Pith Number

pith:PR7NW7GU

pith:2026:PR7NW7GUNP5CBDJKEYERJTB7EQ

not attested not anchored not stored refs resolved

Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems via Deep Reinforcement Learning

Alberto Pettena, Edoardo Scarpel, Federico Chiariotti, Gian Antonio Susto, Marco Fabris, Matteo Cederle

A deep reinforcement learning agent rebalances dockless bikes in real time by routing one truck to localized hotspots.

arxiv:2605.14501 v1 · 2026-05-14 · eess.SY · cs.AI · cs.LG · cs.SY

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Add to your LaTeX paper

\usepackage{pith}
\pithnumber{PR7NW7GUNP5CBDJKEYERJTB7EQ}

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

Experiments on real-world data show significant reductions in availability failures with a minimal fleet size, while limiting spatial inequality and mobility deserts.

C2weakest assumption

The graph-based simulator used to train the DRL agent accurately reflects real user behavior, demand patterns, and operational constraints of the bike-sharing system.

C3one line summary

A DRL agent routes one truck for real-time pick-up, drop-off, and charging in dockless bike systems, cutting availability failures on real data while keeping fleet size small.

References

26 extracted · 26 resolved · 0 Pith anchors

[1] American Control Conf 2025

[2] Transport infrastructure and the environment: Sustainable mobility and urbanism , author=. 2013 , publisher= 2013

[3] Bike sharing systems: Solving the static rebalancing problem , author=. Discr. Opt. , volume=. 2013 , publisher= 2013

[4] A bike-sharing optimization framework combining dynamic rebalancing and user incentives , author=. ACM Trans. Auton. & Adaptive Sys. (TAAS) , volume=. 2020 , publisher= 2020

[5] Efficient sensors selection for traffic flow monitoring: An overview of model-based techniques leveraging network observability , author=. Sensors , volume=. 2025 , publisher= 2025

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-17T23:39:06.315100Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

7c7edb7cd46bfa208d2a260914cc3f241b4c6973d643114821b5d802eddf7727

Aliases

arxiv: 2605.14501 · arxiv_version: 2605.14501v1 · doi: 10.48550/arxiv.2605.14501 · pith_short_12: PR7NW7GUNP5C · pith_short_16: PR7NW7GUNP5CBDJK · pith_short_8: PR7NW7GU

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/PR7NW7GUNP5CBDJKEYERJTB7EQ \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 7c7edb7cd46bfa208d2a260914cc3f241b4c6973d643114821b5d802eddf7727

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "fce6835eb1514793857e4526372f7fce1f74dad7e1d3a57bf163b880c35766a0",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.LG",
      "cs.SY"
    ],
    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "primary_cat": "eess.SY",
    "submitted_at": "2026-05-14T07:46:23Z",
    "title_canon_sha256": "4f40172d71ba87b1e5bc7b5a9ee9a399386c49f2b3443a5ca07543eedebdd6aa"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.14501",
    "kind": "arxiv",
    "version": 1
  }
}