Trip Reconstruction Tool for GPSBased Personal Travel Surveys
Eui-Hwan Chung
Introduction
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Transportation planning model
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Forecast and evaluate transportation scenarios
Require good-quality travel survey data
Conventional self-reporting survey method
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lack of reporting of short trips and actual routes traveled
poor data quality of
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travel start and end times,
total trip times and,
location of destination
the amount of detail that it is feasible to ask individuals and
households to report is well below that needed for the
activity-based micro-simulation models
Introduction
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Application of GPS for travel surveys
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As the GPS receiver is given to respondents,
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improve the quality of the collected data
serve the convenience of both respondents and survey
operators
The benefit of GPS [Wolf, 2000]
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trip origin, destination, and route data are automatically
collected without burden on the respondent
routes are recorded for all trips allowing for the postprocessing recovery of unreported or misreported trips
accurate trip start and end times are automatically
determined, as well as trip lengths
the GPS data can be used to verify self-reported data.
Introduction
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Approaches for GPS Application
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Electronic Travel Diary (ETD) with GPS
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For each trip a respondent records the following
information to ETD (Just replace paper)
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trip mode, vehicle identification, driver identification,
passenger identification, driver and passenger trip
purposes, trip start time, finish time (or duration), origin
location, destination location,and distance traveled.
In addition to these traditional elements, from GPS
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Route choice and travel speed can be captured
Introduction
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Approaches for GPS Application
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Passive in-vehicle GPS systems
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The intent of passive in-vehicle GPS systems
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To conduct a passive audit of in-vehicle travel
The GPS data will be used in a post-processing step
to the recorded travel diary of the respondent to
validate the reported data and/or to determine trip
under-reporting rates.
The data can be useful in telephone interview (by
refreshing respondent’s memory).
Introduction
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Approaches for GPS Application
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Total replacement of the travel diary with GPS.
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Use GPS data logger to completely replace, rather than
supplement, traditional travel diaries
All essential trip elements are derived through a computerized
process of all GPS data
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both respondent burden and telephone interview time could be
reduced significantly.
Previous research [Wolf, 2000] tried the following
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Trip detection (the number of trips)
To find Land uses and addresses for trip destinations
Trip Purpose Derivation
Trip Distance
Purpose
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Develop algorithms to reconstruct trips of a
traveler holding GPS-logger
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To automatically identify network links and modes
used
Illustration of Overall Concept
Walking
Mode
Change
Passenger Car
Used links
Used Data Sets for Base Map
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Data sources for map of this thesis
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Geographic features of the map
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Transportation property of the map
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DMTI CanMap® Streetfiles Version 6.2
2001 EMME/2 road network V1.0
Transit stop and timetable information from the TTC (Toronto
Transit Commission)
Spatial Scope : Downtown Toronto
Used Tool - ArcGIS
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Version 8.2
Popular and powerful GIS S/W
Provides programming interface
ArcObject – Component Object Model (COM)
Used Device – GPS(1/2)
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Global Positioning System(GPS)
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Provides 3D coordinates of current position on Earth using
artificial satellites
3D coordinates – at least 4 satellites
2D coordinates – at least 3 satellites
As distribution of the satellites is wider and the num. of the
satellites that a GPS receiver gets signal from
simultaneously is larger, the accuracy of the estimated
coordinate improves
Gets speed and azimuth of movement using the Doppler
effect
Used Device – GPS(2/2)
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GPS used in this research
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Wearable
With a logger – recorder of GPS points which are
collected by a GPS receiver
Identifying Used Road Segment
GPS
logger
GPS data
pre-processing
Reformat data
Delete invalid data
GIS
map
Main matching
process
Match the GPS point
with the link
Post matching
processing
Arrange the results of
the matching process
Pre-processing
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Reformat GPS data
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CVS file format (text file)  DBF format
UTC time  Local time
Change format of longitude/latitude
Add additional fields for the next process
Eliminate invalid data
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Number of satellites
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To get 2D coordinates, a GPS receiver should get signals from at least
3 GPS satellites simultaneously
if #Sat < 3 then delete the row
HDOP
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Dispersion of satellites from which a GPS receiver receives signal
The wider the dispersion of satellites, the better accuracy of the
measured coordinates
if HDOP > 5 then delete the row
Main matching process
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The purpose of this process
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Match the GPS point with the link
Identify the traveled links based on the respondent’s GPS
data
GPS points
Which link should be matched with
each GPS point ?
Road Network
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Find matched a link based on distance and azimuth
of moving direction
Main matching process
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Matching Algorithm
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Considering both distance between the GPS point and the
link and an azimuth of GPS point movements
Use topological information – make use of the geometry of
the arcs as well as the connectivity of the arcs
almost perpendicular
Post Matching Process
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The aim of the process
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Make a list of used links
Link OID: 104
(12)
Start (Point_OID = 1 )
(13)
Link OID: 102
(11)
(10)
1
0
Link OID: 103
(14)
Link OID: 101
No
0
1
2
3
…..
Link_OID
103
102
GPS_Start GPS_End FromNodeID ToNodeID MatchType
1
4
10
11
5
8
10
13
…..
Identification of Used Modes
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Available clues for estimation of used modes
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List of Used Links
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GIS Map
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Availability of transit
Property of used link – e.g.) freeway, one-way road
Location of transit stops
Travel Speed
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From GPS
Limitations of GPS
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Limitations
Effect of “Cold/warm Start”
Limitations of GPS
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Limitations
Effect of Urban Canyon
Limitations of GPS
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Limitations
Difficulty of Getting GPS Signal in a bus
Identification of Used Modes
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Basically, to estimate modes, good level of quality of GPS data is
required.
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No GPS Data  No Result
Good quality of data  Elaborate rule
Just depending on GPS Data, it is not easy to estimate all kinds of
mode configurations.
Assumptions
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A trip is one purpose trip.
A mode configuration pattern of a trip is one of the following
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Walk only,
Walk  Bicycle  Walk,
Walk  Passenger Car  Walk, and
Walk  Transit  Walk.
Both trip ends are not in urban canyon area.
There is no cold start of GPS receiver.
Identification of Used Modes
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Process for mode identification
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Two important clues
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Location of mode change points (ending point of the first walking, and the starting
point of the last walking)
Maximum speed from GPS data
Process
Step 1) Find two mode change points, the ending point of the first walking and
the starting point of the last walking segments.
Step 2) If two mode change points exist, go to step 3. Otherwise, go to step 4
Step 3) If both points are in a buffer area of bus stops, the used mode is a bus.
The size of the buffer is set at a radius of 40m from a stop.
Step 4) If maximum speed is faster than 32 km/h, the used mode is a
passenger car.
Step 5) If maximum speed is faster than 10 km/h, the used mode is a bicycle.
Otherwise, the used mode is a walk
Evaluation
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Test suggested methodology using real GPS data
Made 60 trips with GPS receiver
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Reproduce the 60 trips based on existing TTS data
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Randomly sample each trip from the data
O/D and Mode
Route of a trip was decided by a respondent
Both O and D are in downtown Toronto area
To satisfy assumptions
Mode Configuration
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10 Bus
24 Passenger Car
23 Walk
3 Bicycle
Evaluation
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Identification of used links
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% of correctly identified links: 78.5 %
% of un-detected links: 21.5 %
% of incorrectly identified links: 0 %
Identification of used modes
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% of correctly identified modes: 91.7(55/60)