https://doi.org/10.1177/0361198118777064 Transportation Research Record 1–11 © National Academy of Sciences: Transportation Research Board 2018 Reprints and permissions: sagepub.com/journalsPermissions.nav DOI: 10.1177/0361198118777064 journals.sagepub.com/home/trr TRR JOURNAL OF THE TRANSPORTATION RESEARCH BOARD Article Travel demand models predict the number, destination, and modal choice of trips. These characteristics depend on, among other factors, the frequency and length of the trips. This fact segments the demand into urban travel demand, such as short-distance daily commuting, and long-distance travel demand, such as overnight trips or tourism trips. Different distance thresholds are used to distinguish between the two demand segments, although long-distance trips gen- erally exclude recurrent commuting trips. Traditionally, transport modelers have paid more atten- tion to daily short-distance urban traffic demand and its cor- responding models, since these trips are much higher in number than long-distance trips. Models for long-distance trips appeared later and usually transferred parameters from urban models. Some studies have started to highlight the importance of the long-distance travel demand market (sometimes mixed with the so-called intercity travel demand) based on its contribution to the vehicle-kilometers traveled (1), or motivated by the interest in analyzing high-speed rail networks. The development of statewide models also required specific modules for long-distance trips, which jus- tified the need for their improvement. Data availability is reported generally as the major concern for long-distance models (2), since household travel surveys are typically designed for urban travel demand. This paper shows the development of a long-distance pas- senger travel demand model for the province of Ontario, Canada, as part of a province-wide transportation model. The paper analyzes the limitations of survey data regarding non- selected travel alternatives, and the acquisition of additional datasets using location-based big data and online trip plan- ning services. Literature Review Compared with short-distance or daily trips, the number of research studies on long-distance travel demand is limited, probably because the number of long-distance trips per 777064TRR XX X 10.1177/0361198118777064Transportation Research RecordLlorca et al research-article 2018 1 Technical University of Munich, Munich, Germany 2 IVT, ETH Zürich, Zürich, Switzerland 3 PTV Group, Karlsruhe, Germany Corresponding Author: Address correspondence to Carlos Llorca: carlos.llorca@tum.de Estimation of a Long-Distance Travel Demand Model using Trip Surveys, Location-Based Big Data, and Trip Planning Services Carlos Llorca 1 , Joseph Molloy 2 , Joanna Ji 3 , and Rolf Moeckel 1 Abstract Long-distance trips are less frequent than short-distance urban trips, but contribute significantly to the total distance traveled, and thus to congestion and transport-related emissions. This paper develops a long-distance travel demand model for the province of Ontario, Canada. In this paper, long-distance demand includes non-recurrent overnight trips and daytrips longer than 40 km, as defined by the Travel Survey for Residents in Canada (TSRC). We developed a microscopic discrete choice model including trip generation, destination choice, and mode choice. The model was estimated using travel surveys, which did not provide data about destination attractiveness and modal level of service. Therefore, a data collection method was designed to obtain publicly available data from the location-based social network Foursquare and from the online trip planning service Rome2rio. In the first case, Foursquare data characterized land uses and predominant activities of the destination alternatives, by the number of user check-ins at different venue types (i.e., ski areas, outdoor or medical activities, etc.). In the second case, the use of Rome2rio data described the modal alternatives for each observed trip. Combining data from travel surveys, Foursquare, and Rome2rio, coefficients of the model were estimated econometrically. It was found that the Foursquare data on number of check-ins at destinations was statistically significant, especially for leisure trips, and improved the goodness of fit compared with models that only used population and employment. Additionally, Rome2rio mode-specific variables were found to be significant for mode choice selection, making the resulting model sensitive to changes in travel time, transit fares, or service frequencies.