Adaptive multi-agent learning for infrastructure-aware ITS: the IMER data-processing approach
Document Type
Conference Proceeding
Source of Publication
Transportation Research Procedia
Publication Date
1-1-2026
Abstract
The performance of Intelligent Transportation Systems (ITS) critically depends on accurate and efficient road-condition monitoring. This paper presents IMER (Inspect–Map–Eliminate–Reduce), a novel AI-driven data-processing framework that extends the traditional Map-Reduce paradigm for infrastructure maintenance. IMER integrates confidence-based validation, redundancy elimination, and severity prioritization to enhance data quality and decision efficiency. Implemented within a multi-agent architecture, IMER enables autonomous agents to inspect, classify, and fuse multi-source road data in real time, supporting predictive and adaptive maintenance planning. Simulation results using augmented pothole datasets demonstrate a 39.9 % reduction in redundant reports and 39.8 % fewer false positives. These findings highlight IMER’s potential to advance data-driven, resilient, and sustainable road-infrastructure management for next-generation ITS.
DOI Link
ISSN
Publisher
Elsevier BV
Volume
97
First Page
156
Last Page
163
Disciplines
Computer Sciences
Keywords
Inspect-Map-Eliminate-Reduce, Intelligent Transportation Systems (ITS), Multi-Agent Systems, Road Infrastructure Monitoring
Scopus ID
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Hamdani, Mayssa; Jabeur, Nafaa; Yasar, Ansar; Outay, Fatma; and Li, Li, "Adaptive multi-agent learning for infrastructure-aware ITS: the IMER data-processing approach" (2026). All Works. 8115.
https://zuscholars.zu.ac.ae/works/8115
Indexed in Scopus
yes
Open Access
yes
Open Access Type
Gold: This publication is openly available in an open access journal/series