GIS Analysis of
Commercial Trucking
Movements from a
Canadian Perspective
GEOG 596A Peer Review
Kristina Kwiatkowski
Advisor: Justine Blanford
Presentation Outline
O Background Information
O Movement Analysis
O Data
O Currently Methodology
O Objective
O Methodology
O Anticipated Project Outcome
O Project Timeline
Canada - Trucking Overview
Source: US Dept. of Transportation
Source: Transport Canada
Canadian-American Border
O over 8000km in length
O in 2011, over 10 million two way trucking
movements across the border
O 57% of the value of Canada’s trade with the
United States was exchanged using trucking
in 2011
Trucking Overview
Total Canada - U.S. Trade By Mode
(% share of Annual Value Total)
70
60
Percentage Share
50
2008
40
2009
2010
30
2011
20
10
0
Road
Rail
Marine
Air
Other
Import and Export values between USA and
Canada By Road
$300,000.00
$250,000.00
Value
$200,000.00
Exports
$150,000.00
Imports
$100,000.00
$50,000.00
$0.00
2010
2011
2012
Analyzing truck movement is
important
Movement of goods continue to increase
O Safe movement of freight through the
environment
O Ensure reliable transport environments by
maintaining infrastructure and reducing
bottlenecks
O Investment planning
O
To minimize impact of disasters like this….
Movement Analysis
Not new, and used to
O Identify key trucking corridors (Figliozzi et al., 2011)
O Evaluate truck transit times between locations
O
O
O
O
O
(McCormack, 2010)
Assess the feasibility of a statewide truck monitoring
program (McCormack, 2010)
Predict wait-times at border-crossings in USA-Canada
(Khan, 2010)
Analyze changes in cross-border trade movement
between USA and Canada (Leore et al., 2003)
Real-time planning of truck movement (Khan, 2010)
Determine infrastructure investment needs (Transport
Canada, 2011)
Movement
Analysis
O Methods
Source: Guo et al 2012
O Determine origin & destination of trip
O Geofencing
O Time-spent at a location
O Determine purpose of trip
O Analyzing stop-time at a location
O Determining the routing of the trip
O Analyzing truck volumes on highways
O Identify problem routes (e.g. travel is slowed due to
congestion/ poor infrastructure)
Fluidity/Reliability of Movement
To evaluate and identify factors that can affect trade movement,
Transport Canada’s Gateways and Trade Corridors Initiative
(TCGTCI) have developed a fluidity indicator that evaluates how
trade corridors operate (Eisele et al., 2011).
Based on “Time-to-Market” for different modes of transportation (e.g.
marine, rail, roads and air) Transport Canada is able to determine
fluidity of transport throughout Canada.
A Fluidity Indicator is a quantitative value ranging from 0.1
(fluid/reliable) to 1.0 (not as reliable) that is used to
O Measure of performance of Canadian Gateways
used to market and promote Canada’s efficiency
O provide accountability and transparency in the supply chain
O
O Support policymaking, program development and decision making
Calculating Fluidity of Movement
To determine “time-to-market”:
Origin and Destination, Travel speed, Distance
Data
GPS Receivers on
Trucks
Third Party
Company
Transport Canada
SQL Server
Database
(Ottawa)
Transport Canada
Data Services
(Moncton)
Truck Movement in North
America
One day of GPS data
March 1, 2013
30,770 distinct trucks
2,965,989 GPS points
No known source or
destination
Continual stream of
information
Summary of Current Methodology for
determining movement between locations
O
O
O
O
O
Major Canadian cities geofenced based on Census
Metropolitan Area (CMA) boundary
CMA boundary table stored in SQL table
96 unique city pairs with time and distance thresholds
created and stored in SQL table
Algorithm queries the raw trucking GPS database and creates
trips based on whether or not a truck was in a city of interest
after being in a previous city of interest and then compares
this with the threshold time and distance
Output of the algorithm is two .csv tables: a summary trip
table with time and distance, and a table containing GPS
points for each trip
Determining movement by distance
and time using geofencing
Actual Movement:
Calgary
Regina
Winnipeg
Regina
Winnipeg
Algorithm Results:
Calgary
Resulting Tables:
Summary Table
Trip ID
1
2
3
Origin
Calgary
Regina
Calgary
Destination Time(minutes) Distance(km)
Regina
480
802
Winnipeg
360
575
Winnipeg
840
1377
Trip Detail Table
Trip ID
1
1
1
Latitude
50.454722
50.45666
50.47777
Longitude
-104.606667
-104.6088
-104.6111
Date
20130215
20130215
20130215
Time
144038
144138
144238
Limitation of Current Methodology
• Route taken by truck can be a variety of possible routes
• Single trip will be broken into multiple trips as the truck passes through a geofenced
area resulting in double counting
• Origin and destination are determined by geofenced area therefore areas outside of this
area will be incorrectly classified and not captured
Objective
The purpose of this study is to minimize
misclassification of trips and improve upon the
identification of source and destinations locations.
allow for improved routing analysis and
O estimates of “time-to-market” between locations
O so that it can be used with the fluidity indicator to
obtain better assessments of reliability across the
transport network (i.e. better identify problem routes
and areas in need of investments)
O
Study – Data
Due to large volume of GPS
data collected, data for 1
month (N=35 million) will be
used while refining and
developing methods
Study area will include crossborder movement (e.g.
Emerson)
• 3 trucks March 1-7
• No defined Origins or
Destinations
Study – Understanding the data and
trucking movement
Frequency of GPS points
captured (this is variable)
Daily Movement
-Does this vary by route
-Is movement mainly during
daylight hours
-Is movement mainly during
weekdays
-Number of stops and length of
stops taken.
Study – Determining Source and
Destination
Improving identification of source and destination
O Several methods used different stop times (3
minutes to 10 minutes)
Distances travelled
O What distances are travelled associated with
each trip?
Routing Analysis
O
What are the key routes used?
O
Density analysis of GPS routes
Study – Determining border-wait
times
O border wait times are calculated by geofencing
O known border cue areas were geofenced
O dwell time is calculated by subtracting the time
of the first point out of the fence from the point
before entering the fence (Tardif, 2009)
Integration of methods to analyze routes
Geofence to
isolate trucks that
cross the border &
calculate border
dwell time
Analyze routes driven
using a density
calculation
Join isolated
Truck IDs to
Database and
pull their GPS
points 72 hours
before and after
crossing
Validate Origin and
Destination
Remove duplicates,
format the date & time
and calculate the time in
between each GPS point
per truck
Flag the Origin and
Destination in the
database using defined
stop time length
Anticipated Project Outcome
O
Determination of Origin and destination
Improve “time-to-market” inputs used in the Fluidity
Indicator
O Comprehensive assessment and validation of methods
applicable for determining origin and destination
O
O
Automated methods
O
O
O
Efficient analysis of trucking movement
Ability to include new locations without being restricted to
96 paired locations
Trucking movement analysis:
Improved understanding of origins and destinations of
cross-border truck movement
O Identification of key routes taken by trucks both in Canada
and the USA
O Identification of problem areas along a route
O
Project Timeline
November 2013: isolate and clean March 2013 data for the
Emerson crossing. Identify trip origins and destinations,
distances and transit & dwell times.
December 2013: Validate origins and destinations. Perform
Density analysis of routes.
January 2014: Test the process on a larger crossing. Develop
automated processes for trip calculations and analyses
March 2014: Finalize project and write up
Selected References
Andrienko, G., Andrienko, N., Bak, P., Keim, D., & Wrobel, S. (2013). Visual Analytics of Movement. Berlin, Heidelberg:
Springer Berlin Heidelberg. doi:10.1007/978-3-642-37583-5
Axhausen, K. W., Schönfelder, S., Wolf, J., Oliveira, M., & Samaga, U. (2003). Eighty Weeks of GPS Traces : Approaches to
Enriching Trip Information Submitted to the 83 rd Transportation Research Board Meeting Updated November 2003.
Eisele, Wi., Tardif, L.-P., Villa, J. C., Schrank, D. L., & Lomax, T. (2011). Evaluating Global Freight Corridor Performance for
Canada. Journal of Transportation of the Institute of Transportation Engineers, I(I), 39–58.
Figliozzi, M. A., Wheeler, N., Albright, E., Walker, L., Sarkar, S., & Rice, D. (2011). Algorithms for Studying the Impact of Travel
Time Reliability Along Multisegment Trucking Freight Corridors. Transportation Research Record, 2224, 26–34.
doi:10.3141/2224-04
Guo, D., Zhu, X., Jin, H., Gao, P., & Andris, C. (2012). Discovering Spatial Patterns in Origin-Destination Mobility Data.
Transactions in GIS, 16(3), 411–429. doi:10.1111/j.1467-9671.2012.01344.x
Rinzivillo, S., Pedreschi, D., Nanni, M., Giannotti, F., Andrienko, N., & Andrienko, G. (2008). Visually driven analysis of
movement data by progressive clustering. Information Visualization, 7(3-4), 225–239. doi:10.1057/palgrave.ivs.9500183
Schuessler, N., & Axhausen, K. W. (2008). Processing Raw Data from Global Positioning Systems Without Additional
Information. Transportation Research Record: Journal of the Transportation Research Board, 2105, 28–36.
doi:10.3141/2105-04
Tardif, L.-P. (2009). Application of Freight Flow Measurements. Vancouver: TRB/OECD Workshop. Retrieved from
http://www.internationaltransportforum.org/Proceedings/reliability/P-Tardiff.pdf
Transport Canada. (2011). Transportation in Canada 2011 (p. 149). Ottawa.
Acknowledgements
Justine Blanford
Louis-Paul Tardif
Andrew Carter
Alexander Gregory