A presentation conducted by Dr Jun Ma, SMART Infrastructure Facility, University of Wollongong. Presented on Tuesday the 1st of October 2013. A region’s socio-economic development and liveability are affected to a great extent by the region’s infrastructure services. Data-driven forecasting the demands for infrastructure utilities (electricity, water, waste, etc) of a region becomes a challenging issue in the situation of highly integrative infrastructure networks and restricted data sharing, which involves handling temporary and spatial infrastructure utility data simultaneously and modelling the correlations between different infrastructure utilities and their interactions with relevant socio-economic and environmental indicators. Data mining and complex fuzzy set techniques are used to implement this kind of analytically capability in SMART Infrastructure Dashboard. The developed method and technique can be used for better governance, planning and delivering of effective and efficient infrastructure service and facility. It can also provide support evidence for a region’s long-term sustainable planning and development.

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2. Data-Driven Forecasts
of Regional Demand
for Infrastructure Services

3. Outline
Problem
Challenges
Case study: residential electricity
Case study: travel mode choice
Summary

4. SMART Infrastructure Dashboard (SID)

5. SMART Infrastructure Dashboard (SID)
SID aims at providing an integrated view of regional
infrastructure development
SID provides
An information platform of regional infrastructure
services
easy, transparent and intuitive access to infrastructure data
from public agencies, private operators, researchers, etc
easy, transparent and intuitive observe correlations between
infrastructure services and demography, economy,
environment factors, etc
A cross-service analysis platform
infrastructure services
insights into spatial, technical, social and economic issues

6. Data flow in SID

7. Data-driven capability in infrastructure
services
supports decision making in:
region’s liveability and sustainable development
socio-economic development and environment protection
urban planning, land use
challenges faced:
data may be of different types, forms and of varying quality
appropriate system requirements for data processing, storing,
accessing, and re-using
modelling techniques/methods for analysing data
visualising the data

8. Data-driven forecasts in SID
Study area: the Illawarra region in NSW, Australia1
ABS, Australian Standard Geographical
ABS, Australian Statistical Geography
Classification (ASGC), 2006
Standard (ASGS), 2011
1
source: Australian Bureau of Statistics (ABS), www.abs.gov.au

9. Data-driven forecasts in SID
Data:
electricity consumptions
water consumptions
regional temperature and
rainfall measures
regional demographic profiles
community travel surveys and
statistics
...
Data granular:
spatial: following the ABS
geographic classifications
temporal: ranges from daily to
5-yearly

10. Residential electricity consumption (REC)
Why do we need to model REC
REC is a significant indicator of infrastructure service
REC is affected by social, economical, and environmental
factors
What data is used for modelling REC
Utility data: residential electricity consumption
Demography data: population, dwelling number based on
structure, household income
Environmental data: rainfall, temperature
How do we model REC
A Complex Fuzzy Set based method

11. REC modelling
A Complex Fuzzy Set is able to model
Uncertainty in REC
Periodicity in REC

12. REC modelling
A Complex Fuzzy Set (CFS) is defined as
µ(x) = r(x) · ejω(x) ,
where x ∈ X, r(x) ∈ [0, 1], j ∈ C and ω(x) a periodic function.
1.20
1.00
0.80
0.60
0.40
0.20
0.00
1
2
3
4
5
6
Moving window (POA1)
7
8
9
10
Moving Window (POA2)
11
12
13
14
Fixed Window (POA1)
15
16
17
18
Fixed Window (POA 2)
19
20

13. REC modelling work flow
Step
Main Tasks
1
Specify Spatial and Temporary Scales
2
Identify CFSs for utility and other factors on regulated data
3
Convert and represent sourced data in CFS forms
4
Analyse and extract correlation pattern among converted data
5
Validate REC modelling in real situations/scenarios

14. REC modelling result
REC for postcode 2500
REC for postcode 2533

15. Travel mode choice modelling
Traffic congestion is an
important issue of big
cities.
Sydney is with congestion
level 33%.
(source: www.tomtom.com)
(Sydney’s traffic congestion, source: www.abc.net.au)

16. Travel mode choice modelling
What data is used for travel mode
choice:
Sydney Household Travel Survey
conducted by Bureau of Transport
Statistics (BTS), Transport for New
South Wales (TfNSW)
Data processing:
individual vs. household
fuzzification of “income” and
“travel time”
(source: www.bts.nsw.gov.au)
Methods: ANN + Decision tree

17. Fuzzy sets of “income” and “travel time”
1.20
1.10
1.00
1
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
0
582.97
888.84
1125.39
1318.51
cummulative decils
high income
1506.82
1686.12
1850.36
2028.44
2176.57
2351.17
Log fit of cummulative decils
2505.35
2676.02
lower income
2852.99
3031.03
3266.58
3512.09
3833.87
middle income
4217.96
4856.20

18. Performance
Experiment
Empirical Settings
PCI (%)
Fuzzy sets
Dependent trip
DT
ANN
1
N
N
64.71
68.1
2
Y
N
67.67
68.7
3
N
Y
85.63
85.9
4
Y
Y
86.17
86.8

19. Mode distribution
Travel Modes
HTS data
DT
ANN
Car Driver
40.95
43.50
43.11
Car Passenger
20.65
30.76
19.05
8.37
7.54
7.74
29.26
17.68
29.55
0.77
0.53
0.53
Public Transport
Walk
Bicycle

20. Summary
Data-driven forecast techniques and methods are important
for analysing the capability of infrastructure services.
They are often presented with challenges from the data
itself – in the form of processing, analysis and modelling, and
visualisation.
They can be used for building an integrated view of
infrastructure service for use in governance, planning and
the design of infrastructure services and facilities.
They can support decision making in infrastructure
services.
What we need to do: COLLABORATION
data
techniques
platforms