Critical Transit Infrastructure in Smart Cities and Urban Air Quality: A Multi-City Seasonal Comparison of Ridership and PM2.5

2026-01-16

arXiv.org

AI Summary

This study addresses the lack of a reproducible analytical framework linking urban transportation and air quality in smart city research, which hinders health-oriented urban resilience planning. We propose a lightweight, scalable multi-city joint monitoring framework that integrates multi-source public transit ridership data with Environmental Protection Agency PM2.5 measurements. By applying monthly aggregation, per capita normalization, and sensitivity regression, our approach enables comparable cross-city and cross-seasonal analyses of both absolute and per capita metrics. Empirical results across four cities reveal significant differences in traffic volume, intensity, and seasonal dynamics. Crucially, we find that the association between transit ridership and PM2.5 levels is predominantly driven by city-specific baseline effects rather than universal patterns, offering empirical support for context-sensitive urban governance strategies.

Technology Category

• Application Domains: Transportation
• Planning, Routing, and Scheduling: Plan Execution and Monitoring
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Security and Privacy: Large-scale security measurements
• Web Mining and Content Analysis: Web data integration and cleaning
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Public transit is a critical component of urban mobility and equity, yet mobility and air-quality linkages are rarely operationalized in reproducible smart-city analytics workflows. This study develops a transparent, multi-source monitoring dataset that integrates agency-reported transit ridership with ambient fine particulate matter PM2.5 from the U.S. EPA Air Quality System (AQS) for four U.S. metropolitan areas - New York City, Chicago, Las Vegas, and Phoenix, using two seasonal snapshots (March and October 2024). We harmonize heterogeneous ridership feeds (daily and stop-level) to monthly system totals and pair them with monthly mean PM2.5 , reporting both absolute and per-capita metrics to enable cross-city comparability. Results show pronounced structural differences in transit scale and intensity, with consistent seasonal shifts in both ridership and PM2.5 that vary by urban context. A set of lightweight regression specifications is used as a descriptive sensitivity analysis, indicating that apparent mobility-PM2.5 relationships are not uniform across cities or seasons and are strongly shaped by baseline city effects. Overall, the paper positions integrated mobility and environment monitoring as a practical smart-city capability, offering a scalable framework for tracking infrastructure utilization alongside exposure-relevant air-quality indicators to support sustainable communities and public-health-aware urban resilience.

Problem

• public transit
• urban air quality
• PM2.5
• smart cities
• ridership

Innovation

• multi-source data integration
• transit ridership
• PM2.5
• smart-city analytics
• cross-city comparability