GIS Analysis of Commercial Trucking

Movements from a Canadian Perspective

GEOG 596A Peer Review

Kristina Kwiatkowski Advisor: Justine Blanford

Presentation OutlineO Background InformationO Movement AnalysisO DataO Currently MethodologyO ObjectiveO MethodologyO Anticipated Project OutcomeO Project Timeline

Canada - Trucking Overview

Source: US Dept. of Transportation

Source: Transport Canada

Canadian-American Border

O over 8000km in lengthO 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

Road Rail Marine Air Other0

10

20

30

40

50

60

70

2008200920102011

Perc

enta

ge

Share

Total Canada - U.S. Trade By Mode (% share of Annual Value Total)

Import and Export values between USA and Canada By Road

2010 2011 2012$0.00

$50,000.00

$100,000.00

$150,000.00

$200,000.00

$250,000.00

$300,000.00

ExportsImports

Valu

e

Analyzing truck movement is important

O 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

To minimize impact of disasters like this….

Movement AnalysisNot new, and used to

O Identify key trucking corridors (Figliozzi et al., 2011)

O Evaluate truck transit times between locations (McCormack, 2010)

O Assess the feasibility of a statewide truck monitoring program (McCormack, 2010)

O Predict wait-times at border-crossings in USA-Canada (Khan, 2010)

O Analyze changes in cross-border trade movement between USA and Canada (Leore et al., 2003)

O Real-time planning of truck movement (Khan, 2010)

O Determine infrastructure investment needs (Transport Canada, 2011)

Movement Analysis

O MethodsO Determine origin & destination of trip

O Geofencing O Time-spent at a location

O Determine purpose of tripO Analyzing stop-time at a location

O Determining the routing of the tripO Analyzing truck volumes on highways O Identify problem routes (e.g. travel is slowed due

to congestion/ poor infrastructure)

Source: Guo et al 2012

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 toO Measure of performance of Canadian Gateways

O used to market and promote Canada’s efficiencyO provide accountability and transparency in the supply chain

O Support policymaking, program development and decision making

Calculating Fluidity of Movement

To determine “time-to-market”:Origin and Destination, Travel speed, Distance

GPS Receivers on Trucks

Third Party Company

Transport Canada Data

Services (Moncton)

Transport Canada SQL

Server Database (Ottawa)

Data

Truck Movement in North America

March 1, 201330,770 distinct trucks2,965,989 GPS points

One day of GPS data

No known source or destination

Continual stream of information

Summary of Current Methodology for determining movement between

locationsO Major Canadian cities geofenced based on Census

Metropolitan Area (CMA) boundaryO CMA boundary table stored in SQL tableO 96 unique city pairs with time and distance

thresholds created and stored in SQL tableO 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

O 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

Calgary Regina

Winnipeg

Calgary Regina

Winnipeg

Actual Movement:

Algorithm Results:

Trip ID Origin Destination Time(minutes) Distance(km)1 Calgary Regina 480 8022 Regina Winnipeg 360 5753 Calgary Winnipeg 840 1377

Trip ID Latitude Longitude Date Time1 50.454722 -104.606667 20130215 1440381 50.45666 -104.6088 20130215 1441381 50.47777 -104.6111 20130215 144238

Resulting Tables:Summary Table Trip Detail Table

Regina

Winnipeg

Calgary Saskatoon

• 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

Limitation of Current Methodology

ObjectiveThe purpose of this study is to minimize misclassification of trips and improve upon the identification of source and destinations locations.

O allow for improved routing analysis and O estimates of “time-to-market” between

locationsO 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)

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 cross-border 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 destinationO Several methods used different stop

times (3 minutes to 10 minutes)Distances travelledO What distances are travelled associated

with each trip?Routing AnalysisO 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 geofencedO 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

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

Flag the Origin and Destination in the database using defined stop time length

Validate Origin and Destination

Analyze routes driven using a density calculation

Anticipated Project Outcome

O Determination of Origin and destinationO Improve “time-to-market” inputs used in the Fluidity

IndicatorO Comprehensive assessment and validation of

methods applicable for determining origin and destination

O Automated methodsO Efficient analysis of trucking movement O Ability to include new locations without being

restricted to 96 paired locationsO Trucking movement analysis:

O 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

Project TimelineNovember 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 ReferencesAndrienko, 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 BlanfordLouis-Paul TardifAndrew Carter

Alexander Gregory