Paper Citation Record · LEDGER

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand

As of 6 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2605.19172.

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

pith.paper-citation-record.v1
2605.19172 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-20T11:43:29.621781Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+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

23 of 23 outbound references displayed

verified exact8
verified fuzzy13
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 642a0583-15cb-4a82-998e-d22fd2a8dcd4 · outbound

This paper cites Effects of urban delivery restric- tions on traffic movements.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand Effects of urban delivery restric- tions on traffic movements

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T11:53:27.980474Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation a6b231ac-e415-49c8-8f83-ed54e407d22f · outbound

This paper cites Real-time demand forecast- ing for an urban delivery platform.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand Real-time demand forecast- ing for an urban delivery platform

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T11:53:27.982872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6abbd75d-b3b7-4462-9814-b6c1f22258b8 · outbound

This paper cites Autonomous robot- driven deliveries: A review of recent developments and future direc- tions.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand Autonomous robot- driven deliveries: A review of recent developments and future direc- tions

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T11:53:27.985229Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 74f237ff-4247-43a1-be63-83ff0f22938f · outbound

This paper cites Joint estimation and prediction of city-wide delivery demand: A large language model empowered graph-based learning approach.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand Joint estimation and prediction of city-wide delivery demand: A large language model empowered graph-based learning approach

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T11:53:27.970409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 1fa36488-4a80-4688-9c4b-0d2205302698 · outbound

This paper cites Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-05-20T11:48:15.229393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3628870f-e5a6-436f-af12-eb682323ffe9 · outbound

This paper cites Graph WaveNet for Deep Spatial-Temporal Graph Modeling.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand Graph WaveNet for Deep Spatial-Temporal Graph Modeling

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-20T11:48:15.223544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8d3afdeb-08ac-42c9-abfe-393fae005fe6 · outbound

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

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand Con- necting the dots: Multivariate time series forecasting with graph neural networks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T11:53:27.975241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5de0e5c8-fc62-4f47-9cf1-2a5b6359a3bb · outbound

This paper cites Graph Deep Learning for Time Series Forecasting.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand Graph Deep Learning for Time Series Forecasting

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-20T11:48:15.235521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 391413f7-3d4c-4b2d-8a57-af91839495fd · outbound

This paper cites Domain adversarial spatial-temporal network: A transferable frame- work for short-term traffic forecasting across cities.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand Domain adversarial spatial-temporal network: A transferable frame- work for short-term traffic forecasting across cities

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T11:53:27.977801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0c0b0cc9-f588-42be-bd5b-77d3f1be0191 · outbound

This paper cites Language Models Represent Space and Time.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand Language Models Represent Space and Time

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-20T11:48:15.226540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6d1195bf-6f15-4363-a93e-5e2fc6d600c5 · outbound

This paper cites GeoLLM: Extracting Geospatial Knowledge from Large Language Models.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand GeoLLM: Extracting Geospatial Knowledge from Large Language Models

Reference 11

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T11:48:15.232739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b1e59cbc-1efc-474d-a2c7-bb4ae9e7aaaf · outbound

This paper cites Geolocation representation from large language models are generic enhancers for spatio-temporal learning.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand Geolocation representation from large language models are generic enhancers for spatio-temporal learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T11:53:27.966334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 03b7253d-29c3-4f0d-b7c5-28736a1f4cb7 · outbound

This paper cites A poisson-based distribution learning framework for short-term prediction of food de- livery demand ranges.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand A poisson-based distribution learning framework for short-term prediction of food de- livery demand ranges

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T11:53:27.962535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b1861ef8-662b-4790-8c48-58ada8f6fa64 · outbound

This paper cites A survey on service route and time prediction in instant delivery: Taxonomy, progress, and prospects.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand A survey on service route and time prediction in instant delivery: Taxonomy, progress, and prospects

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T11:53:27.964432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 287cea35-d7fc-41ce-aedd-86b4746059af · outbound

This paper cites On the equivalence between temporal and static equivariant graph representations.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand On the equivalence between temporal and static equivariant graph representations

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T11:53:27.967838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 23ed8d06-3799-4642-8894-14accbbdfba9 · outbound

This paper cites Inductive graph neural networks for spatiotemporal kriging.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand Inductive graph neural networks for spatiotemporal kriging

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T11:53:27.972876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 3902d2da-f8e2-4c0b-834f-79455cf6b70b · outbound

This paper cites Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-20T11:48:15.220333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 48d0c291-0d67-462c-a680-da254d6a6628 · outbound

This paper cites Inductive and adaptive graph convolution networks equipped with constraint task for spatial–temporal traffic data kriging.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand Inductive and adaptive graph convolution networks equipped with constraint task for spatial–temporal traffic data kriging

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T11:53:27.958874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d607bb4b-32d2-4e5d-886f-d0fa758bee18 · outbound

This paper cites Retrieval- augmented generation for knowledge-intensive nlp tasks.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand Retrieval- augmented generation for knowledge-intensive nlp tasks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T11:53:27.960656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fe47300f-67a5-4c40-9f8a-8f4e868260af · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand Semi-Supervised Classification with Graph Convolutional Networks

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-05-20T11:48:15.241367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f883316c-fc9f-454b-852b-996ae8a98276 · outbound

This paper cites LaDe: The First Comprehensive Last-mile Delivery Dataset from Industry.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand LaDe: The First Comprehensive Last-mile Delivery Dataset from Industry

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-20T11:48:15.217805Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 683e7e55-46f9-4233-badf-4ce603a86024 · outbound

This paper cites Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-20T11:48:15.238428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 4f06667a-ce01-4a37-a9b0-0dfae1ea8f45 · outbound

This paper cites Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging.

Bridge: Retrieval-Augmented Spatiotemporal Modeling for Urban Delivery Demand Spatial Aggregation and Temporal Convolution Networks for Real-time Kriging

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T11:48:15.214518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Pith citing papers

No inbound Pith citation observations are available.