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<dc:title>New unconventional sources of information for transport analysis. The case of a model for public transport services in Majorca</dc:title>
<dc:creator>Martínez-Alvaro, Oscar</dc:creator>
<dc:creator>Hernández Santana, Iván</dc:creator>
<dc:subject>Modelización</dc:subject>
<dc:subject>Simulación</dc:subject>
<dc:subject>Transporte público</dc:subject>
<dc:subject>Modelling</dc:subject>
<dc:subject>Simulation</dc:subject>
<dc:subject>Public transport</dc:subject>
<dcterms:abstract>Most transport modeling requires the use of data available in official sources plus&#xd;
complementary information that helps to fill gaps. This later information is mostly obtained&#xd;
with the help of private data and field data collection. But sometimes this field data collection&#xd;
is not possible for several reasons. On the other hand, there are more and more sources of&#xd;
information readily available, which may provide indirect clues about users’ behavior.&#xd;
Within the framework of a bidding process, a modelling of Majorca’s public transport was&#xd;
carried out in order to ascertain elasticities of demand to several parameters of supply that&#xd;
were expected to change. For that purpose, there appeared a need of getting detailed&#xd;
information on tourist activities, which are a huge share of total mobility in summertime.&#xd;
Official information on population is rather detailed, but tourism data are available only on&#xd;
an aggregated basis.&#xd;
Some indirect sources of information were analyzed. The easiest and most useful ones were&#xd;
TripAdvisor and Booking. The former is sort of a macro-survey of popularity and it seems&#xd;
logical that those nuclei with more opinions are those most visited, thus acting as an indicator&#xd;
of attraction. The latter provides information on location of accommodation offered, thus&#xd;
providing a tool for the geographical distribution of trip generation on a very detailed basis.&#xd;
In total, 933,934 opinions for 198 nuclei from TripAdvisor, and 2,114 accommodation assets&#xd;
for 51 Municipalities from Booking were used. With this information it was possible to&#xd;
calibrate different models for the whole Island, this opening the door to further analyses in&#xd;
the field of transportation and mobility.&#xd;
The Transport Consortium of Majorca (CTM), entity in charge of planning and management&#xd;
of scheduled public transport services on that island, published in March 2019 the call for&#xd;
tenders for all intercity public passenger transport services by bus. It opened a scenario of&#xd;
important changes (CTM 2017a and 2017b) that sought to increase the total demand of the&#xd;
intercity bus network by 25% between 2015 and 2028, by means of a substantial variation&#xd;
in the supply side (travel times, timetables, connectivity between lines, etc.). For example,&#xd;
an increase in supply between 35.8% and 73.1% of vehicle-km was planned, with different figures for each of the three packs into which the tender was divided. In addition, a new fare&#xd;
system was introduced, with a very complex structure that imposed a heavy surcharge when&#xd;
purchasing the ticket on the bus. However, no technical study with more information on the&#xd;
expected demand figures was released.&#xd;
Under these circumstances, any projection of the historical data was irrelevant and one of&#xd;
the bidding companies, which also operated a good part of the existing services, felt the need&#xd;
of calculating the expected demand with scientific tools.&#xd;
The analysis of the problem in question faced, fundamentally, an important lack of detail of&#xd;
the available information:&#xd;
 As for transport supply or demand, the official information was limited to total&#xd;
figures by line (CTM, 2017 and “Open Data” site).&#xd;
 In terms of demand factors, the greatest disaggregation available in official sources&#xd;
(ibestat) was at municipal level.&#xd;
The following sections focus on the cross-section analysis of the general services, without&#xd;
dealing with other issues analyzed at the time, such as the new lines to the airport and the&#xd;
time-series modeling.</dcterms:abstract>
<dcterms:dateAccepted>2022-09-19T11:07:18Z</dcterms:dateAccepted>
<dcterms:available>2022-09-19T11:07:18Z</dcterms:available>
<dcterms:created>2022-09-19T11:07:18Z</dcterms:created>
<dcterms:issued>2021-07</dcterms:issued>
<dc:type>info:eu-repo/semantics/conferenceObject</dc:type>
<dc:identifier>978-84-18465-12-3</dc:identifier>
<dc:identifier>http://hdl.handle.net/10259/6919</dc:identifier>
<dc:identifier>10.36443/10259/6919</dc:identifier>
<dc:language>eng</dc:language>
<dc:relation>R-Evolucionando el transporte</dc:relation>
<dc:relation>http://hdl.handle.net/10259/6490</dc:relation>
<dc:relation>https://doi.org/10.36443/9788418465123</dc:relation>
<dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
<dc:publisher>1222</dc:publisher>
</qdc:qualifieddc></metadata></record></GetRecord></OAI-PMH>