Study of travellers’ preferences towards travel offer categories and incentives in the journey planning context

PLOS ONE RESEARCH ARTICLE Study of travellers’ preferences towards travel offer categories and incentives in the journey planning context Eva Malichová ID1*, Milan Straka ID1, Ľuboš Buzna1, Damiano Scandolari2, Mario Scrocca2, Marco Comerio2 1 Faculty of Management Science and Informatics, University of Žilina, Žilina, Slovakia, 2 Cefriel, Milano, Italy a1111111111 a1111111111 a1111111111 a1111111111 a1111111111 OPEN ACCESS Citation: Malichová E, Straka M, Buzna Ľ, Scandolari D, Scrocca M, Comerio M (2023) Study of travellers’ preferences towards travel offer categories and incentives in the journey planning context. PLoS ONE 18(4): e0284844. https://doi. org/10.1371/journal.pone.0284844 Editor: Charitha Dias, Qatar University, QATAR Received: January 27, 2023 Accepted: April 7, 2023 Published: April 26, 2023 Copyright: © 2023 Malichová et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: The dataset generated by the survey research and analysed during the current study is available in the Zenodo repository (https://doi.org/10.5281/zenodo. 4593471). Funding: This work was supported in part by Ride2Rail project financed from the Shift2Rail Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 881825. L.B. and M.S. were in part supported by project VEGA 1/0077/22 Innovative prediction methods for optimisation of * Abstract Nowadays, efforts to encourage changes in travel behaviour towards eco-friendly and active modes of transport are intensifying. A promising solution is to increase the use of sustainable public transport modes. Currently, a significant challenge related to this solution is the implementation of journey planners that will inform travellers about available travel solutions and facilitate decision-making by using personalisation techniques. This paper provides some valuable hints to journey planner developers on how to define and prioritise the travel offer categories and incentives to meet the travellers’ expectations. The analysed data were obtained from a survey conducted in several European countries as part of the H2020 RIDE2RAIL project. The results confirm that travellers prefer to minimise travel time and stay on time. Also, incentives such as price discounts or class upgrades may play a crucial role in influencing the choices among travel solutions. By applying the regression analysis, it was found that preferences of travel offer categories and incentives are correlated with some demographic or travel-related factors. The results also show that subsets of significant factors strongly differ for particular travel offer categories and incentives, what underlines the importance of personalised recommendations in journey planners. 1 Introduction Efficient public transport is one of the promising solutions for sustainable mobility [1]. Together with other sustainable modes of transportation (walking, cycling, micro-mobility options, shared transport services), it encourages multimodality and brings several positive effects such as congestion reduction, decarbonisation, physical health improvement, but also societal impacts such as increasing access to life opportunities, easing integration into society and many others [2, 3]. Although a big emphasis is currently placed on using these forms of transport, Europeans still prefer to travel mainly by private motorised vehicles [4]. It is evident that, in addition to promoting sustainable transport, it is necessary to focus on creating solutions that will facilitate people’s transition from private motorised vehicles to sustainable transport. One such solution is a multimodal journey planner. PLOS ONE | https://doi.org/10.1371/journal.pone.0284844 April 26, 2023 1 / 25 PLOS ONE public service systems, APVV-19-0441 Allocation of limited resources to public service systems with conflicting quality criteria and in part by the Operational Program Integrated Infrastructure 2014-2020 Innovative Solutions for Propulsion, Power, and Safety Components of Transport Vehicles" through the European Regional Development Fund under grant ITMS313011V334. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. Study of travellers’ preferences towards travel offer categories and incentives Multimodal transport is recognised as a key element of sustainable transport, as it takes advantage of combining modes of transport [5]. Although several journey planners across Europe have been developed, many have focused only on one mode of transport or have been able to plan trips only within a particular geographical area [6]. Thus, if travellers want to plan a trip across several regions or countries, they must combine multiple journey planners, which makes trip planning difficult. Modern journey planners should provide travellers with relevant available travel solutions combining different modes of transport while considering their preferences, needs, and other factors. However, due to the consideration of multiple possible transport modes and other criteria, journey planners can overwhelm the traveller with a large number of suitable travel solutions. Hence, journey planning might be a complex decisionmaking situation with a plethora of influence factors and relevant criteria. One of the ways to address this problem and provide a comprehensible and straightforward way of presenting travel solutions is the usage of the categorisation technique, often applied in recommender systems. Categorisation, in this case, is understood as assigning a specific label (e.g., cheap) to travel offers based on their characteristic properties (e.g., low price). When presented to the travellers, this travel offer label should facilitate their decision-making process, as they will be able to recognise faster travel offers that match their needs. Furthermore, categories can be used as features describing travel offers in various machine learning tasks (e.g., building a filter that will rank travel offers based on previous choices of a given or similar traveller). Incentivisation is another tool used in the recommender system that a service provider can use to affect decisions taken by travellers. It is possible to use various incentives to motivate travellers to modify their original travel decisions for some reward, whether financial or nonfinancial. In the context of journey planning, incentives can represent an important mechanism for changing initial travel decisions into more sustainable ones. Here, we are concerned with the question: How should we define categories and incentives and which of them c (...truncated)