Cycling routes to mitigate heat stress on cyclists in tropical urban environments: an extendable agent-based modeling framework for strategic decision-making and business applications

dc.contributor.authorWaidyalankara, SAR
dc.contributor.authorDassanayake, SM
dc.contributor.authorJayasooriya, V
dc.date.accessioned2025-01-17T08:50:40Z
dc.date.available2025-01-17T08:50:40Z
dc.date.issued2024
dc.description.abstractHeat stress is a significant concern for cyclists in tropical urban environments, impacting both their well-being and overall thermal comfort. While extensive research has been conducted globally to mitigate heat stress, particularly in cold climate cities where winter heat stress reduces urban thermal comfort, the focus has largely been on pedestrian thermal comfort. Research on addressing heat stress among cyclists, especially in tropical urban settings, remains limited. The optimization of cycling routes to minimize perceived heat stress and improve thermal comfort is an area that has not been extensively explored. This study addresses these research gaps by introducing a novel and effective methodology utilizing an Agent-Based Model to assist urban cyclists in selecting routes that minimize heat stress. By doing so, the study aims to enhance the thermal comfort of cyclists in tropical cities, contributing to a more sustainable and cyclist-friendly urban environment.en_US
dc.identifier.conferenceInternational Conference on Business Researchen_US
dc.identifier.doihttps://doi.org/10.31705/ICBR.2024.20en_US
dc.identifier.emailsasiniwaidyalankara@gmail.comen_US
dc.identifier.facultyBusinessen_US
dc.identifier.pgnospp. 259-262en_US
dc.identifier.placeMoratuwaen_US
dc.identifier.proceeding7th International Conference on Business Research (ICBR 2024)en_US
dc.identifier.urihttp://dl.lib.uom.lk/handle/123/23167
dc.identifier.year2024en_US
dc.language.isoenen_US
dc.publisherBusiness Research Unit (BRU)en_US
dc.titleCycling routes to mitigate heat stress on cyclists in tropical urban environments: an extendable agent-based modeling framework for strategic decision-making and business applicationsen_US
dc.typeConference-Full-texten_US

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