GIS Analysis of Commercial - PowerPoint Presentation

Slide1

GIS Analysis of Commercial Trucking Movements from a Canadian Perspective

GEOG 596A Peer Review

Kristina Kwiatkowski

Advisor

: Justine BlanfordSlide2

Presentation Outline Background Information

Movement Analysis

Data

Currently Methodology

Objective

Methodology

Anticipated Project Outcome

Project TimelineSlide3

Canada - Trucking Overview

Source: US Dept. of Transportation

Source: Transport CanadaSlide4

Canadian-American Border over 8000km in length

in 2011, over 10 million two way trucking movements across the border

57% of the value of Canada’s trade with the United States was exchanged using trucking in 2011Slide5

Trucking Overview

Percentage Share

Total Canada - U.S. Trade By Mode

(

% share of Annual Value Total)Slide6

Import and Export values between USA and Canada By Road

ValueSlide7

Analyzing truck movement is important

Movement of goods continue to increase

Safe

movement of freight through the

environment

Ensure reliable transport environments by maintaining infrastructure and reducing bottlenecks

Investment planningSlide8

To minimize impact of disasters like this….Slide9

Movement Analysis

Not new, and used to

Identify key trucking

corridors (

Figliozzi

et al., 2011

) Evaluate truck transit times between locations (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)Slide10

Movement Analysis

Methods

Determine origin & destination of trip

Geofencing

Time-spent at a location

Determine purpose of trip

Analyzing stop-time at a location Determining the routing of the

trip Analyzing truck volumes on highways Identify problem routes (e.g. travel is slowed due to congestion/ poor infrastructure)

Source:

Guo

et al 2012Slide11

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

Measure of performance of Canadian Gateways

used to market and promote Canada’s efficiency

provide accountability and transparency in the supply chain

Support policymaking, program development and decision making Slide12

Calculating Fluidity of Movement

To determine “time-to-market”:

Origin and Destination, Travel speed, Distance

DataSlide13

Truck Movement in North America

March 1, 2013

30,770 distinct trucks

2,965,989 GPS points

One day of GPS data

No known source or destination

Continual stream of informationSlide14

Summary of Current Methodology for determining movement between locations

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 tripSlide15

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

802

2

Regina

Winnipeg

360

575

3

Calgary

Winnipeg

840

1377

Trip

ID

Latitude

Longitude

Date

Time

1

50.454722

-104.606667

20130215

144038

1

50.45666

-104.6088

20130215

144138

1

50.47777

-104.6111

20130215

144238

Resulting Tables:

Summary Table

Trip Detail TableSlide16

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 MethodologySlide17

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

estimates of “time-to-market” between locations

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)Slide18

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 DestinationsSlide19

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.Slide20

Study – Determining Source and Destination

Improving identification of source and destination

Several methods used different stop times (3 minutes to 10 minutes)

Distances

travelled

What

distances are travelled associated with each trip?Routing Analysis

What are the key routes used? Density analysis of GPS routes Slide21

Study – Determining border-wait times

border wait times are calculated by geofencing

known border cue areas were geofenced

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)Slide22

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 calculationSlide23

Anticipated Project Outcome

Determination of Origin and destination

Improve “time-to-market” inputs used in the Fluidity Indicator

Comprehensive assessment and validation of methods applicable for determining origin and destination

Automated methods

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 Identification of key routes taken by trucks both in Canada and the USA Identification of problem areas along a routeSlide24

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 upSlide25

Selected References

Andrienko

, G.,

Andrienko

, N.,

Bak

, P., Keim, D., & Wrobel, S. (2013). Visual Analytics of Movement

. Berlin, Heidelberg: Springer Berlin Heidelberg. doi:10.1007/978-3-642-37583-5Axhausen, 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:10.3141/2224-04Guo, 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.Slide26

Acknowledgements

Justine Blanford

Louis-Paul Tardif

Andrew Carter

Alexander Gregory