Ride-hailing demand elasticities and value of travel time savings: An empirical analysis with massive data including sociodemographic characteristics

Understanding how users respond to ride-hailing prices and travel time is essential for designing effective and equitable transport policies. However, most existing evidence comes from high-income countries and stated-preference data, with limited attention to user heterogeneity and spatial contexts. This study contributes to filling this gap by estimating price elasticities and value of travel time savings (VOTTS) for ride-hailing trips in Greater Santiago, Chile, using aggregated data from over three million revealed-preference trip records provided by Uber, organized by origin-destination zone pairs. We estimate log-linear demand models that incorporate detailed sociodemographic and spatial indicators to capture variation across urban and rural zones, times of day, and user characteristics. Results show that price elasticities are higher in absolute terms in rural areas and during off-peak periods, while VOTTS is greater in urban zones and at peak hours. Additionally, ride-hailing demand increases in areas with Metro access and higher immigrant populations, but decreases in zones with higher poverty levels and larger elderly populations. These findings provide valuable insights for tailoring pricing, subsidies, and infrastructure investments to local needs and for enhancing the integration between ride-hailing and public transport in diverse urban settings.