Preferences for long-distance coach transport: Evidence from a discrete choice experiment

Acker, Véronique Van; Kessels, Roselinde; Cuervo, Daniel Palhazi; Lannoo, Steven; Witlox, Frank

doi:10.1016/j.tra.2019.11.028

Cited by 8 publications

(8 citation statements)

References 46 publications

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“…We did this to limit the cognitive burden of the respondents. The levels of the other four attributes differed (Kessels et al, 2017;Van Acker et al, 2020). We determined these using the Fedorov Casier and Witlox An Analysis of Trip Preferences among E-bike Users in Commuting: Evidence from an Online Choice-based Conjoint Experiment algorithm, which selects the 16 profiles that represent the most optimal set of profiles in a CBC and generates a D-Optimal design (Fedorov, 1972).…”

Section: Methodsmentioning

confidence: 99%

“…The third reason was that the model allowed the inclusion of the none-of-these option. This made it easier to estimate the coefficients and to give a better predictive fit than not including it or using a nested logit model (Haaijer et al, 2001;Lannoo et al, 2018;Van Acker et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

“…We assumed normally distributed preference parameters without correlations between attributes, which is a common practice in choice modelling (Balbontin et al, 2019;Hou et al, 2019;Van Acker et al, 2020). These random parameters accommodate unobserved heterogeneity in the respondents' preferences.…”

Section: Methodsmentioning

confidence: 99%

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Analysis of Trip Preferences among E-bike Users in Commuting: Evidence from an Online Choice-based Conjoint Experiment

Casier

Witlox

2022

European Journal of Transport and Infrastructure Research

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The present study reports on the preferences among e-bike users for commuting trips. An online survey with sixteen choice-based conjoint questions was conducted in March 2020 amongst 144 e-cyclists and non e-cyclists in Flanders (Belgium). Their choices were analysed using a no-choice binomial logit model. The impact of the following factors showed to be most important: weather conditions, trip time, type of e-bike, and financial support by the employer. The effect on e-bike users of factors such as cycling infrastructure, secure parking, and shower facilities at the workplace seems more limited. Based on the difference between male and female respondents, trip time and type of e-bike differed significantly. The only significant difference between e-bike owners and non e-bike owners was the type of e-bike. Trip time, weather conditions and financial intervention differed significantly between the young and old age group. Our findings suggest that policymakers should focus on investments in e-bikes, such as cycling infrastructure, mileage allowance and facilities at the workplace to make this a sustainable and socially inclusive mode of transport.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Analysis of Trip Preferences among E-bike Users in Commuting: Evidence from an Online Choice-based Conjoint Experiment

Casier

Witlox

2022

European Journal of Transport and Infrastructure Research

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“…For long-distance travel-based on work by Limtanakool et al [18] for the car, Creemers et al [10] for light rail, Vanoutrive et al [34] for rail, Lannoo et al [16] and Van Acker et al [31] for bus/coach travel, Aparicio [1] for car and air travel, and Lepage [17] for rail and air travel-three common mode determining variables are important: (1) travel time, (2) travel cost, and (3) overall comfort/convenience. In most cases, the longer the travel time, the less likely this mode will be chosen.…”

Section: Factors Influencing Long Distance Modal Choicementioning

confidence: 99%

Changing tracks: identifying and tackling bottlenecks in European rail passenger transport

Witlox

Zwanikken

Jehee

et al. 2022

Eur. Transp. Res. Rev.

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For Europe's urban agglomerations to be economically competitive, it is vital that international destinations be easily accessible. Although much has been invested in the construction of European rail infrastructure over the past century, passenger transport by rail has not grown as fast as transport by road and air. So why do people not use international trains more, even though they have an extensive international rail network at their disposal? Based on a series of in-depth interviews with relevant public and private stakeholders and two expert meetings, we identify the main bottlenecks and constraints. In order to understand the complexity of international rail transport, we have divided the existing bottlenecks into four groups corresponding to four layers of the rail transport system: mobility services, transport services, traffic services, and the physical and digital infrastructure. We formulate concrete policy recommendations for improvements to be made in the various components of the rail transport system.

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“…The mode choice model varies with demographics, socioeconomic and geographic conditions. Two of the most significant factors in choosing among available alternative travel modes are found to be the adequacy of transportation infrastructure and level of service [16].…”

Section: Introductionmentioning

confidence: 99%

Modeling the Behavior in Choosing the Travel Mode for Long-Distance Travel Using Supervised Machine Learning Algorithms

Momin¹,

Barua²,

Hamim³

et al. 2022

Komunikácie

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The long-distance travel (LDT) mode choice modeling is important for transportation planners. This study investigated alternative mode choice behavior for the LDT between the intercity buses and trains. A questionnaire survey, consisting of important mode choice attributes, was conducted on various groups of people in Bangladesh. Numerous travel mode choice contributing features (e.g., travel time, travel costs, origindestination, comfort, safety, travel time reliability, ticket availability and schedule flexibility) were considered and the LDT mode choice models were developed using various machine learning algorithms typically applied for classification problems. With 95.31 % accuracy and 0.95 F1-score, Random Forest model was the best performing model for the dataset. According to the findings of this study, the intercity bus is preferred over the intercity train for LDT in Bangladesh.

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Preferences for long-distance coach transport: Evidence from a discrete choice experiment

Cited by 8 publications

References 46 publications

Analysis of Trip Preferences among E-bike Users in Commuting: Evidence from an Online Choice-based Conjoint Experiment

Analysis of Trip Preferences among E-bike Users in Commuting: Evidence from an Online Choice-based Conjoint Experiment

Changing tracks: identifying and tackling bottlenecks in European rail passenger transport

Modeling the Behavior in Choosing the Travel Mode for Long-Distance Travel Using Supervised Machine Learning Algorithms

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