Pith. sign in

Paper Citation Record · LEDGER

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics

As of 23 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2411.14014.

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

pith.paper-citation-record.v1
2411.14014 v2

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:42:25.907272Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T06:36:20.078866Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T06:38:05.740492Z

Reference resolution

46 of 46 outbound references displayed

  • verified exact0
  • verified fuzzy40
  • unresolved6
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ff6b1ba2-20ad-419f-92e1-53640d897445 · outbound

This paper cites In- terpreting trajectories from multiple views: A hierarchical self-attention network for estimating the time of arrival,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics In- terpreting trajectories from multiple views: A hierarchical self-attention network for estimating the time of arrival,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:27.507294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.479141Z digest=sha256:6127d52e2175a67a10900fc6bc0a9febde6b7377bc313d260d436a7af975ed96

Observation cb5ae772-92a5-4972-b48f-7dad2483cfc5 · outbound

This paper cites Dual graph convolution architecture search for travel time estimation,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Dual graph convolution architecture search for travel time estimation,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:27.464375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.485520Z digest=sha256:3d00b3a2f4d09709c03620c50c701bc89a5d6b4b274bac77bb92db63daa69238

Observation c688f5ee-af11-42fb-8ec5-f91b3786b559 · outbound

This paper cites Travel time distribution estimation by learning representations over temporal attributed graphs,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Travel time distribution estimation by learning representations over temporal attributed graphs,

Reference 3

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:27.420788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.497634Z digest=sha256:b146262bc07fcb6e3df51c08a2bb8fabab9af9bbc78ea5921451fe075f4e8274

Observation f9a48153-c28b-4f7a-95f0-1a607a989201 · outbound

This paper cites Destination prediction by trajectory distribution-based model,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Destination prediction by trajectory distribution-based model,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:27.394290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.507196Z digest=sha256:99f20a71116cab0160f228ba803dc663b83fee78e30343e1396bb0eeb5ed2322

Observation c7afd53d-3c2a-4add-9eff-775e395b2b90 · outbound

This paper cites Destination prediction based on virtual POI docks in dockless bike-sharing system,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Destination prediction based on virtual POI docks in dockless bike-sharing system,

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:27.371189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.516069Z digest=sha256:c376606a2b88c824663cf8b309f4cefbee538c36bb5a59442d90c639cb6798ef

Observation fb072cf7-520e-4e79-a11f-e4014fb7a211 · outbound

This paper cites How machine learning informs ride-hailing services: A survey,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics How machine learning informs ride-hailing services: A survey,

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-12T15:42:25.530176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:42:25.530176Z digest=sha256:15259589521e08af584b79f1b39c72d9d2ffb5179a42a6f18d0e7ead3ac49cba

Observation fc7bbac7-bdfa-4730-8097-6d07679284f5 · outbound

This paper cites Spatio-temporal trajectory similarity learning in road networks,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Spatio-temporal trajectory similarity learning in road networks,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:27.292837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.538890Z digest=sha256:df0346f04a8a80ac1fc507b7fb6066f79dbb3badf47745ac941c79f555b98151

Observation 057770b7-aa39-478f-9406-d461f90dad6c · outbound

This paper cites Trajgat: A graph- based long-term dependency modeling approach for trajectory similarity computation,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Trajgat: A graph- based long-term dependency modeling approach for trajectory similarity computation,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:27.256288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.547569Z digest=sha256:17bd8cf3bdd8f727cad3f4c4fc507540daa7f5d52bc85b5c7498c4245f61e53e

Observation 38918187-79e3-41cc-99a8-baabb4df426c · outbound

This paper cites KGTS: contrastive trajectory similarity learning over prompt knowl- edge graph embedding,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics KGTS: contrastive trajectory similarity learning over prompt knowl- edge graph embedding,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:27.223307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.555362Z digest=sha256:55437459100d052f9ddfafb1cbac6483a1a36657e7c55f83818ee578aee25d49

Observation 4530f98a-9f92-4c48-a979-00dedae84f9f · outbound

This paper cites Contrastive trajectory simi- larity learning with dual-feature attention,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Contrastive trajectory simi- larity learning with dual-feature attention,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:27.194248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.568214Z digest=sha256:62b52ea214543f8c6794f48f8bbf8945352f8609db62df35494a11701fb4c45b

Observation f0f25766-285b-435e-ab4e-d894aa3e4e71 · outbound

This paper cites Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Pre-training General Trajectory Embeddings with Maximum Multi-view Entropy Coding

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-12T15:42:25.583814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:42:25.583814Z digest=sha256:71112a121bbb4e08d99662e7dd3e40fae7d4fdd1b952770ed8153515627e91ca

Observation 0611a0b5-c1e8-46ed-8f5e-3061b3c45aa9 · outbound

This paper cites Efficient trajectory similarity computation with contrastive learning,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Efficient trajectory similarity computation with contrastive learning,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:27.154534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.593843Z digest=sha256:55c5f74e8af5ad87b45fd1bc7cfbea112949926114bdba1aac4c55699e232388

Observation c63a428f-fa19-478a-b982-06aee927e69d · outbound

This paper cites Deep representation learning for trajectory similarity computation,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Deep representation learning for trajectory similarity computation,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:27.122962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.603976Z digest=sha256:e1a5cc92da4e1b1c609ec016b46290f7454a6abeb70d996ef9a4047ddc05ff20

Observation 9d3bf49e-3e1d-4100-9fbf-3aff652c95c6 · outbound

This paper cites Self-supervised trajectory representation learning with temporal regularities and travel semantics,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Self-supervised trajectory representation learning with temporal regularities and travel semantics,

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:27.079206Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.611957Z digest=sha256:3f954d00781c27dd908bf9ae028e02dc30667a2f7308b2da5b3304b92ff8049c

Observation 104629be-4b92-4564-b914-59ea61b4da11 · outbound

This paper cites Lightpath: Lightweight and scalable path representation learning,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Lightpath: Lightweight and scalable path representation learning,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:27.046951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.627093Z digest=sha256:347b9d51e9ab5e5366528b93281ffaf88f46a162c6a0dd0b8a6fede8115c219f

Observation 9f4b21df-aaaf-4ce7-b07c-6d38c646f1a8 · outbound

This paper cites Jointly contrastive representation learning on road network and trajectory,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Jointly contrastive representation learning on road network and trajectory,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:27.004878Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.635278Z digest=sha256:999e3887e1f623248cafbb86c2056a5a3089a80c48e9e49b996db1b9a179d1b0

Observation 1afa6e47-2ba2-4fa0-b718-ca80fb83017a · outbound

This paper cites Trembr: Exploring road networks for trajectory representation learning,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Trembr: Exploring road networks for trajectory representation learning,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.967470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.642743Z digest=sha256:9436118516005bb29a46c7d3cfd6ab8992891587202e8e8df503dc6430585d19

Observation c2fbf785-d329-43b3-924b-26e99911623e · outbound

This paper cites Fast map matching, an algorithm integrating hidden markov model with precomputation,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Fast map matching, an algorithm integrating hidden markov model with precomputation,

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-12T15:42:25.655888Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:42:25.655888Z digest=sha256:76f7e0d8ebf46904da427f7d600665f11f32bd87c29c92a16669fd6d590d3596

Observation ba8b742f-0d29-414a-a87b-065f5e57892b · outbound

This paper cites Pre-training general trajectory embeddings with maximum multi-view entropy cod- ing,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Pre-training general trajectory embeddings with maximum multi-view entropy cod- ing,

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-12T15:42:25.664087Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:42:25.664087Z digest=sha256:055dc5f69d9e388606eefcb8084e5b57d8392d92690a40882c1a72d63f338b2b

Observation 58185bd0-5a1f-4e8a-b61f-7984afc82082 · outbound

This paper cites Attention is all you need,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Attention is all you need,

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.892673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.670534Z digest=sha256:5579502d1e2ba8fe4dca4d87d1324295916065945ee0e8cf608f3650c4d87c43

Observation c418882a-3cea-4a76-826e-0ac1802e864c · outbound

This paper cites Masked autoencoders are scalable vision learners,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Masked autoencoders are scalable vision learners,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.860904Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.679136Z digest=sha256:57e33d86224ddb7733fd4688f9242bb64d7c14740e35aed6d4d6cb43e7d70448

Observation 8067a6dd-e2df-4f24-b47f-0488d2197451 · outbound

This paper cites Masked autoencoders as spatiotemporal learners,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Masked autoencoders as spatiotemporal learners,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.833678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.686887Z digest=sha256:752a007875ae2cdcd590dba50d690e97f3ab0a015ae26eaba3419b8002400764

Observation 5b53cda0-664a-41ef-8005-57b6230ced0c · outbound

This paper cites Mavil: Masked audio-video learners,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Mavil: Masked audio-video learners,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.795244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.692551Z digest=sha256:77eed99f1da19f6f2352264348c1632a3e73b57346b84fb3c97253bb45ae7bbe

Observation 4232c3e7-e6da-4257-84e3-510b951c29ba · outbound

This paper cites BERT: pre-training of deep bidirectional transformers for language understanding,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics BERT: pre-training of deep bidirectional transformers for language understanding,

Reference 24

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.753451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.698992Z digest=sha256:e154b2d1b62bbc0b45da9b900ace40aba3ff99012c276a0001699bfe9405edeb

Observation 6bf6a492-b74d-4e84-8a5d-677f3743a712 · outbound

This paper cites Masked siamese networks for label-efficient learning,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Masked siamese networks for label-efficient learning,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.721684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.705237Z digest=sha256:9bd7174968e0b1d0c0c8e2b30ed6fdec176ec43d90d4b4179e03f03454384d71

Observation 05f49fa3-7175-4506-afc2-d8d75fd49a11 · outbound

This paper cites RoFormer: Enhanced Transformer with Rotary Position Embedding.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics RoFormer: Enhanced Transformer with Rotary Position Embedding

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-12T15:42:25.713170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:42:25.713170Z digest=sha256:109269323038b47856d2ed1a6cd1c7f8e3c610e82871aff43c3d26481d95842c

Observation 386a2e14-9fcf-4101-bfe5-87433f13a1ea · outbound

This paper cites Root mean square layer normalization,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Root mean square layer normalization,

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.684180Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.721163Z digest=sha256:ca31fe9682b574cce478315efd089b1e806522014ce4cd2009d28f1ee379a9bc

Observation 8f58c718-bacb-4e52-bc7e-5e54f45043dc · outbound

This paper cites With a little help from my friends: Nearest-neighbor contrastive learning of visual representations,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics With a little help from my friends: Nearest-neighbor contrastive learning of visual representations,

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.648437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.726987Z digest=sha256:8ad301ad6dbf4451f164b7fcb289d513ed2ea4683d8a84aba02952d776738de9

Observation 5cee4e4e-6c55-4bac-affa-11fa7bdabedf · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Representation Learning with Contrastive Predictive Coding

Reference 29

Resolution
unresolved
no resolver link, observed 2026-08-12T15:42:25.735153Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:42:25.735153Z digest=sha256:36cf796d7e0b96874f22c491ed04a2af89029d2cbb200ae1745e8fd3b64715cb

Observation 76bdf3a0-5109-4b7d-9b80-7c2404ac9ef6 · outbound

This paper cites Trajectory similarity learning with auxiliary supervision and optimal matching,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Trajectory similarity learning with auxiliary supervision and optimal matching,

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.613040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.743481Z digest=sha256:aebf121f4430679ba41e7740675b999e05cde8e43688889a3ca6b794c2462eca

Observation be82ff57-18f0-4039-9e7c-b57351fc3b0e · outbound

This paper cites T3S: effective representation learning for trajectory similarity computation,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics T3S: effective representation learning for trajectory similarity computation,

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.582975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.754844Z digest=sha256:a0078bf52c4290c90f5d635b3cc4e017bba263f73a0796d2fc6a69e069703cad

Observation 9c5157ee-3aa2-4373-8b4f-312ca20ed72e · outbound

This paper cites Weakly- supervised temporal path representation learning with contrastive cur- riculum learning,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Weakly- supervised temporal path representation learning with contrastive cur- riculum learning,

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.555191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.761000Z digest=sha256:161097d1303d358b89aa5610d1d6e32d7668853f864a72f676732550f73c77eb

Observation daa1cbef-5c1a-44c8-a598-2790b1c33c21 · outbound

This paper cites Robust road network representation learning: When traffic patterns meet traveling semantics,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Robust road network representation learning: When traffic patterns meet traveling semantics,

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.530929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.767696Z digest=sha256:146bc00ed8e19d6d065d8d212a643912f692c89ba79c711e6ceca04109705d88

Observation 2dcd9bd3-2f49-46aa-81b8-85e789b2861f · outbound

This paper cites Deepwalk: online learning of social representations,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Deepwalk: online learning of social representations,

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.507799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.787270Z digest=sha256:e8470793a6feee0dd332e4e1a3ab903d765dcad6fdb3b09546d78a45967131af

Observation 3952201a-4ecb-464e-b5a3-f3a7216a4896 · outbound

This paper cites Bootstrap your own latent - A new approach to self-supervised learning,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Bootstrap your own latent - A new approach to self-supervised learning,

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.482082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.795981Z digest=sha256:7ef4f3b75501ca8105768c9bf217fbc8158a2a57204fa2f808a74d93f50c5f0e

Observation 84f5047b-2e17-4370-8285-36f86873687d · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics A simple framework for contrastive learning of visual representations,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.451778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.804219Z digest=sha256:8563390ed1cb331dad2296b5e61b828cde49c04326c4f05081e11a47c926f8c9

Observation 551efb0c-1c4c-4c70-906d-3f4b1e4ba3df · outbound

This paper cites node2vec: Scalable feature learning for networks,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics node2vec: Scalable feature learning for networks,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.422804Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.810169Z digest=sha256:4a78cdd285669c54e7b0f878d3a3272efa30ffc6265387ec81b41634a2e967da

Observation 3e072d8f-f78b-4169-84ab-4b5773757cfe · outbound

This paper cites Metacitta: Deep meta-learning for spatio-temporal prediction across cities and tasks,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Metacitta: Deep meta-learning for spatio-temporal prediction across cities and tasks,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.366720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.819193Z digest=sha256:189c7c54bd8cc4eb885be595fbbdca37646916f444c3bce139f53362f000a986

Observation fa865bcd-eb2d-45e1-a424-e2e3633f47ec · outbound

This paper cites Multi-scale representation learning for spatial feature distributions using grid cells,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Multi-scale representation learning for spatial feature distributions using grid cells,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.327027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.829207Z digest=sha256:b41ed8b0b46ec4879662c69e9d879e8e60f057c720883ddd11ad627fc498b0c9

Observation 95fcf570-18a8-4b0b-a3d9-54e29a7a2857 · outbound

This paper cites Tile2vec: Unsupervised representation learning for spatially distributed data,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Tile2vec: Unsupervised representation learning for spatially distributed data,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.305814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.839028Z digest=sha256:e6fce2402668a246ed770845079decf71923304932b93392834c0bf62bd852e7

Observation 6d64c834-0b7a-4a38-9c56-1d5064663597 · outbound

This paper cites Beyond the first law of geography: Learning representations of satellite imagery by leveraging point-of-interests,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Beyond the first law of geography: Learning representations of satellite imagery by leveraging point-of-interests,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.263623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.853540Z digest=sha256:f46f8b42c141696c61b591d520a0de4bb1b25784515bf1007c6041ecdf0c795f

Observation 0fe675dc-1404-47d8-929b-19cc5076de4b · outbound

This paper cites A survey on map-matching algorithms,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics A survey on map-matching algorithms,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.229668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.861180Z digest=sha256:fc226a104541fb365051ffd8110716e9a9dbcabd3d9d8af09f374afca4b079a4

Observation 91ac4c12-8125-4283-835e-0baf4c1ae899 · outbound

This paper cites Learning effective road network representation with hierarchical graph neural networks,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Learning effective road network representation with hierarchical graph neural networks,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.203670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.870993Z digest=sha256:a31ca69d48ab7bb697c2404bf7f2ee1416406cfa5459e1356462db29134703d8

Observation 2d8cb5b0-3404-443c-ad11-34aebefe076f · outbound

This paper cites On representation learning for road networks,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics On representation learning for road networks,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.172233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.878974Z digest=sha256:4b215d1ef7a35fdf908f70ef2cb8e10d3d1af18fd05c945c63aa7869df17db70

Observation 1105c11f-d9c1-40fe-97fe-de26de6797ec · outbound

This paper cites Unsupervised path representation learning with curriculum negative sampling,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics Unsupervised path representation learning with curriculum negative sampling,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.130092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.894922Z digest=sha256:e076fe020a0e2f22946989f48833760aef627b2919ac57eb478f0c6b69336c77

Observation 7ff0a6d8-67ed-4761-8fd4-b185d7cdff18 · outbound

This paper cites More than routing: Joint GPS and route modeling for refine trajectory representation learning,.

Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics More than routing: Joint GPS and route modeling for refine trajectory representation learning,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-12T15:42:26.102481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-12T15:42:25.907272Z digest=sha256:a3384fd9f97f21ed3e026bcd8c37358a85ae99d029cbb861d8508fa8634aa745

Pith citing papers

Observation c57ad470-fd79-4d07-b77e-8bb2caf3f665 · inbound

TrajTok: Adaptive Spatial Tokenization for Trajectory Representation Learning cites this paper.

TrajTok: Adaptive Spatial Tokenization for Trajectory Representation Learning Trajectory Representation Learning on Road Networks and Grids with Spatio-Temporal Dynamics

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-20T06:38:05.742319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-05-20T06:36:20.078866Z digest=sha256:cc249ce41e91ab5ab609ec88bb41d71952255b5bb594ab62c4ab31cb8211ecba