GIS Analysis of Commercial Trucking Movements from a Canadian Perspective. GEOG 596A Peer Review Kristina Kwiatkowski Advisor : Justine Blanford. Presentation Outline. Background Information Movement Analysis Data Currently Methodology Objective Methodology
Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.
GIS Analysis of Commercial Trucking Movements from a Canadian Perspective
GEOG 596A Peer Review
Kristina Kwiatkowski Advisor: Justine Blanford
Source: US Dept. of Transportation
Source: Transport Canada
Total Canada - U.S. Trade By Mode
(% share of Annual Value Total)
To minimize impact of disasters like this….
Not new, and used to
Source: Guo et al 2012
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
To determine “time-to-market”:
Origin and Destination, Travel speed, Distance
One day of GPS data
No known source or destination
Continual stream of information
Trip Detail Table
The purpose of this study is to minimize misclassification of trips and improve upon the identification of source and destinations locations.
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 cross-border movement (e.g. Emerson)
Frequency of GPS points captured (this is variable)
Improving identification of source and destination
Geofence to isolate trucks that cross the border & calculate border dwell time
Join isolated Truck IDs to Database and pull their GPS points 72 hours before and after crossing
Remove duplicates, format the date & time and calculate the time in between each GPS point per truck
Validate Origin and Destination
Flag the Origin and Destination in the database using defined stop time length
Analyze routes driven using a density calculation
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
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.