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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

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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