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pith:4RPZQURX

pith:2025:4RPZQURXRAPT4JF5PGTMQMBRR6

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Learning Multimodal Embeddings for Traffic Accident Prediction and Causal Estimation

Haris N. Koutsopoulos, Hongyang R. Zhang, Minxuan Duan, Ziniu Zhang

Combining satellite images with road network graphs predicts traffic accidents at 90.1% AUROC and identifies causal factors.

arxiv:2512.02920 v3 · 2025-12-02 · cs.LG · cs.CV · cs.SI

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Claims

C1strongest claim

integrating both data modalities improves prediction accuracy, achieving an average AUROC of 90.1%, a 3.7% gain over graph neural network models that use only graph structures. With the improved embeddings, we conduct a causal analysis using a matching estimator to identify the key factors influencing traffic accidents. We find that accident rates rise by 24% under higher precipitation, by 22% on higher-speed roads such as motorways, and by 29% due to seasonal patterns, after adjusting for other confounding factors.

C2weakest assumption

Satellite imagery supplies predictive information about road surface and surroundings that is not already captured by the provided weather statistics, road type labels, and traffic volume features; the matching estimator fully balances all relevant confounders between high- and low-precipitation locations.

C3one line summary

Multimodal embeddings from satellite images and road graphs raise accident prediction AUROC to 90.1 percent and attribute 24 percent higher rates to increased precipitation after confounder adjustment.

References

45 extracted · 45 resolved · 0 Pith anchors

[1] Roadtracer: Automatic extraction of road networks from aerial images 2018

[2] L. J. Blincoe, T. R. Miller, E. Zaloshnja, and B. Lawrence.The economic and societal impact of motor vehicle crashes, 2010 (Revised). Tech. rep. United States. Department of Transportation. National H 2010

[3] TEMPO: Prompt- based Generative Pre-trained Transformer for Time Series Forecasting 2024

[4] FT-AED: Benchmark dataset for early freeway traffic anomalous event detection 2024

[5] Multimodal learning with graphs 2023

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First computed	2026-05-17T23:39:16.941121Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

e45f985237881f3e24bd79a6c830318f9c61270ccb646606ef1065938bc8adce

Aliases

arxiv: 2512.02920 · arxiv_version: 2512.02920v3 · doi: 10.48550/arxiv.2512.02920 · pith_short_12: 4RPZQURXRAPT · pith_short_16: 4RPZQURXRAPT4JF5 · pith_short_8: 4RPZQURX

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/4RPZQURXRAPT4JF5PGTMQMBRR6 \
  | 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: e45f985237881f3e24bd79a6c830318f9c61270ccb646606ef1065938bc8adce

Canonical record JSON

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    "license": "http://creativecommons.org/licenses/by/4.0/",
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    "submitted_at": "2025-12-02T16:39:32Z",
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