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- W3171582011 abstract "As life expectancy increases around the world, it also grows the number of people with reduced mobility (older adults and disabled individuals), leading to a need to rethink transport planning in urban areas to include these individuals. To accomplish this, city planners must understand the key features that most influence the travel behaviour among people with reduced mobility to provide independent and healthy lifestyles and promote the use of healthy active transport modes among them. In this study, a set of machine learning models was trained to predict the travel behaviour of people with reduced mobility in Medellín, and the most appropriate model was used to examine the underlying key features that most influence the travel behaviour among these individuals. Additionally, a specific systemic analysis was carried out for people with reduced mobility who choose active transport modes to make their trips. Results show that the CatBoost implementation of a gradient boosting model outperforms other machine learning models considering the predefined evaluation metrics. The numerical results reveal that travel time, trip frequency and motorised-vehicle ownership are the key features that most influence the travel behaviour of people with reduced mobility in Medellín. In order to ensure an equitable and inclusive society, the main objective of public agencies should aim to understand the travel behaviour of an increasing population with reduced mobility that deserves inclusive solutions to improve their mobility conditions." @default.
- W3171582011 created "2021-06-22" @default.
- W3171582011 creator A5071881902 @default.
- W3171582011 date "2021-09-01" @default.
- W3171582011 modified "2023-10-03" @default.
- W3171582011 title "Travel time, trip frequency and motorised-vehicle ownership: A case study of travel behaviour of people with reduced mobility in Medellín" @default.
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- W3171582011 doi "https://doi.org/10.1016/j.jth.2021.101110" @default.
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