Multi-step ahead prediction of taxi demand using time-series and textual data

Modelling urban mobility and understanding what drives the travel behavior of people is the key research topic for developing effective and efficient intelligent transportation systems that adapt to the travel demand. Typical forecasting approaches focus only on capturing recurrent mobility trends that relate to routine behaviors (Krygsman et al., 2004), and on exploiting short-term correlations with recent observation patterns (Moreira-Matias et al., 2013 and Van Oort et al., 2015). While this type of approaches can be successful for long-term planning applications or for modelling demand in non-eventful areas such as residential neighborhoods, in lively and highly dynamic areas that are prone to the occurrence of multiple special events, such as music concerts, sports games, festivals, parades and protests, these approaches fail to accurately model mobility demand (Pereira et al., 2015). As we move towards the deployment of autonomous vehicles, understanding and being able to anticipate mobility demand becomes crucia