seed” matrices for the IPF process
– Developed synthetic records where APC and/or OD > 0 but
On-to-off = 0
• Use IPF to expand on-to-off counts
– On-to-off counts as “seed” matrices
– APC counts for row/column marginals
– Result: segment-to-segment flows
2015 TRB Planning Applications
Conference
May 20, 2015
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Using IPF to Develop Segment to Segment Flows
Generated from the on-to-off data by route, time
period, direction, and segment
Generated
from the APC
data by route,
time period,
direction, and
segment
Indicates the need for a
Synthetic Record
2015 TRB Planning Applications
Conference
May 20, 2015
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Develop Origin/Destination (OD) Flows
• Initial expansion factors developed by dividing segment-tosegment flows expanded to APC values by segment-tosegment count of OD survey records
• Synthetic records developed in cells where On-to-off flows
and/or APC > 0 but OD = 0
2015 TRB Planning Applications
Conference
May 20, 2015
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Origin/Destination Expansion Example
Segment-tosegment flows
expanded to
APC values
Segment-tosegment count of
OD survey
records *
Segment-tosegment
expansion
factors
*Synthetic origin/destination survey record
2015 TRB Planning Applications
Conference
May 20, 2015
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Performed by direction and time
period for each route
* Synthetic OD survey records
developed where the observed
flow > 0 but count of main
survey records = 0
Is AEP Better Than Traditional RTD Expansion?
• Traditional expansion: route, time period, and direction
using OD survey only (RTD)
• Objective: compare AEP results to traditional expansion
results using APC data as the “ground truth”
• Metrics
• Mean Absolute Percent Error (MAPE)
• Root Mean Square Error (RMSE)
• Three COTA routes
• Local Route 1 (large – 8,800 daily boardings)
• Crosstown Route 89 (medium – 1,000 daily boardings)
• Express Route 61 (small – 150 daily boardings)
2015 TRB Planning Applications
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May 20, 2015
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Comparison Results – Local Route 1
Average Daily Ridership = 8,824
Expanding the data
using RTD produces
mean absolute
percentage errors
that are 3-5 times
higher than
expanding the data
with the AEP
Similarly root mean
square errors are 2-4
times higher
expanding the data
using RTD
7 segments
2015 TRB Planning Applications
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May 20, 2015
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Comparison Results – Route 89
Average Daily Ridership = 999
Similar to Route 1,
AEP expansion has
less MAPE than RTD.
Less segmentation of
the route begins to
close the gap between
expansion methods
RMSEs are closer but
AEP methodology still
drastically
outperforms RTD
expansion
3 segments
2015 TRB Planning Applications
Conference
May 20, 2015
Page 11
Comparison Results – Route 61
Average Daily Ridership = 139
2 segments
Again, AEP outperforms RTD expansion when comparing MAPE
When the minimal number of segments are utilized, RMSE for both
methodologies are very similar
2015 TRB Planning Applications
Conference
May 20, 2015
Page 12
Results Continued
AEP methodology addresses flow movements better using
number of segments traveled; minimizes short trip bias
2015 TRB Planning Applications
Conference
May 20, 2015
Page 13
Results Continued
Expansion Factor (EF) Criteria
Criteria
EF=0
0-0.4
0.4-1.0
1.0-2.0
Total
EF>2.0
% of Total
Total # of Records
288
355
1,759
10,965
149
13,516
100%
% of the Total
2%
3%
13%
81%
1%
100%
0
190
256
582
10
1,038
8%
200
0
0
0
0
200
1%
88
0
0
0
0
88
1%
OD sampling rate to On2Off sampling rate by a ratio > 1.5
0
86
673
589
0
1,348
10%
OD sampling rate to On2Off sampling rate by a ratio <0.3333
0
0
0
0
139
139
1%
On2Off expansion factor 0 - 0.5
0
58
155
442
0
655
5%
Result of RTD expansion (no On2Off records collected)
0
21
48
117
0
186
1%
Reasonable expansion factors in both surveys
0
0
627
9,235
0
9,862
73%
How many are/include
Synthetic Records
Missing O, B, A, and/or D?
Erroneous information?
Large number of OD survey
records with an expansion factor
less than 1.0 (+2,400 or 18%)
2015 TRB Planning Applications
Conference
May 20, 2015
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The causes were
explicable based on the
data
Conclusions
• Using IPF with on-to-off flow data and APCs produces
more accurate boarding and alighting results than RTD in
these routes
• Also improved representation of short trips
• Missing flow movements incorporated into the expanded
dataset which removed biases from over- and
underweighting of various flow movements
2015 TRB Planning Applications
Conference
May 20, 2015
Page 15
Acknowledgements
Rebekah Anderson, Ohio Department of Transportation
Dr. Mark McCord, The Ohio State University
Dr. Rabi Mishalani, The Ohio State University
Mike, McCann, The Central Ohio Transportation Authority
2015 TRB Planning Applications
Conference
May 20, 2015
Page 16
Thank you
Jamie.Snow@aecom.com
David.Schmitt@aecom.com
May 20, 2015