Travel satisfaction with public transport: Determinants, user classes, regional disparities and their evolution

Abenoza, Roberto F.; Cats, Oded; Susilo, Yusak O.

doi:10.1016/j.tra.2016.11.011

Cited by 161 publications

(147 citation statements)

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“…Furthermore, these determinants may vary depending on user, trip and mode characteristics. For example, researchers found that feeling safe while waiting was more important for women than men (Susilo and Cats 2014) while ride comfort was found important for students (Abenoza et al 2017) and the elderly (Dell'Olio et al 2011). Considering transport modes, Iseki and Smart (2012) found that information was of relevance for heavy-rail users while accessibility in and to the station together with reliability of the service were more significant to bus and light rail users.…”

Section: Literature Reviewmentioning

confidence: 99%

“…While commuting trips are repetitive, exposed to rush hour crowding and dominated by temporal constraints, noncommuting trips are less frequent and are characterized by a more flexible travel time budget (Li 2003). Some researchers postulate that frequent travelers (Abenoza et al 2017) and those with a seasonal ticket (and thus it is assumed frequent users) are overall more satisfied (Woldeamanuel and Cygansky 2011) because their trips are more stable and less exposed to salient events (Suzuki et al 2014). In contrast, other findings indicate that regular users are more exposed to negative critical incidents than occasional users and thereby their satisfaction will be lower ( Van't Hart 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

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How does travel satisfaction sum up? An exploratory analysis in decomposing the door-to-door experience for multimodal trips

2018

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Understanding how satisfaction with individual trip legs aggregates to the overall travel experience for different types of trips will enable the identification of the trip legs that are most impactful. For this purpose we analyze data on retrospective evaluations of entire multi-modal trip experiences and satisfaction with individual trip legs. We formulate and describe alternative aggregation rules and underpin them in theory and previous empirical findings. The results of a series of regression models show that for a large number of multi-modal trip configurations normative rules can better reproduce overall travel satisfaction than heuristic rules. This indicates that all trip legs need to be considered when evaluating the overall travel experience, especially for trips legs involving waiting and/or transferring time. In particular, weighting satisfaction with individual trip legs with perceived trip leg durations yielded the best predictor of overall travel satisfaction. No evidence for a disproportional effect of the last or most exceptional part of the trip was found. However, a larger dataset would be needed in order to replicate this work and potentially generalize the results. This research contributes to the literature on combining multi-episodic experiences and provides novel empirical evidence in the transport domain.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

How does travel satisfaction sum up? An exploratory analysis in decomposing the door-to-door experience for multimodal trips

2018

Self Cite

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“…Meanwhile, in the transportation planning literature, transportation services were believed to have an impact on peoples’ travel happiness and ultimately, on their overall happiness (Chen et al., 2019; Ye & Titheridge, 2019). In fact, while academic interests in travel happiness are relatively new (Chen et al., 2019; Singleton, 2019), over the last decade (see De Vos, 2019b; De Vos, Schwanen, Van Acker, & Witlox, 2013; Ettema, Gärling, Olsson, & Friman, 2010), studies have extensively explored various happiness determinants (Chen et al., 2019), focusing on trip and traveler characteristics (Zhu & Fan, 2018a, 2018b) although other dimensions such as the built environment and climate were also investigated (Abenoza, Cats, & Susilo, 2017; De Vos, Ettema, & Witlox, 2019; Park, 2017; Park & Kim, 2017). Frequently analyzed variables include travel modes (Morris, 2015; Morris & Guerra, 2015; Singleton, 2019; St‐Louis, Manaugh, van Lierop, & El‐Geneidy, 2014; Zhu & Fan, 2018b)—those for active travel, including walk and bike [but not for commute (Singleton, 2019)], and public transit were often found as the happiest and unhappiest modes, respectively, although the reciprocal relationship between mode choice and travel happiness was also conceptually acknowledged (De Vos, 2019b; De Vos et al., 2013; De Vos & Witlox, 2017)—activities/experiences on the way to the destination (De Vos et al., 2013; Ettema, Friman, Gärling, Olsson, & Fujii, 2012), presence/characteristics of companions (De Vos, 2019a; Zhu & Fan, 2018b), trip purposes (in relation to the compulsion level of the trip) (De Vos, Mokhtarian, Schwanen, Van Acker, & Witlox, 2016; Mokhtarian, Papon, Goulard, & Diana, 2015; Zhu & Fan, 2018b) and frequencies (Zhu & Fan, 2018a), and weather conditions at the time of the trip (Ettema, Friman, Olsson, & Gärling, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

The relationship between overall happiness and perceived transportation services relative to other individual and environmental variables

Gim

2020

Growth and Change

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Studies on happiness determinants have mainly focused on individual characteristics, but recently, the focus was extended to public services. Regarding the role of transportation services, however, few discussions exist on the overall happiness beyond the transitory happiness from a single trip, which differs by the mechanical characteristics of the transportation means, and particularly, those in non-Western areas are very few. This study analyses the perceived transportation services-happiness relationship using a social survey of 46 thousand citizens in Seoul, South Korea. How transportation services contribute to happiness is discussed in relation to the auxiliary travel utility through activities/ non-activities on the way to the destination and to the intrinsic utility, which is generated by satisfying mobility instincts, in addition to the derived utility by those activities that are conducted at the destination, the traditional focus in the transportation literature. An analytical model based on partial least squares structural equation modeling estimates the relationship between happiness and the factor of perceived transportation services while controlling for seven factors. Analytical findings are that except for gender, the significance and magnitude of each of the indicators are comparable to those reported in previous studies and as expected, perceived transportation services significantly encourage happiness. Specifically, satisfaction with 714 | GIM major determinants of happiness on life situation (sociodemographics), attitudes/intentions, behavior, environmental conditions, leisure amenities, and urban risks. The Seoul Survey is conducted every year, but the 2009 version uniquely asked about fitness and diet, both of which are known to affect happiness (Lee & Jung, 2013). Thus, this study used the 2009 data in particular. Data on research variables are available online: https://drive.google.com/open?id=1zjt6 Qait_yniJy tPWWF ztoVe sH4k-w0Gb. A figure for the structure of the relationships between respective factors and their indicator variables will be given later.The remainder of the paper is structured as follows. The next section will provide a review of the literature on the definition and measurement of happiness followed by its determinants including transportation services. Then, the paper will present research variables and their data for the empirical analysis. Analytical results will be separately given for indicator-factor relationships and factor-factor relationships. The paper will conclude with a discussion on research findings and their differences from those conducted on Western areas and the relative importance of transportation service indicators and its policy implications.

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“…The evaluation and assessment of the quality of transport offered is a challenging topic that has actracted many studies in the last years [1][2][3]. In addition, it is a crucial step in decisional processes, since policy makers and transport authorities are expected to assess the quality of all services.…”

Section: Introductionmentioning

confidence: 99%

“…Limited literature can be found on this topic, since it seems to be a rather innovative and challenging issue. A recent work provides an interesting review on useful techniques for assessing the heterogeneity in travellers' evaluation of public transport provision through the use of market segmentation techniques [3]. As detailed by the authors, various approaches coming from data mining and machine learning can be used to stratify the samples, such as "correspondence analysis, decision tree algorithms discriminant analysis, MNL [Multinomial Logit Model], and cluster analysis".…”

Section: Introductionmentioning

confidence: 99%

Comparing Transport Quality Perception among Different Travellers in European Cities through Co-Cluster Analysis

Pirra

Pensa

2019

Sustainability

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The quality of the transport system offered at city level constitutes an important and challenging goal for society, for local authorities, and transport operators. Therefore, appropriate evaluation of travellers' satisfaction is required to support service performance monitoring, benchmarking, and market analysis. This aspect implies the collection of satisfaction levels for different passengers' groups, as it could provide interesting suggestions for identifying priority areas of action. To this end, an original study aimed at understanding the main aspects affecting the common view of satisfaction among different kinds of travellers at European level is presented in this paper. A specific survey investigating how travellers perceive the quality of their journey is proposed to people living in cities characterised by different sizes. Data are then analysed through a multi-view co-clustering algorithm, an innovative machine learning technique that highlights clusters of respondents grouped according to various categories of features. Such results could be used by local authorities and transport providers to understand the specific actions to be operated to improve the quality of transport service offered in a market segmentation dimension.

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Travel satisfaction with public transport: Determinants, user classes, regional disparities and their evolution

Cited by 161 publications

References 39 publications

How does travel satisfaction sum up? An exploratory analysis in decomposing the door-to-door experience for multimodal trips

How does travel satisfaction sum up? An exploratory analysis in decomposing the door-to-door experience for multimodal trips

The relationship between overall happiness and perceived transportation services relative to other individual and environmental variables

Comparing Transport Quality Perception among Different Travellers in European Cities through Co-Cluster Analysis

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