=Paper=
{{Paper
|id=Vol-2939/paper3
|storemode=property
|title=Towards a More Informed Multimodal Travel Shopping
|pdfUrl=https://ceur-ws.org/Vol-2939/paper3.pdf
|volume=Vol-2939
|authors=Mario Scrocca,Marco Comerio,Damiano Scandolari,Irene Celino
|dblpUrl=https://dblp.org/rec/conf/i-semantics/ScroccaCSC21
}}
==Towards a More Informed Multimodal Travel Shopping==
https://ceur-ws.org/Vol-2939/paper3.pdf
Towards a More Informed Multimodal
Travel Shopping
Mario Scrocca , Marco Comerio , Damiano Scandolari , and Irene Celino
Cefriel, Milan, Italy name.surname@cefriel.com
Abstract. Current travel planning applications provide only a limited
support to users for an informed selection of travel solutions. The cat-
egorization of travel offers contributes to solve this limitation comple-
menting basic information and creating awareness on the selection. This
paper reports the work done for the conceptualization of offer categories
and proposes a solution for enabling their usage in multimodal travel
shopping.
Keywords: Travel Planning · Travel Shopping · Offer Categories · Mul-
timodal Transportation
1 Introduction
Current travel planning applications support users in identifying existing
travel solutions to move from a place A to a place B, ordered according to
their main characteristics (i.e., price, length in Km, and duration in hours).
The categorization of travel offers could complement this basic information to
support a more informed travel shopping and to create awareness.
This paper reports the work done in the context of the Ride2Rail project1 ,
within the Shift2Rail IP4 innovation program2 , where the main scenario targets
a user looking for a door-to-door multimodal travel solution. To improve the user
experience and to create awareness about more sustainable solutions, Ride2Rail
aims to provide a conceptualization of offer categories as labels conferred to
particular characteristics of a travel offer (i.e., offer features).
Figure 1 describes the reference travel shopping process and how Ride2Rail
is enhancing it through the adoption of offer categories. The proposed diagram
is based on the Shift2Rail IP4 reference ontology [1] defined to overcome the
technical challenges of involving different Transport Service Providers (TSPs) in
the multimodal travel shopping flow. Through a Personal Application (PA), the
user creates a mobility request specifying origin, destination, expected date-time
Copyright © 2021 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
1
https://ride2rail.eu/
2
https://shift2rail.org/research-development/ip4/
2 M. Scrocca, M. Comerio, D. Scandolari and I. Celino
Fig. 1: Offer Categories in the Travel Shopping process.
departure/arrival and additional search options. The PA sends the generated
mobility request to the Travel Solution Aggregator (TSA) that is responsible for
providing a set of travel solutions by invoking multiple Travel Experts (TE). Each
TE, managed by a TSP or by an intermediary selling TSP solutions, may return
zero, one or more itinerary offer items, each defined as the relation between three
concepts: a travel episode (i.e., the segment of the itinerary supported by the
TSP), the product, and the passenger(s) involved. After receiving these itinerary
offer items from the different TEs, the TSA composes them in a set of travel
solutions that are then displayed on the user’s PA in the form of trips and offers.
Trips defined by the TSA are bound to one or more related offers (e.g., first class
and economy offers for the same trip). The described flow can be enhanced by an
additional component defined in Ride2Rail, the Offer Categorizer : an external
service used by the TSA to determine the offer categories associated with each
offer before sending them to the PA.
On the basis of the presented data model, the proposed conceptualization
aims at improving the user awareness in choosing among heterogeneous travel
solutions by defining a shared semantic for their categorization. The next sec-
tions describe the analysis of the state-of-the-art on the definition of offer cate-
gories, the conceptualization and the catalogue produced by Ride2Rail, the final
remarks and the next steps.
2 Analysis of the State-of-the-art on Offer Categories
From the analysis of the state-of-the-art, the following patterns to support the
Ride2Rail conceptualization of offer categories emerge:
– Types of Variables: contributions (e.g., [2,10]) dealing with the identifi-
cation of the types of variables that can be used to describe a multimodal
travel offer;
Towards a More Informed Multimodal Travel Shopping 3
– Actual Variables: contributions (e.g., [2,10,5,6,7,9,8]) dealing with the
identification of actual variables describing a multimodal travel offer that
can be used to define offer categories;
– Multiple Variables: contributions (e.g., [6,9,8]) dealing with the identifi-
cation of multiple variables that can be used to define offer categories con-
sidering different characteristics of a travel offer.
Integrating the models proposed in [2,10], it is possible to identify the fol-
lowing macro-areas to partition variables describing a multimodal travel offer:
– Instrumental: variables related to the measurable characteristics of the
travel solution (cost, time, etc. . . );
– Perception: variables related to the users’ perception while travelling (com-
fort, safety, etc. . . );
– Symbolic: variables related to the personal value attributed by a user to a
specific travel solution (prestige, status, etc. . . ).
The performed analysis of the state-of-the-art highlighted a set of actual
variables, belonging to the identified macro-areas. However, while instrumen-
tal variables are objective and easily measurable, the same does not hold true
for perception and symbolic variables. The difficulty of obtaining an unambigu-
ous definition of the concepts makes these factors harder to be appropriately
defined. Nevertheless, an objective quantification of perception variables could
be evaluated through feedback collected from an adequate statistical sample of
users, e.g., measuring the feeling of personal safety or the level of comfort. Sym-
bolic variables are more subjective and, for this reason, cannot be considered to
objectively characterize offers. Therefore, instrumental and perception variables
represent potential determinant factors for an offer category, i.e., the variables
that can be used to determine the membership of an offer to an offer category.
From the analysed state-of-the-art, we selected a list of actual variables de-
scribing an offer that can be used as determinant factors of low-level offer cate-
gories, i.e., classes that can be easily associated with an offer given its objective
characteristics. It is important to notice that an offer category should be assigned
relatively to the set of offers provided for a mobility request. For example, the
total travel time variable is the determinant factor for a low-level offer category
that minimizes the total travel time and identifies the quickest travel solution
among the ones available. The complete list of low-level Offer Categories identi-
fied and the related determinant factors is reported in [3].
The assignment of specific categories identified in the state-of-the-art analy-
sis, like comfortable or environmentally sustainable¸ is not straightforward and
not possible using a single variable describing an offer. For this reason, contri-
butions from [6,9,8] are extremely valuable to define offer categories determined
by multiple variables.
The identified patterns have been used to support the conceptualization of
the term offer category, while the preliminary analysis of variables and low-
level offer categories have been used to define a first catalogue of concrete offer
categories for multimodal travel offers (briefly described in Section 3 and fully
reported in [4]).
4 M. Scrocca, M. Comerio, D. Scandolari and I. Celino
3 Conceptualisation of Offer Categories
In this section, we report the conceptualization of the Offer Category term and
its related concepts.
– Offer Feature: An offer is described by a set of objective variables (such as
transportation mode, level of CO2 emissions, cost, etc.). The values assigned
to the objective variables for a specific offer identify its offer features. For
example, can be a feature of an offer. An
offer feature can be computed considering data provided by the TSP (e.g.,
the price), and/or additional data, such as information related to the trip(s)
associated with the offer in a travel solution (e.g., length in km), or to the
vehicle used in the offer (e.g., CO2 emissions).
– Offer Category: it identifies a set of offers having specific shared charac-
teristics. An offer is assigned to a given offer category considering a set of
offer features, namely the determinant factors for that offer category. The
membership of an offer to a given offer category is defined by a Category
Score (CS) in the range of [0,1], where 0 means “no membership”, and 1
indicates “full membership”.
– Offer Categorizer: a component offering a service that implements a set
of functions to compute the CS of an offer with respect to a set of given offer
categories. Different offer categorizers can be created with different charac-
teristics, e.g., adopting different strategies, and/or external data sources to
compute the offer features, or implementing different algorithms to compute
the CS.
A first catalogue of offer categories has been extracted from the state-of-
the-art analysis and subsequently framed considering the provided definitions.
The completeness of the catalogue and the interest in each proposed category
have been tested though a survey filled by 609 European travelers3 . Rather than
computing an exhaustive list of all the possible offer categories, the goal of this
catalogue is to elicit the ones that resulted from the survey as the most relevant
to provide a comprehensive clusterization of travel solutions obtained in response
to a mobility request. In the following, we report the final Ride2Rail catalogue
of offer categories describing the most relevant determinant factors for each of
them. The offer categories are ranked according to the relevance attributed by the
respondents of the survey. New offer categories, elaborated thanks to suggestions
collected though the survey (i.e., Panoramic and Healthy), are mentioned at the
end of the list since we cannot provide an estimation of the relative relevance
for the travellers.
– QUICK: The quick category measures how convenient and efficient the so-
lution is in terms of time-related issues, considering the total travel time,
the waiting time between legs and the number of stops required. If the solu-
tion includes a segment on-road (e.g., bus/car) and real-time data on traffic
congestion is available, it can be taken into account.
3
The complete description of the survey and its results are available in [4]
Towards a More Informed Multimodal Travel Shopping 5
– RELIABLE: The reliable category concerns the likelihood of delays, traffic
congestion, breakdowns or last-minute changes that could affect the travel
time and comfort of the trip. Some solutions are inherently variable (e.g.,
traffic delays when crossing a city at rush hour), while other solutions might
offer a small window to change the mean of transport that could cause mas-
sive idle times. For this reason, the frequency of the service for involved
solutions should be taken into account.
– CHEAP: The cheap category concerns the total price of a trip, the pos-
sibility of sharing part of it with others and the ease of payment, giving
additional value to solutions that offer an integrated fare system and do not
require the user to purchase different tickets from different platforms.
– COMFORTABLE: The comfortable category concerns objective factors,
such as the number of interchanges required or the possibility of having a
comfortable seat, but also covers a set of other elements evaluated through
users’ feedback. Relevant factors are the cleanliness of the stations and ve-
hicles used, and the feeling of personal safety.
– DOOR-TO-DOOR: The door-to-door category is associated with offers
that cover the first and the last mile of the mobility request. It is measured
by the amount of walking or driving distance the user has to cover.
– ENVIRONMENTALLY FRIENDLY: The environmentally friendly cat-
egory covers the green aspects of the trip, taking into account the amount of
CO2 emissions measured per kilometre/traveller for each mean of transport
included in the offer, and factoring in relevant data such as the distance
covered and the number of passengers. If available, additional determinant
factors can be considered, e.g., the energy consumption, the NOx emissions
and the carbon footprint.
– SHORT: The short category focuses on minimizing the distance covered.
– MULTITASKING: The multitasking category concerns the extent to which
the user can perform other tasks while travelling. These activities can regard
productivity (personal or work), fitness, or enjoyment. It takes into account
the amount of space available, the presence of business areas, internet con-
nection and/or plugs. Lastly, the level of privacy might also influence the
extent to which a person can work and could be considered as a determinant
factor for this category.
– SOCIAL: The social category concerns the maximization of the number of
people the user will share the trip with and the possibility to socialize based
on the context and means used. Moreover, it takes into account solutions
that contribute to social causes or involve volunteering or charity activities
(e.g., donations).
– PANORAMIC: The panoramic category promotes solutions passing through
beautiful landscapes (like a particular village or a forest) or historical sites.
This category also takes into account the usual sightseeing itineraries for
tourists to promote solutions passing near monuments or other interesting
spots.
– HEALTHY: The healthy category concerns the involvement of walking
and/or cycling in an offer.
6 M. Scrocca, M. Comerio, D. Scandolari and I. Celino
4 Conclusions and Next Steps
The state-of-the-art analysis has supported the identification of a set of pat-
terns considered for the conceptualization of the term Offer Category: (i) the
assignment of an offer to a given category should consider the specific values
assumed by each variable that describes the offer and the associated trip; (ii)
offer categories should be assigned considering a set of objective variables of the
offer and should not be conditioned by the characteristics and preferences of a
specific user; (iii) offer categories can be defined considering multiple variables
of the offer. The described conceptualization and the provided catalogue of offer
categories is guiding the next step of the Ride2Rail project: the implementation
of an offer categorizer component and its testing in four demo sites.
As future work, the definition of a vocabulary (thesaurus and/or ontology)
for offer categories based on the proposed conceptualization would support the
provision of enriched interoperable descriptions of travel solutions.
Acknowledgments
The presented research was supported by the RIDE2RAIL project (Grant Agree-
ment 881825), co-funded by the European Commission under the Horizon 2020
Framework Programme.
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