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INTERNATIONAL SYMPOSIUM FOR
Data-Driven Forecastsof Regional Demand
for Infrastructure Services
Outline
Problem
Challenges
Case study: residential electricity
Case study: travel mode choice
Summary
SMART Infrastructure Dashboard (SID)
SMART Infrastructure Dashboard (SID)I SID aims at providing an integrated view of regional
infrastructure developmentI SID provides
I An information platform of regional infrastructureservices
I easy, transparent and intuitive access to infrastructure datafrom public agencies, private operators, researchers, etc
I easy, transparent and intuitive observe correlations betweeninfrastructure services and demography, economy,environment factors, etc
I A cross-service analysis platformI infrastructure servicesI insights into spatial, technical, social and economic issues
Data flow in SID
Data-driven capability in infrastructureservices
I supports decision making in:I region’s liveability and sustainable developmentI socio-economic development and environment protectionI urban planning, land use
I challenges faced:I data may be of different types, forms and of varying qualityI appropriate system requirements for data processing, storing,
accessing, and re-usingI modelling techniques/methods for analysing dataI visualising the data
Data-driven forecasts in SIDI Study area: the Illawarra region in NSW, Australia1
ABS, Australian Standard GeographicalClassification (ASGC), 2006
ABS, Australian Statistical GeographyStandard (ASGS), 2011
1source: Australian Bureau of Statistics (ABS), www.abs.gov.au
Data-driven forecasts in SIDI Data:
I electricity consumptionsI water consumptionsI regional temperature and
rainfall measuresI regional demographic profilesI community travel surveys and
statisticsI ...
I Data granular:I spatial: following the ABS
geographic classificationsI temporal: ranges from daily to
5-yearly
Residential electricity consumption (REC)I Why do we need to model REC
I REC is a significant indicator of infrastructure serviceI REC is affected by social, economical, and environmental
factorsI What data is used for modelling REC
I Utility data: residential electricity consumptionI Demography data: population, dwelling number based on
structure, household incomeI Environmental data: rainfall, temperature
I How do we model RECI A Complex Fuzzy Set based method
REC modellingI A Complex Fuzzy Set is able to model
I Uncertainty in RECI Periodicity in REC
REC modellingA 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 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 200.00
0.20
0.40
0.60
0.80
1.00
1.20
Moving window (POA1) Moving Window (POA2) Fixed Window (POA1) Fixed Window (POA 2)
REC modelling work flow
Step Main Tasks
1 Specify Spatial and Temporary Scales2 Identify CFSs for utility and other factors on regulated data3 Convert and represent sourced data in CFS forms4 Analyse and extract correlation pattern among converted data5 Validate REC modelling in real situations/scenarios
REC modelling result
REC for postcode 2500 REC for postcode 2533
Travel mode choice modelling
I Traffic congestion is animportant issue of bigcities.
I Sydney is with congestionlevel 33%.(source: www.tomtom.com)
(Sydney’s traffic congestion, source: www.abc.net.au)
Travel mode choice modelling
(source: www.bts.nsw.gov.au)
I What data is used for travel modechoice:Sydney Household Travel Surveyconducted by Bureau of TransportStatistics (BTS), Transport for NewSouth Wales (TfNSW)
I Data processing:I individual vs. householdI fuzzification of “income” and
“travel time”I Methods: ANN + Decision tree
Fuzzy sets of “income” and “travel time”
582.97 888.84 1125.39 1318.51 1506.82 1686.12 1850.36 2028.44 2176.57 2351.17 2505.35 2676.02 2852.99 3031.03 3266.58 3512.09 3833.87 4217.96 4856.200.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
1.10
1.20
0
1
cummulative decils Log fit of cummulative decils lower income middle incomehigh income
Performance
Experiment Empirical Settings PCI (%)
Fuzzy sets Dependent trip DT ANN1 N N 64.71 68.12 Y N 67.67 68.73 N Y 85.63 85.94 Y Y 86.17 86.8
Mode distribution
Travel Modes HTS data DT ANN
Car Driver 40.95 43.50 43.11Car Passenger 20.65 30.76 19.05Public Transport 8.37 7.54 7.74Walk 29.26 17.68 29.55Bicycle 0.77 0.53 0.53
SummaryI Data-driven forecast techniques and methods are important
for analysing the capability of infrastructure services.I They are often presented with challenges from the data
itself – in the form of processing, analysis and modelling, andvisualisation.
I They can be used for building an integrated view ofinfrastructure service for use in governance, planning andthe design of infrastructure services and facilities.
I They can support decision making in infrastructureservices.
I What we need to do: COLLABORATIONI dataI techniquesI platforms
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