Pith Number

pith:C7N3IKWK

pith:2026:C7N3IKWKJV3I273PPDZCGTFXGF

not attested not anchored not stored refs resolved

Dynamic Deployment of Mobile Charging Trucks During Natural Disaster Evacuation: An Offline-to-Online Framework

Kaan Ozbay, Rui Ma, Zilin Bian

An adaptive offline-to-online framework deploys mobile charging trucks to cut electric vehicle risk exposure during evacuations by up to 71 percent in simulations.

arxiv:2605.16784 v1 · 2026-05-16 · cs.MA

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\usepackage{pith}
\pithnumber{C7N3IKWKJV3I273PPDZCGTFXGF}

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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

ARMD reduces average risk exposure by up to 71.1% in demand perturbation scenarios and 39.3% to 60.5% in fixed infrastructure or road link failure cases, consistently outperforming offline optimization, online heuristic dispatch, and rolling-horizon optimization.

C2weakest assumption

The simulated hurricane evacuation environment built from Hillsborough County data sufficiently captures real-world EV charging demand patterns, travel times, and behavioral responses under uncertainty to allow reliable policy transfer from offline training to online operation.

C3one line summary

Develops ARMD framework with MAPPO for decentralized MCT allocation and spatio-temporal predictors for dynamic routing, showing up to 71.1% risk reduction in simulated hurricane evacuations versus baselines.

References

62 extracted · 62 resolved · 1 Pith anchors

[1] Evacuation route planning for alternative fuel vehicles.Transportation research part C: emerging technologies, 143:103837, 2022 2022

[2] Assessing the impact of electric vehicles on traffic emissions: An agent-based modeling approach considering traveler behavior changes.Procedia Computer Science, 257: 329–335, 2025 2025

[3] Zerun Liu, Tu Lan, Zilin Bian, Jingqin Gao, and Kaan Ozbay. A comprehensive framework for the assessment of the effects of increased electric truck weights on road infrastructure: A new york city case 2025

[4] Department of Energy 2022

[6] Gas Stations in the United States of America: Ev- erything You Need to Know

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:03:21.824012Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

17dbb42aca4d768d7f6f78f2234cb7317cf4ef702f607b6e3b3ba7da9e82cd5b

Aliases

arxiv: 2605.16784 · arxiv_version: 2605.16784v1 · doi: 10.48550/arxiv.2605.16784 · pith_short_12: C7N3IKWKJV3I · pith_short_16: C7N3IKWKJV3I273P · pith_short_8: C7N3IKWK

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/C7N3IKWKJV3I273PPDZCGTFXGF \
  | 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: 17dbb42aca4d768d7f6f78f2234cb7317cf4ef702f607b6e3b3ba7da9e82cd5b

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "e56ea17b7fc924ca55160056d8ea38323d6e9efef6d1317a19cd00b30ba08dcf",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.MA",
    "submitted_at": "2026-05-16T03:35:16Z",
    "title_canon_sha256": "fe65e299d5dab9ffb48161db5f6ecf917ca6c1510466f8906faf15052d72bc44"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16784",
    "kind": "arxiv",
    "version": 1
  }
}