Transportation and Technology in Rural Denmark: Communities of Mobility

Kjærup, Maria; Skov, Mikael B.

doi:10.1145/3520495.3520498

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…It is quite interesting to note that if the sample interviewed is small, quality of insights increases, but representativeness of the sample can lead to unexpected and non-transferable outcomes. Kjaerup [16] interviewed 19 people living or travelling in Denmark and also found that private cars are the preferred mode; however, residents showed a propensity towards multimodal transport and considered also the suitability of Mobility as a Service in rural area.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Most of the studies concentrate on analysing operational characteristics of the transit system serving towns [15], using qualitative methods such as focus group interviews [16], co‐creation methods through a foresight future exercise [17], or structured interviews [18], which allow us to engage directly with users to identify daily mobility issues and possible interventions. Limitations of this approach are linked to the sample composition, where achieving a good balance of individuals in terms of age, gender, income, car availability proves difficult.…”

Section: Introductionmentioning

confidence: 99%

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Introduction of emerging mobility services in rural areas through the use of mobile network data combined with activity‐based travel demand modelling

Franco

Kaba

et al. 2023

IET Intelligent Trans Sys

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Whilst urban areas are thriving in trialling new mobility services (NMS), rural environments, often perceived as areas of low demand for travel, struggle to attract investments for creating more mobility solutions alongside traditional public transport (PT) services, making residents more reliant on private cars.This paper describes how policy interventions for introducing NMS in rural areas should be guided by big data to capture real and accurate travel behaviours, therefore avoiding perceived biases and potentially underestimating demand.In the UK, the provision of transport in rural areas is solely linked to population density and does not consider differences between places and residents’ travel habits. The proposed data‐driven decision‐making process used trip‐chains from mobile network data (MND) to derive recent and accurate travel patterns from residents and provide the right mix of on‐demand mobility services alongside existing fixed scheduled public transport (PT).The manuscript describes the steps carried out to study three rural areas at low, medium and high population density in the UK: a data landscape to select study areas; the development of an activity‐based model, which uses anonymised mobile network data (MND) aggregated at trip‐chains level to derive travel patterns; and the development of an on‐line questionnaire and focus groups with rural communities to co‐designing solutions based on attitudes towards NMS.Results demonstrated that a data‐driven decision making process to introduce NMS is a viable solution for updating demand for travel in rural areas, offering a broad understanding of mobility needs and the relationship of interdependency with nearby areas, therefore allowing policy makers to create users‐centric transport solutions.The study concludes by drawing recommendations for NMS for passengers and goods for the NMS proposed for a rural areas [Demand Responsive Transport (DRT), Micro‐mobility and delivery drones].

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Introduction of emerging mobility services in rural areas through the use of mobile network data combined with activity‐based travel demand modelling

Franco

Kaba

et al. 2023

IET Intelligent Trans Sys

View full text Add to dashboard Cite

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Opportunities and Challenges for Designing Interactive Applications for Coffee Producers in the Countryside of Brazil

Memento,

Santos,

Marques

et al. 2024

Proceedings of the XXIII Brazilian Symposium on Human Factors in Computing Systems

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Urban (UMaaS) and rural (RMaaS) mobility as a service (MaaS): practical insights from international practitioners and experts

Milne,

Beecroft,

Nelson

et al. 2024

Eur. Transp. Res. Rev.

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The concept and potential of Mobility as a Service (MaaS) has been the subject of significant debate in academia and industry for nearly a decade. There are several city-based Mobility as a Service (MaaS) pilots globally. There remains a significant lack of practitioner evidence of MaaS with the focus on city-based solutions rather than in rural and suburban areas. To that end, this paper asks four research questions to contribute to the gap in Rural MaaS (RMaaS) and Urban MaaS (UMaaS); firstly, is MaaS a mobility option for rural areas given the identified evidence in scientific literature? Secondly, how do practitioner experiences with MaaS (in all areas) differ considering factors like phraseology, geography, available modes, transportation, the origin and implementation stages? Thirdly, what practical learnings can be drawn from practitioners in the field? Fourthly, what is the future of MaaS for rural and urban areas. The research and findings are based upon grey literature and twenty semi-structured interviews with representatives from research or government organisations, public bodies, MaaS technology suppliers, transport operators and experts. Each participant discussed and contributed to the practicalities around real-world applications of MaaS in urban, regional, or rural areas. The analysis produced 2 applied tools which will be useful to practitioners interested in MaaS; a Thematic Map visualising the common matters emerging from the interviews revolving around ‘People, Policy, Practice and Pilots (4Ps); a Practical Framework for Implementing MaaS tool, which can be used by any practitioner at any stage of a MaaS project.

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Transportation and Technology in Rural Denmark: Communities of Mobility

Cited by 3 publications

References 12 publications

Introduction of emerging mobility services in rural areas through the use of mobile network data combined with activity‐based travel demand modelling

Introduction of emerging mobility services in rural areas through the use of mobile network data combined with activity‐based travel demand modelling

Opportunities and Challenges for Designing Interactive Applications for Coffee Producers in the Countryside of Brazil

Urban (UMaaS) and rural (RMaaS) mobility as a service (MaaS): practical insights from international practitioners and experts

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