Page 168 - 360.revista de Alta Velocidad - Nº 6

Coronado, José María. Moyano, Amparo. Romero de Ávila, Vicente.
Rodríguez Lázaro, Francisco Javier. Ruiz Fernández, Rita.

station design as an intermodal node is also a relevant issue. The location of the platforms,
park&ride spaces, parking, bus stops, etc. are key elements on the internal efficiency of the
building (Tapiador et al., 2009; Menéndez et al., 2002). Although there are specific case studies
which provide modal share data of access to and from the station (Menéndez, et al., 2002),
there are no studies about the quality of the pedestrian link from the station to the city center.
Although there are no systematic studies about the effects of HSR on tourism (Coronado, et al.,
2012), scholars coincide on the positive impacts of HSR effects on local tourism (Bazin, et al.,
2011; Masson & Petiot, 2009; Van den Berg & Pol, 1998). The possibilities of urban tourism also
depend on the local strategies which aim to take advantage of the new accessibility provided
by HSR and the centrality generated by the station, including planning, management and
promotional strategies (Ribalaygua, 2005; Bellet, et al., 2012). Planning strategies are related
to the integration of the station and the coordination between the HSR project and the city/
regional planning. Management strategies aim to make the most of the station’s vocation for
centrality either as a modal exchange centre or as a developer of the surrounding land. Finally,
promotional measures have focused on urban marketing campaigns, trying to recruit economic
activity, building amenities and service facilities for congresses and meetings, and linking the
image of the city to the modernity associated to HSR.

In fact, cities try to make the most of the HSR station, linking it to concepts such as centrality
and/or modernity, but often, when a tourist arrives to the station finds it hard to get to the
city center, especially if it is a novel destination and the station is not central. No strategies or
policies have been found to make comfortable and legible itineraries between the HSR station
and the city center, with walkable and pleasant promenades for city-edge stations.

2.2 Locating tourists using GPS

Assessing tourists’ behavior in cities is not an easy task. While it is easy, or at least possible, to have
accurate data about the temporal distribution and number of visitors to some cultural amenities
like museums and other monuments where it is necessary to buy a ticket to enter, it is not so
easy to know their physical distribution or which itineraries they use around the city in both open
spaces and in the street network. Tourists blend with city inhabitants and it is not always easy to
differentiate them when using cameras or street counting/audits (Ruiz-Apilanez et al., 2015).

GPS are a powerful tool to analyze this behavior as it is possible to register the location of the
tourist at every moment. However, there are some restrictions. First, GPS data loggers have
a limit in the duration of their batteries, so the visits must not be too long. An interesting
alternative is to use smartphones’ GPS, but in this case it is necessary to previously contact the
users whose behavior is going to be analyzed so they download the Apps to register the tracks,
and this is nearly impossible with tourists (Marmolejo & Chaves Custodio, 2016). Second, it
is also necessary to have access to the tourist twice in order to give him/her the GPS device
and to reclaim it at the end of the day. GPS have been used with cruise visitors taking one-day
visits to cities (Ferrante, et al., 2016) that are identified when exiting and returning to the
ship, or parks visitors (Santos, et al., 2016) that can be provided with the GPS device at the
entry of the amenity (Beecoa, et al., 2014) (Zheng, et al., 2017). Third, the number of tracks
that can be registered is limited by the number of GPS devices available, but on the other
hand, the amount of information that each track provide is large. The tracks can be mapped
depending of the objective of the study, but data usually include density of visitors, time-space
distributions, speeds, stopping points, entering and exiting, gates, etc. It is also possible to
elaborate statistical analysis of the tourists’ behavior: total distance walked, duration of the
visit, number of points of interest visited, etc. To overcome this difficulty, shared tracks in
social network sites like Endomondo, Strava, MapMyRide, Runtastic, Sports-or wikiloc can be
used (Mínguez García, et al., 2015) (B. & Nogueira Mendes, 2016), but only in very popular cities
or destinations that will assure a large number of tracks.

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