1. The University of Sydney Page 1
Combining spatial data:
benefits and applications
Modelling Workshop, AITPM 2016
Sydney, Australia
Dr Adrian B. Ellison
Dr Richard B. Ellison
2. The University of Sydney Page 2
Background
› Increasingly large sources of transport and land use data
› Biggest benefit comes from combining several sources
› For modelling, reduces reliance on assumptions
3. The University of Sydney Page 3
Background
› Existing statistical methods are not well suited
› Behavioural data (from GPS, smartcards, etc.) can be ‘noisy’
› Need to isolate the impact of individuals’ behaviour from external factors
7. The University of Sydney Page 8
From driver behaviour to public transport user
behaviour
8. The University of Sydney Page 9
From driver behaviour to public transport user
behaviour
9. The University of Sydney Page 10
Application in transport models – MetroScan-TI
10. The University of Sydney Page 11
Application in transport models – MetroScan-TI
Predictive
Models
Stated
Preference
Surveys
Revealed
Preference
Surveys
Traffic/pedestrian
counts
Smartphone
and GPS traces
PT Operations,
smartcard data
Census, land-use
and GPS data
11. The University of Sydney Page 12
Application in transport models – MetroScan-TI
12. The University of Sydney Page 13
Application in transport models – MetroScan-TI
13. The University of Sydney Page 14
Software
› Solution uses free and open source software
R Project (statistical software) Quantum GIS
PostgreSQL with PostGIS
(database)
OSRM (routing)
14. The University of Sydney Page 15
Software
› Open standards to make combining datasets easier and more robust
› Benefits from the largest combination of functionality
› Packaged deployment possible with distributed and high performance
computing
15. The University of Sydney Page 16
Combining spatial data:
benefits and applications
Modelling Workshop, AITPM 2016
Sydney, Australia
Dr Adrian B. Ellison
Adrian.Ellison@Sydney.edu.au
Dr Richard B. Ellison
Richard.Ellison@Sydney.edu.au
