A Counterfactual AI-Based System for Spatio-Temporal Traffic Risk Prediction and Intelligent Safety Intervention in Smart Transportation Systems

This paper presents a novel system-oriented counterfactual deep learning framework, termed Hybrid Prediction–Intervention Neural Architecture (HPINA) for intelligent traffic accident risk prediction and proactive safety intervention in smart transportation systems. Unlike conventional data-driven mo...

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Bibliografske podrobnosti

Principais autores:	Nawal Louzi, Areen M. Arabiat, Mahmoud AlJamal
Format:	Artigo
Jezik:	Inglês
Izdano:	MDPI AG 2026-04-01
Serija:	Infrastructures
Teme:	traffic accident prediction counterfactual learning intelligent transportation systems spatio-temporal modeling deep learning traffic safety analysis
Online dostop:	https://www.mdpi.com/2412-3811/11/5/152
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