Age-Friendly Public Transport: Evidence from Travel Behaviors of Older Adults in Hong Kong's Metro Station

Abstract

Many economies worldwide are rapidly moving towards ageing societies and are faced with significant existing and future surges in their ageing populations. Age-friendly public transport systems are essential in these societies for enhancing older adults' mobility and ensuring well-being. Although previous research has confirmed the impacts of the built environment on human travel behavior, most studies have focused on outdoor environments, with limited research on older adults' travel behavior in metro stations. This study examines the in-station travel time of elderly metro travelers, highlighting spatial and temporal differences compared to younger travelers, and identifying the travel and built environment features that contribute to these differences. Using five-day smart card data, a Random Forest machine learning model investigates the influence of trip features, station features, and built environment characteristics on travel time differences between older and younger adults inside metro stations. Results show that older adults tend to spend more time inside stations compared to younger adults, with travel time differences demonstrating spatial variations between stations. Crowdedness level, number of staircases, exits, and escalators all have significant nonlinear effects on travel time. Quantifying these nonlinear effects can help identify challenging stations for older adults and provide policy guidance for creating age-friendly metro stations, promoting transport equity for older adults.