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<p>Urban metro systems, in rapidly developing cities like Delhi, experience high passenger crowding, which may contribute to thermal stress within Transit-Oriented Development zones. This study examines the effect of metro crowding on local thermal conditions using a regression model, where Land Surface Temperature is modeled as the dependent variable, and the Crowding Contribution Index, developed from Automated Fare Collection and General Transit Feed Specification data, serves as the independent variable. To account for ecological moderation, the Normalized Difference Vegetation Index and Normalized Difference Moisture Index are included. Data from 237 Delhi Metro stations were analyzed across three concentric buffer zones: 0–500&#xa0;m, 500–800&#xa0;m, and 800–2000&#xa0;m. In the 0–500&#xa0;m and 500–800&#xa0;m zones, the Crowding Contribution Index is positively associated with Land Surface Temperature, indicating that stations with high crowding contribute to localized thermal hotspots, while the NDVI and NDMI mitigate these effects through vegetative cooling and surface moisture. In contrast, in the 800–2000-meter buffer zone, Land Surface Temperature is influenced predominantly by ecological variables, with no significant effect of crowding. A causal robustness check using propensity-score matching confirms that the CCI–LST relationship in the inner buffer. This study demonstrates how sensor-enabled transit datasets, spatial analytics, and geospatial sensing can be integrated into intelligent mobility frameworks to model urban heat impacts. It highlights the need for climate-responsive, sensor-informed transit planning that links environmental performance with passenger dynamics, supporting the development of future-ready transportation networks in megacities.</p>

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A Sensor-Driven Framework for Modeling the Impact of Metro Crowding Contributions on Local Thermal Stress Across Transit-Oriented Development Zones

  • 期刊论文

  • 发表日期：

    2026年1月6日

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  • Blessy David Xavier,
  • Akimasa Fujiwara

摘要

Urban metro systems, in rapidly developing cities like Delhi, experience high passenger crowding, which may contribute to thermal stress within Transit-Oriented Development zones. This study examines the effect of metro crowding on local thermal conditions using a regression model, where Land Surface Temperature is modeled as the dependent variable, and the Crowding Contribution Index, developed from Automated Fare Collection and General Transit Feed Specification data, serves as the independent variable. To account for ecological moderation, the Normalized Difference Vegetation Index and Normalized Difference Moisture Index are included. Data from 237 Delhi Metro stations were analyzed across three concentric buffer zones: 0–500 m, 500–800 m, and 800–2000 m. In the 0–500 m and 500–800 m zones, the Crowding Contribution Index is positively associated with Land Surface Temperature, indicating that stations with high crowding contribute to localized thermal hotspots, while the NDVI and NDMI mitigate these effects through vegetative cooling and surface moisture. In contrast, in the 800–2000-meter buffer zone, Land Surface Temperature is influenced predominantly by ecological variables, with no significant effect of crowding. A causal robustness check using propensity-score matching confirms that the CCI–LST relationship in the inner buffer. This study demonstrates how sensor-enabled transit datasets, spatial analytics, and geospatial sensing can be integrated into intelligent mobility frameworks to model urban heat impacts. It highlights the need for climate-responsive, sensor-informed transit planning that links environmental performance with passenger dynamics, supporting the development of future-ready transportation networks in megacities.