INTERNATIONAL SYMPOSIUM FOR
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Data-Driven Forecasts
of 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)
SID aims at providing an integrated view of regional
infrastructure development
SID p...
Data flow in SID
Data-driven capability in infrastructure
services
supports decision making in:
region’s liveability and sustainable develo...
Data-driven forecasts in SID
Study area: the Illawarra region in NSW, Australia1

ABS, Australian Standard Geographical

A...
Data-driven forecasts in SID
Data:
electricity consumptions
water consumptions
regional temperature and
rainfall measures
...
Residential electricity consumption (REC)
Why do we need to model REC
REC is a significant indicator of infrastructure serv...
REC modelling
A Complex Fuzzy Set is able to model
Uncertainty in REC
Periodicity in REC
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 p...
REC modelling work flow

Step

Main Tasks

1

Specify Spatial and Temporary Scales

2

Identify CFSs for utility and other ...
REC modelling result

REC for postcode 2500

REC for postcode 2533
Travel mode choice modelling

Traffic congestion is an
important issue of big
cities.
Sydney is with congestion
level 33%.
(...
Travel mode choice modelling
What data is used for travel mode
choice:
Sydney Household Travel Survey
conducted by Bureau ...
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

8...
Performance
Experiment

Empirical Settings

PCI (%)

Fuzzy sets

Dependent trip

DT

ANN

1

N

N

64.71

68.1

2

Y

N

6...
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....
Summary
Data-driven forecast techniques and methods are important
for analysing the capability of infrastructure services....
SMART International Symposium for Next Generation Infrastructure: Data-driven forecasts of regional demand for infrastruct...
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SMART International Symposium for Next Generation Infrastructure: Data-driven forecasts of regional demand for infrastructure services

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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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SMART International Symposium for Next Generation Infrastructure: Data-driven forecasts of regional demand for infrastructure services

  1. 1. INTERNATIONAL SYMPOSIUM FOR EF=@G-HFI!BAG?FEG-! !"#"$%&'()*+,-&)."/#/+-,+ &)0'-*"1+%)2"*%+,-&+ '*,&"/#&3.#3&)+/)&('.)/++ ?,.!J#;;#C9$>!&2.!<#$K20.%!<#$+2917+#28!+#!+,.!1789$.88!&$%!/#;9<'!%9&;#>7.!9$!-'%$.'5! •! ! •! ! G9<L!-&C.28!MF&N#$&;!A78+2&;9&!6&$LO! F9<L!I2.9$.2!MP,&920&$!MH$J2&8+27<+72.!F-QO "#$%&'(!)*+,!-./+.01.2!3*4)5!6789$.88!:!/#;9<'!=9&;#>7.! ?7.8%&'! 4! @<+#1.2! +#! ?,728%&'(! =9&;#>7.! )2%! @<+#1.25! A<&%.09<! &$%! B#;9<'! !B2.8.$+.%!1'5!=2!R7$!"&(!-"AG?!H$J2&8+27<+72.!S&<9;9+'(!T$9U.289+'!#J! Q#;;#$>#$>! CCCD98$>9D#2>! CCCD98$>9D#2>!
  2. 2. Data-Driven Forecasts of Regional Demand for Infrastructure Services
  3. 3. Outline Problem Challenges Case study: residential electricity Case study: travel mode choice Summary
  4. 4. SMART Infrastructure Dashboard (SID)
  5. 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. 6. Data flow in SID
  7. 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. 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. 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. 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. 11. REC modelling A Complex Fuzzy Set is able to model Uncertainty in REC Periodicity in REC
  12. 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. 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. 14. REC modelling result REC for postcode 2500 REC for postcode 2533
  15. 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. 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. 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. 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. 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. 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

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