International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Citizen‑Centric Infrastructure Management: AI Models for Engagement and Sustainability |
|---|---|
| ABSTRACT | Cities increasingly rely on citizen reporting (e.g., 311 service requests), IoT sensors, and open data portals to guide infrastructure maintenance and sustainability actions. Yet most deployments optimize service metrics in isolation—focusing narrowly on response time or backlog—without jointly considering equity across neighborhoods and sustainability outcomes such as emissions, resilience, and safety. This paper presents Civic‑Sustain AI, an equity‑constrained, multi‑objective framework that integrates (i) citizen engagement modeling, (ii) sustainability impact modeling, (iii) fairness constraints with subgroup auditing, and (iv) privacy‑conscious governance aligned with NIST AI RMF and the NIST Privacy Framework. We define two composite metrics—Citizen Engagement Score (CES) and Sustainability Impact Score (SIS)—and pose portfolio selection of municipal actions as a constrained multi‑objective Bayesian optimization problem using q‑Expected Hypervolume Improvement (qEHVI). We articulate a reproducible evaluation plan on Open311‑compatible data (e.g., NYC 311), EPA EJScreen indicators, climate exposures, and DOT asset records, and provide an illustrative synthetic study to characterize trade‑offs on the CES–SIS Pareto frontier. Civic‑Sustain AI aligns outputs with ISO 37120/37122 [4,5] indicator families so that optimization results map to reporting standards used by cities. We discuss risk, privacy, and ethical guardrails; threats to validity; and pathways to deployment via digital‑twin dashboards. A brief, non‑central reference to the DICE framework is included to motivate multi‑source data‑integration concepts while maintaining a primarily civic focus. |
| TITLE | |
| AUTHOR | RAJ MEHTA |
| VOLUME | 176 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1311001 |
| pdf/1_Citizen‑Centric Infrastructure Management AI Models for Engagement and Sustainability.pdf | |
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