Paper Citation Record · LEDGER

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting

As of 11 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2506.02576.

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

pith.paper-citation-record.v1
2506.02576 v3

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:24:39.332178Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

32 of 32 outbound references displayed

verified exact1
verified fuzzy25
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4bfacf5f-10e5-4b79-bc6d-c2f8e6c38d1a · outbound

This paper cites Layer Normalization.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Layer Normalization

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T11:24:36.614815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1677024f-26dc-4852-856a-8d744d48bf77 · outbound

This paper cites Stochastic origin- destination matrix forecasting using dual-stage graph con- volutional, recurrent neural networks.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Stochastic origin- destination matrix forecasting using dual-stage graph con- volutional, recurrent neural networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:43.939467Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8055870a-922b-4a9f-a24f-e04124bae9d7 · outbound

This paper cites Std-plm: Understanding both spa- tial and temporal properties of spatial-temporal data with plm.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Std-plm: Understanding both spa- tial and temporal properties of spatial-temporal data with plm

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:43.747489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 05a8d19b-7545-4b69-9077-653192396061 · outbound

This paper cites Deep multi-view graph- based network for citywide ride-hailing demand predic- tion.Neurocomputing, 510:79–94,.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Deep multi-view graph- based network for citywide ride-hailing demand predic- tion.Neurocomputing, 510:79–94,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:43.423491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c3ec6ef9-5393-4abd-99b5-afeaa4f0c889 · outbound

This paper cites TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecasting.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecasting

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T11:24:37.738084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation affa9429-9fdc-4066-81cf-2f40770d29e0 · outbound

This paper cites Asstformer: Adaptive sparse spatial-temporal transformer for effective traffic forecasting.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Asstformer: Adaptive sparse spatial-temporal transformer for effective traffic forecasting

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:43.098823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c9434d7c-031b-45b0-843a-707e5ded12e8 · outbound

This paper cites Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:42.936341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 89161672-7725-4f68-aea5-64df5c7cc2dc · outbound

This paper cites Stpsformer: Spatial-temporal probsparse transformer for long-term traffic flow forecasting.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Stpsformer: Spatial-temporal probsparse transformer for long-term traffic flow forecasting

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:42.241883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d31d25ec-6639-4ad5-becb-9a1b5cc76ff0 · outbound

This paper cites STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T11:24:38.110006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffcaf0a3-242c-487f-a2cc-fe7d5ff360e6 · outbound

This paper cites Spatial-Temporal Dynamic Graph Attention Networks for Ride-hailing Demand Prediction.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Spatial-Temporal Dynamic Graph Attention Networks for Ride-hailing Demand Prediction

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:24:39.619320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d36fb94e-6f31-4c1a-9260-d28b7bfde652 · outbound

This paper cites Tfb: Towards comprehensive and fair benchmarking of time se- ries forecasting methods.Proceedings of the VLDB En- dowment, 17(9):2363–2377,.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Tfb: Towards comprehensive and fair benchmarking of time se- ries forecasting methods.Proceedings of the VLDB En- dowment, 17(9):2363–2377,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:41.650600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 6567dab0-5cd0-43d3-b866-237e3d87cdbc · outbound

This paper cites A comprehensive sur- vey of deep learning for multivariate time series fore- casting: A channel strategy perspective.arXiv preprint arXiv:2502.10721,.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting A comprehensive sur- vey of deep learning for multivariate time series fore- casting: A channel strategy perspective.arXiv preprint arXiv:2502.10721,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T11:24:38.330229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80ef2731-0ec7-4717-89e2-b6a3704742db · outbound

This paper cites Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecast- ing.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecast- ing

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:41.504359Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation b7af7e65-0067-4e4a-9506-beef87fe9ff0 · outbound

This paper cites Graph wavenet for deep spatial-temporal graph modeling.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Graph wavenet for deep spatial-temporal graph modeling

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:41.298531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 1a39709f-5850-48c3-86b7-33a881d01aa2 · outbound

This paper cites Con- necting the dots: Multivariate time series forecasting with graph neural networks.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Con- necting the dots: Multivariate time series forecasting with graph neural networks

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:41.084357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 88b1b6da-5bbf-460b-ab1f-25870b8c2ee2 · outbound

This paper cites Catch: Channel-aware multivariate time series anomaly detection via frequency patching.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Catch: Channel-aware multivariate time series anomaly detection via frequency patching

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:40.849020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8364bb56-5a07-4cdd-9f38-a937ab2e1cfd · outbound

This paper cites Learning dynamic and hierarchical traffic spatiotemporal features with transformer.IEEE Transactions on Intelli- gent Transportation Systems, 23(11):22386–22399,.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Learning dynamic and hierarchical traffic spatiotemporal features with transformer.IEEE Transactions on Intelli- gent Transportation Systems, 23(11):22386–22399,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:40.705695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0d3e5c42-84e4-4e8b-8529-eede989d435b · outbound

This paper cites Differential Transformer.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Differential Transformer

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T11:24:39.008059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d66bfef-5c51-4c0c-ab0c-5e2b727588c7 · outbound

This paper cites Regu- larized graph structure learning with semantic knowledge for multi-variates time-series forecasting.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Regu- larized graph structure learning with semantic knowledge for multi-variates time-series forecasting

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:40.285494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ddb7a532-83f8-4191-aa5d-75074b369e24 · outbound

This paper cites Dnn-based prediction model for spatio-temporal data.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Dnn-based prediction model for spatio-temporal data

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:40.144029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 47a5c0fa-140e-4ada-921e-32eb245d01e5 · outbound

This paper cites Gman: A graph multi- attention network for traffic prediction.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Gman: A graph multi- attention network for traffic prediction

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:39.994340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 85ce6bbe-d1b3-485b-86f3-7da51e09b9d3 · outbound

This paper cites Soup: Spatial-temporal demand forecasting and com- petitive supply in transportation.IEEE Transactions on Knowledge and Data Engineering, 35(2):2034–2047, 2021.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Soup: Spatial-temporal demand forecasting and com- petitive supply in transportation.IEEE Transactions on Knowledge and Data Engineering, 35(2):2034–2047, 2021

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:39.849409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation af8671e3-e5eb-4ba1-bbe3-86f5d7849511 · outbound

This paper cites Price- and-time-aware dynamic ridesharing.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Price- and-time-aware dynamic ridesharing

Reference 1994

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:44.618342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 444a799a-4530-4a22-8224-a65437ce307f · outbound

This paper cites Adaptive graph convolutional recurrent network for traffic forecasting.Advances in neural infor- mation processing systems, 33:17804–17815,.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Adaptive graph convolutional recurrent network for traffic forecasting.Advances in neural infor- mation processing systems, 33:17804–17815,

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:44.991023Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 72b6321b-6ba6-4248-aca6-893abec881a4 · outbound

This paper cites Machine learning for public transportation de- mand prediction: A systematic literature review.Engineer- ing Applications of Artificial Intelligence, 137:109166,.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Machine learning for public transportation de- mand prediction: A systematic literature review.Engineer- ing Applications of Artificial Intelligence, 137:109166,

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:44.451124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d94be244-70e7-45a8-92ef-4b82df2bbe28 · outbound

This paper cites Self-supervised spatial-temporal bottleneck attentive network for efficient long-term traffic forecasting.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Self-supervised spatial-temporal bottleneck attentive network for efficient long-term traffic forecasting

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:44.088570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 17f35689-b813-4951-ac57-897ebc8858e2 · outbound

This paper cites Using dynamic time warping to find patterns in time series.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Using dynamic time warping to find patterns in time series

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:44.818399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation cc72dae7-efe2-4639-b4b4-bb23f05e903e · outbound

This paper cites Deep multi-view spatial-temporal network for taxi demand prediction.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Deep multi-view spatial-temporal network for taxi demand prediction

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:40.493873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 300a44f9-5824-46ce-9755-9419e793e4a1 · outbound

This paper cites Hexagon- based convolutional neural network for supply-demand forecasting of ride-sourcing services.IEEE Transactions on Intelligent Transportation Systems, 20(11):4160–4173,.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Hexagon- based convolutional neural network for supply-demand forecasting of ride-sourcing services.IEEE Transactions on Intelligent Transportation Systems, 20(11):4160–4173,

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:43.263177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 20d05a86-741e-4c4d-9f32-cfeb7928c5e4 · outbound

This paper cites Deep residual learning for image recog- nition.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Deep residual learning for image recog- nition

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T11:24:37.122668Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5801d218-4d59-43e4-888f-e54ff5ca1456 · outbound

This paper cites Spatiotemporal multi-graph convolution network for ride- hailing demand forecasting.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Spatiotemporal multi-graph convolution network for ride- hailing demand forecasting

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:44.267766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 538f28ae-d029-45b6-b974-77746f1787d7 · outbound

This paper cites Pdformer: Propagation delay- aware dynamic long-range transformer for traffic flow pre- diction.

ADFormer: Aggregation Differential Transformer for Passenger Demand Forecasting Pdformer: Propagation delay- aware dynamic long-range transformer for traffic flow pre- diction

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T11:24:43.556024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.