The usage of location based big data and trip planning services for the estimation of a long-distance travel demand model. Predicting the impacts of a new high speed rail corridor

Travel demand models are a useful tool to assess transportation projects. Within travel demand, long-distance trips represent a significant amount of the total vehicle-kilometers travelled, in contrast to commuting trips. Consequently, they pay a relevant role in the economic, social and environmental impacts of transportation. This paper describes the development of a microscopic long-distance travel demand model for the Province of Ontario (Canada) and analyzes the sensitivity to the implementation of a new high speed rail corridor. Trip generation, destination choice and mode choice models were developed for this research. Multinomial logit models were estimated and calibrated using the Travel Survey for Residents in Canada (TSRC). It was complemented with location-based social network data from Foursquare, improving the description of activities and diverse land uses at the destinations. Level of service of the transit network was defined by downloading trip time, frequency and fare using the planning service Rome2rio. New scenarios were generated to simulate the impacts of a new high speed rail corridor by varying rail travel times, frequencies and fares of the rail services. As a result, a significant increase of rail modal shares was measured, directly proportional to speed and frequency and inversely proportional to price