Publicação
Dynamic vs. fixed pricing in urban mobility: an empirical analysis of fare sensitivity in taxi services
| Resumo: | This thesis examines the interplay across diverse mobility options in New York City, focusing on cost variability and commuter behavior. This work investigates the evolution of fare disparities between taxis, using comprehensive datasets from NYC’s Taxi and Limousine Commission. The study revealed how commuter choices are influenced by operational regions and pricing models: key findings highlight significant temporal and spatial fare trends. A comparative analysis of dynamic versus fixed pricing models showcased the flexibility of ride hailing platforms like Uber in addressing peak demands and spatial variations, differing from the rigidity observed in traditional pricing systems. These analyses provide critical insights into spatiotemporal variations in taxi demand, including the differential impacts of demographic factors. The findings aim to guide policymakers in developing adaptive and sustainable mobility strategies tailored to the evolving urban landscape. |
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| Autores principais: | Suchkova, Angelina |
| Assunto: | Urban mobility Geo-specific Analysis Trend analysis Raid-hailing services Fare elasticity |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | This thesis examines the interplay across diverse mobility options in New York City, focusing on cost variability and commuter behavior. This work investigates the evolution of fare disparities between taxis, using comprehensive datasets from NYC’s Taxi and Limousine Commission. The study revealed how commuter choices are influenced by operational regions and pricing models: key findings highlight significant temporal and spatial fare trends. A comparative analysis of dynamic versus fixed pricing models showcased the flexibility of ride hailing platforms like Uber in addressing peak demands and spatial variations, differing from the rigidity observed in traditional pricing systems. These analyses provide critical insights into spatiotemporal variations in taxi demand, including the differential impacts of demographic factors. The findings aim to guide policymakers in developing adaptive and sustainable mobility strategies tailored to the evolving urban landscape. |
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