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Paper Citation Record · LEDGER

Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas

As of 21 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2412.10531.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2412.10531 v2

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T15:55:25.541610Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

15 of 15 outbound references displayed

  • verified exact0
  • verified fuzzy15
  • unresolved0
  • parse uncertain0
  • malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation de91389d-a4c7-4b94-a4bb-c3c8cf1f5044 · outbound

This paper cites An econometric study of the impact of economic growth and energy use on carbon emissions: panel data evidence from fifty eight countries.

Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas An econometric study of the impact of economic growth and energy use on carbon emissions: panel data evidence from fifty eight countries

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:55:25.807264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation d92bc25a-af07-40ea-bd68-61ff0230f00b · outbound

This paper cites Environmental impacts and behavioral drivers of deep decarbonization for transportation through electric vehicles.

Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas Environmental impacts and behavioral drivers of deep decarbonization for transportation through electric vehicles

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:55:25.794238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation d98039b8-4822-4630-b612-496fe9c5d6ef · outbound

This paper cites Long-term implications of electric vehicle penetration in urban decarbonization scenarios: An integrated land use–transport–energy model.

Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas Long-term implications of electric vehicle penetration in urban decarbonization scenarios: An integrated land use–transport–energy model

Reference 3

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T15:55:25.492460Z digest=sha256:4450e7cb9bf1b39cd1d4e6973e46ef5d0ae3df60f34c723f833c41077b0abd35

Observation 2e750819-d9de-4d2d-973d-40f124fc2467 · outbound

This paper cites Multi-objective distribution planning approach for optimal network investment with ev charging control.

Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas Multi-objective distribution planning approach for optimal network investment with ev charging control

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:55:25.767452Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T15:55:25.496225Z digest=sha256:23db3f9d5354b4bb05158a4185816cadd4d766461fdaab9ecf34d04863d581cd

Observation 97785516-0d78-42b0-ba1e-086f41857a23 · outbound

This paper cites Joint expansion planning of distribution networks, ev charging stations and wind power generation under uncertainty.

Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas Joint expansion planning of distribution networks, ev charging stations and wind power generation under uncertainty

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:55:25.749031Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T15:55:25.499587Z digest=sha256:94312a21ae0d2d040a38dea014aa02d9d0b76dd000c9e05657ab1637cecf0dcf

Observation 1c3ce7f7-d6d9-4af4-84e6-a02ca0f4b75e · outbound

This paper cites Challenges, solutions and future trends in ev-technology: A review.

Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas Challenges, solutions and future trends in ev-technology: A review

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:55:25.732292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T15:55:25.503892Z digest=sha256:5ff2fb23965d2a076d141099cf23038b08c7f47df21b3b5062f02c5af8a16247

Observation 7fc85d40-7630-4b07-9dcb-1654a7779697 · outbound

This paper cites A comprehensive study of key electric vehicle (ev) components, technologies, challenges, impacts, and future direction of development.

Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas A comprehensive study of key electric vehicle (ev) components, technologies, challenges, impacts, and future direction of development

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:55:25.713348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T15:55:25.509367Z digest=sha256:064bbc4cc47e18dfc3ffcbfb23119ac5f30866bc3fb55adf5080bb8e703bfeba

Observation 8597894c-cc0f-4f29-ba46-e32395c753cf · outbound

This paper cites Charging infrastructure access and operation to reduce the grid impacts of deep electric vehicle adoption.

Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas Charging infrastructure access and operation to reduce the grid impacts of deep electric vehicle adoption

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:55:25.697284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T15:55:25.513232Z digest=sha256:b35a9a6064c1f285099bf3ff314027dde028472ed9943f4d124b9712dc844255

Observation 0049b88f-4146-4907-a961-b99ebbe865ef · outbound

This paper cites Factors influencing the economics of public charging infrastructures for EV–A review.Renewable and Sustainable Energy Reviews, 94:500–509, 2018.

Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas Factors influencing the economics of public charging infrastructures for EV–A review.Renewable and Sustainable Energy Reviews, 94:500–509, 2018

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:55:25.682398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Observation f7fbffcc-ae87-48ee-b449-8e8191f6c3c2 · outbound

This paper cites Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions.

Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas Big data analytics in smart grids: state-of-the-art, challenges, opportunities, and future directions

Reference 10

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T15:55:25.520826Z digest=sha256:d5c96d7e9b38d2283b2003112228766bb067fdf6232e10b252d6a9d3782cc6e0

Observation f4e1f09a-3f49-4a32-8992-1a08e54ef576 · outbound

This paper cites Big energy data management for smart grids—issues, challenges and recent developments.

Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas Big energy data management for smart grids—issues, challenges and recent developments

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:55:25.638844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T15:55:25.524817Z digest=sha256:835c7597fb89b6be26d63da5f06a863b90673ca2fd621339aa69ce9d2f7ba63c

Observation 86eab970-a84c-48cc-ae40-289812e2129e · outbound

This paper cites Charging strategies to minimize the peak load for an electric vehicle fleet.

Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas Charging strategies to minimize the peak load for an electric vehicle fleet

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:55:25.625648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T15:55:25.529475Z digest=sha256:f37aa9fad2ca476d14304fa12a37f32e5ab55caa75e7ad91b108a150849e27ba

Observation 3cdd9c7c-58d5-455e-9255-1875d8a7fb63 · outbound

This paper cites Stochastic-based optimal charging strategy for plug-in electric vehicles aggregator under incentive and regulatory policies of dso.

Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas Stochastic-based optimal charging strategy for plug-in electric vehicles aggregator under incentive and regulatory policies of dso

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:55:25.609761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T15:55:25.533900Z digest=sha256:20e961d7e759ba48c7c9c84254070ed84ef6a196e0a50387c52531119592e7e7

Observation 50730544-258b-424a-8451-8356dfb13beb · outbound

This paper cites Základní sídelní jednotky - polygony, 2024.

Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas Základní sídelní jednotky - polygony, 2024

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:55:25.595274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-11T15:55:25.537994Z digest=sha256:0e5066cf3e25fbe66617511e775f445da5d82f46c95641c6dbdadf44b3456ac6

Observation ec26fa2e-246c-48c9-a139-2ab87eb9a294 · outbound

This paper cites Ve ˇrejné dobíjení | PRE, 2024.

Towards Using Machine Learning to Generatively Simulate EV Charging in Urban Areas Ve ˇrejné dobíjení | PRE, 2024

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T15:55:25.579274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

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Pith citing papers

No inbound Pith citation observations are available.