1001 Smart Cities

Permanent project revision

In-Ground On-Street Parking Sensor Network

Revision 2026-11-04-evidence-baseline · effective 2026-11-03

Current revision. This is the snapshot used by the live entity page.

unreviewed

Created the first structured Evidence Passport with 10 claims and 6 source dossiers.

sha256:d6a603b764375c41d8f0817c49cb393271bbee03f0e5fc854967bb1080176b27
All revisions → Current entity page →

Change set

  • add /evidencePassport — Added claim-level evidence mapping and source dossiers.
  • add /revisionHistory — Established a permanent, hash-addressed baseline revision.
10
of 10 claims mapped
6
source dossiers
4
publication dates recorded
0
archives linked
0
exact locators
0
content captures hashed

Claims, one by one

The text below is copied verbatim from the published record. Each claim names the source dossiers that carry it; caveats stay attached to the claim they qualify.

Claim 1 clm-89b054dcfa1d539f quantitative
The City of Melbourne began burying in-ground sensors under CBD, Southbank and inner-city parking bays in 2011
Published field
snippet · verbatim
Mapping
mapped · no interpretation or interpolation added by this file
What the mapping says
The April 2011 report says councillors approved in-ground sensors from 1 July across the CBD, Southbank and West Melbourne; the 2015 article confirms the council has used the sensors since 2011.
Caveat
No claim-specific caveat recorded

Sources carrying this claim

  1. Parking.net — City of Melbourne to install new parking technology to catch drivers (20 April 2011)
  2. IoT Hub — Smart councils sense parking spaces (14 Oct 2015)
Claim 2 clm-fca159d9f7369a9c quantitative
had about 5,300 in place by 2015
Published field
snippet · verbatim
Mapping
mapped · no interpretation or interpolation added by this file
What the mapping says
In October 2015 a council account put the deployment at 5,300 sensors across the central CBD, Southbank and parts of East and West Melbourne.
Caveat
No claim-specific caveat recorded

Sources carrying this claim

  1. IoT Hub — Smart councils sense parking spaces (14 Oct 2015)
Claim 3 clm-a8e24cc7567701fa descriptive
publishing real-time bay occupancy as open data
Published field
snippet · verbatim
Mapping
mapped · no interpretation or interpolation added by this file
What the mapping says
The city's open data portal publishes a live dataset from its in-ground bay sensors; the state data directory also holds the archived sensor records for 2011, the year the network started.
Caveat
No claim-specific caveat recorded

Sources carrying this claim

  1. City of Melbourne Open Data Portal — On-street Parking Bay Sensors
  2. Victorian Government Data Directory — On-street Car Parking Sensor Data 2011
Claim 4 clm-016105c419868289 quantitative
in November 2026 the feed was still live on the city's open data portal
Published field
snippet · verbatim
Mapping
mapped · no interpretation or interpolation added by this file
What the mapping says
The portal's in-ground bay-sensor dataset was still being refreshed in November 2026; the dataset page states no date of its own, so the currency comes from the live records rather than from a quotable sentence.
Caveat
No claim-specific caveat recorded

Sources carrying this claim

  1. City of Melbourne Open Data Portal — On-street Parking Bay Sensors
Claim 5 clm-cd2b769d4f618d3c descriptive
third-party apps such as ParkThere build on it
Published field
snippet · verbatim
Mapping
mapped · no interpretation or interpolation added by this file
What the mapping says
The ParkThere listing names the City of Melbourne's on-street parking bay sensor dataset as one of its data sources and describes itself as an independent third-party app.
Caveat
No claim-specific caveat recorded

Sources carrying this claim

  1. ParkThere app listing (Google Play)
Claim 6 clm-972d7c43bcc0115b quantitative
the technology was expected to catch an extra 150 cars a day
Published field
impact · verbatim
Mapping
mapped · no interpretation or interpolation added by this file
What the mapping says
The April 2011 report on the council vote states the expectation of 150 additional cars caught per day.
Caveat
The report, which relays a Herald Sun article, does not say who made the forecast.

Sources carrying this claim

  1. Parking.net — City of Melbourne to install new parking technology to catch drivers (20 April 2011)
Claim 7 clm-d7ae5b62e52df1a7 descriptive
a council spokesperson reported better compliance with signed time limits, fewer fines issued and a more regular turnover of central-city bays
Published field
impact · verbatim
Mapping
mapped · no interpretation or interpolation added by this file
What the mapping says
The 2015 article quotes a council spokesperson describing these three effects since the sensors were introduced.
Caveat
These are the council's own statements; no figures or method were given.

Sources carrying this claim

  1. IoT Hub — Smart councils sense parking spaces (14 Oct 2015)
Claim 8 clm-910ed6a9178311f3 descriptive
the council's own account, not an independently audited evaluation
Published field
impact · verbatim
Mapping
mapped · no interpretation or interpolation added by this file
What the mapping says
The only source for the outcome is a quote from a council spokesperson in a trade-press feature.
Caveat
No claim-specific caveat recorded

Sources carrying this claim

  1. IoT Hub — Smart councils sense parking spaces (14 Oct 2015)
Claim 9 clm-feb48dd16cd05bfa descriptive
A sensor network approved to catch more overstaying cars
Published field
lesson · verbatim
Mapping
mapped · no interpretation or interpolation added by this file
What the mapping says
The approval report describes the sensors as designed to alert parking officers when a vehicle overstays.
Caveat
No claim-specific caveat recorded

Sources carrying this claim

  1. Parking.net — City of Melbourne to install new parking technology to catch drivers (20 April 2011)
Claim 10 clm-0b31100801368fe5 quantitative
more than a decade after the 2011 rollout, the bay data still runs on the city's open data portal and in third-party apps
Published field
lesson · verbatim
Mapping
mapped · no interpretation or interpolation added by this file
What the mapping says
The portal still publishes the in-ground sensor feed, and a third-party app lists that dataset as its data source.
Caveat
No claim-specific caveat recorded

Sources carrying this claim

  1. City of Melbourne Open Data Portal — On-street Parking Bay Sensors
  2. ParkThere app listing (Google Play)

Source dossiers and revision state

This layer records what is known about each source, what is still missing, and the exact snapshot this page is rendering. A missing date, archive or hash stays visible as missing.

unreviewed Revision 2026-11-04-evidence-baseline effective 2026-11-03 · created 2026-11-04

Awaiting operator and domain-expert review; publication of this revision implies neither.

sha256:d6a603b764375c41d8f0817c49cb393271bbee03f0e5fc854967bb1080176b27
Revision history → Permanent version link → Machine-readable record → Evidence Constitution →
10
of 10 claims mapped
6
source dossiers
4
publication dates recorded
0
archives linked
0
exact locators
0
content captures hashed

Governance facts carried by the record

Operator
City of Melbourne
Actor types
city-government
Funding model
public
Funding description
Not recorded
Cost
Not recorded
Ownership
Not recorded
Decision rights
Not recorded
Exit conditions
Not recorded

6 source dossiers

City of Melbourne Open Data Portal — On-street Parking Bay Sensors data.melbourne.vic.gov.au · 3 linked claims

Open original source ↗

Stable source ID
src-b761e8ab92aad21c
Publisher
Name not classified · data.melbourne.vic.gov.au
Type / language
web-page · language not classified
Published
Not recorded
Updated
Not recorded
Source last checked
No source-level check date
Record last verified
2026-11-03
Dossier created
2026-11-04
Retrieved copy
No local capture recorded
Archive
No archived copy linked
Content hash
No captured content hash
Locator
Open the recorded source note

operator dataset, confirms current sensor count and real-time updates

Descriptive only — not page/paragraph exact
Claims linked
IoT Hub — Smart councils sense parking spaces (14 Oct 2015) iothub.com.au · 4 linked claims

Open original source ↗

Stable source ID
src-b24470e225b17c4f
Publisher
Name not classified · iothub.com.au
Type / language
web-page · language not classified
Published
2015-10-14
Updated
Not recorded
Source last checked
No source-level check date
Record last verified
2026-11-03
Dossier created
2026-11-04
Retrieved copy
No local capture recorded
Archive
No archived copy linked
Content hash
No captured content hash
Locator
Open the recorded source note

2011-era trade press, sensor count and council self-reported outcomes

Descriptive only — not page/paragraph exact
Claims linked
Victorian Government Data Directory — On-street Car Parking Sensor Data 2011 discover.data.vic.gov.au · 1 linked claim

Open original source ↗

Stable source ID
src-c7ce5a17201946f7
Publisher
Name not classified · discover.data.vic.gov.au
Type / language
web-page · language not classified
Published
2023-03-07
Updated
2024-06-13
Source last checked
No source-level check date
Record last verified
2026-11-03
Dossier created
2026-11-04
Retrieved copy
No local capture recorded
Archive
No archived copy linked
Content hash
No captured content hash
Locator
Open the recorded source note

confirms 2011 launch year via earliest published dataset

Descriptive only — not page/paragraph exact
Claims linked
Springer/Transportation — Melbourne's parking dynamics and traffic flow dataset paper link.springer.com · background

Open original source ↗

Stable source ID
src-f81e3fbb9adad428
Publisher
Name not classified · link.springer.com
Type / language
web-page · language not classified
Published
2025-11-28
Updated
Not recorded
Source last checked
No source-level check date
Record last verified
2026-11-03
Dossier created
2026-11-04
Retrieved copy
No local capture recorded
Archive
No archived copy linked
Content hash
No captured content hash
Locator
Open the recorded source note

independent peer-reviewed use of the sensor dataset, confirms scale

Descriptive only — not page/paragraph exact
Claims linked
Background source; no baseline claim points here
ParkThere app listing (Google Play) play.google.com · 2 linked claims

Open original source ↗

Stable source ID
src-fc23a60dcce97ec4
Publisher
Name not classified · play.google.com
Type / language
web-page · language not classified
Published
Not recorded
Updated
Not recorded
Source last checked
No source-level check date
Record last verified
2026-11-03
Dossier created
2026-11-04
Retrieved copy
No local capture recorded
Archive
No archived copy linked
Content hash
No captured content hash
Locator
Open the recorded source note

current third-party app consuming live sensor feed, evidence network is still active

Descriptive only — not page/paragraph exact
Claims linked
Parking.net — City of Melbourne to install new parking technology to catch drivers (20 April 2011) parking.net · 3 linked claims

Open original source ↗

Stable source ID
src-b97e8dfa67f19ad1
Publisher
Name not classified · parking.net
Type / language
web-page · language not classified
Published
2011-04-20
Updated
Not recorded
Source last checked
No source-level check date
Record last verified
2026-11-03
Dossier created
2026-11-04
Retrieved copy
No local capture recorded
Archive
No archived copy linked
Content hash
No captured content hash
Locator
Open the recorded source note

Report (relaying the Herald Sun) on the council vote approving in-ground sensors and number-plate recognition from 1 July 2011, at a cost of $5.48 million; the technology was expected to catch an extra 150 cars a day.

Descriptive only — not page/paragraph exact
Claims linked
Known limits of this file
  • Publisher names and source languages have not yet been classified; domains are recorded without guessing.
  • Existing source notes are descriptive locators, not page- or paragraph-exact citations.
  • A missing archive, publication date or content hash is shown as null rather than inferred.
  • The project-level evidence grade ranks the strongest source in the entry; it is not a truth score for every claim.