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Paper Citation Record · LEDGER

Fairness-enhancing deep learning for ride-hailing demand prediction

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2303.05698.

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

pith.paper-citation-record.v1
2303.05698 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:13:10.936077Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T18:37:04.043614Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 540b90cc-9f3a-4d49-bb65-abe35f428322 · inbound

FairTP: A Prolonged Fairness Framework for Traffic Prediction cites this paper.

FairTP: A Prolonged Fairness Framework for Traffic Prediction Fairness-enhancing deep learning for ride-hailing demand prediction

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-11T13:13:10.936077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T13:13:10.936077Z digest=sha256:f7a39bcac7f08f18fce1fc0363832e50c31634bb59818fd66ee3a42ed00f3e10

Observation 14d199e9-daaa-48c2-b412-a4a6f4aa9378 · inbound

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study cites this paper.

Mitigating Spatial Disparity in Urban Prediction Using Residual-Aware Spatiotemporal Graph Neural Networks: A Chicago Case Study Fairness-enhancing deep learning for ride-hailing demand prediction

Reference 62

Resolution
verified exact
local_arxiv, observed 2026-08-10T18:37:04.048848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-10T18:37:03.529790Z digest=sha256:9511f1b1648805b1fc36903e2889c7dbb14eb22f2b6b19aa753502939601930d