arcgis machine learning classification of travel mode...
TRANSCRIPT
ArcGIS Machine Learning
Classification of Travel
Mode in GPS Data
June 30, 2016
2016 ESRI User Conference
Dara Seidl
Abt SRBI | pg 2
Introduction
GPS Travel Survey Considerations
Trip capture v. respondent burden
GPS data processing
Imputation of mode, trip purpose
Smartphone GPS, accelerometer
Abt SRBI | pg 3
Central Questions
1. How can GPS travel characteristics be used
to classify travel mode?
2. How can this classification be implemented in
ArcGIS?
Abt SRBI | pg 4
Accelerometer Data by Mode
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
Bike
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
Bus
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Walk 5-second samples captured by
Android smartphone
X
Y
Z
Abt SRBI | pg 5
Background
Accelerometer Implementation Issues
Cost of additional device
Integration of data sets
If using smartphone:
– Background data logging
– Disruption of picking up phone
– Variations between phones and apps
User fatigue
Abt SRBI | pg 6
Survey Instruments
GPS Data Logger
Abt SRBI | pg 7
Methods Overview
Collect GPS and Diary Travel
Data
Process Into Trips
Divide to 120-Second
Segments
Identify Mode for Reference
Segments
Random Forest Classification
Evaluate Mode Classification
Abt SRBI | pg 8
Data Characteristics
Participants
• 6 TotalNumber
• 5 San Diego
• 1 ChicagoGeography
•Mean: 26Age
Trips
• 92 trips
• 15.3/participantNumber
• 65 sec – 58 min
• Mean: 13 minDuration
• 0.1—12 miles
• Mean: 2.6 milesLength
Abt SRBI | pg 9
Segment Characteristics
Following trip processing, confirmed trips divided into 120-second segments
543 segments
90 segments per participant
148
219
38 3
135
0
50
100
150
200
250
Auto Bicycle Bus Metro Walk
120-Second Segments by Mode
Abt SRBI | pg 10
Segment Variables
Maximum Speed
Mean SpeedMean Heading
Change0-Speed
Frequency
Seconds of Rest at Bus
Stop
0
10
20
30
40
50
60
70
0 20 40 60 80 100
Mea
n S
pee
d (
mp
h)
Maximum Speed (mph)
Abt SRBI | pg 11
Segment Variables
0
20
40
60
80
100
120
140
160
180
0 10 20 30 40 50 60 70
Mea
n H
ead
ing
Ch
ange
(ra
d)
Mean Speed (mph)
Maximum Speed
Mean SpeedMean Heading
Change0-Speed
Frequency
Seconds of Rest at Bus
Stop
Abt SRBI | pg 12
Random Forest Classification
Machine learning algorithm
Grows many decision trees
Uses bagging to prevent
overfitting
Reports most useful predictors
Success in travel mode
classification (Stenneth et al.
2011; Ellis et al. 2014)
Abt SRBI | pg 13
Classification Results
Abt SRBI | pg 14
Segment Prediction Success
ActualClass
Total Class
Percent Correct
AutoN = 113
BicycleN = 189
BusN = 98
WalkN = 140
Auto 148 58.1% 86 7 52 3
Bicycle 219 80.8% 14 177 23 5
Bus 38 55.3% 12 4 21 1
Walk 135 97.0% 1 1 2 131
Total: 540
Average: 72.8%
Overall % Correct: 76.9%
Abt SRBI | pg 15
Variable Importance
0.0 20.0 40.0 60.0 80.0 100.0 120.0
Mean Speed
Max Speed
Zero Speed
Mean Head Change
Bus Stop
Abt SRBI | pg 16
Top Variables by Travel Mode
0.00
20.00
40.00
60.00
80.00
100.00
120.00
Auto Bicycle Bus Walk
Mean Speed Max Speed Mean Head Change
Zero Speed Bus Stop
Abt SRBI | pg 17
Bigger Picture
Classification considers small 120s segments,
rather than full trip characteristics
Considering micro- and macro-view of trips in
tandem may produce better results
Even if segment is misclassified, multiple segments
per trip can produce better estimates of overall trip
modesBicycle
Bicycle
Bicycle
Auto
Bicycle
Bicycle
BicycleTrip 7
Abt SRBI | pg 18
Conclusions
Mode classification by 120 s captures
typically under-represented multimodal
trips (Clifton and Muhs 2011)
Overall 77% accuracy in segment
mode prediction, 81% for bicycle
Random forest classification performs
relatively better for non-motorized
travel modes
