Introduction of emerging mobility services in rural areas through the use of mobile network data combined with activity‐based travel demand modelling
Abstract 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 r...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-08-01
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| Series: | IET Intelligent Transport Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/itr2.12339 |
| _version_ | 1797739585595244544 |
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| author | Patrizia Franco Djibril Kaba Steve Close Shyma Jundi |
| author_facet | Patrizia Franco Djibril Kaba Steve Close Shyma Jundi |
| author_sort | Patrizia Franco |
| collection | DOAJ |
| description | Abstract 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]. |
| first_indexed | 2024-03-12T14:00:20Z |
| format | Article |
| id | doaj.art-f7e0d83bd0204218a0bfd99bc0f43c69 |
| institution | Directory Open Access Journal |
| issn | 1751-956X 1751-9578 |
| language | English |
| last_indexed | 2024-03-12T14:00:20Z |
| publishDate | 2023-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Intelligent Transport Systems |
| spelling | doaj.art-f7e0d83bd0204218a0bfd99bc0f43c692023-08-22T07:47:26ZengWileyIET Intelligent Transport Systems1751-956X1751-95782023-08-011781509152410.1049/itr2.12339Introduction of emerging mobility services in rural areas through the use of mobile network data combined with activity‐based travel demand modellingPatrizia Franco0Djibril Kaba1Steve Close2Shyma Jundi3Modelling and Appraisal Connected Places Catapult Milton Keynes United Kingdom of Great Britain and Northern IrelandModelling and Appraisal Connected Places Catapult Milton Keynes United Kingdom of Great Britain and Northern IrelandModelling and Appraisal Connected Places Catapult Milton Keynes United Kingdom of Great Britain and Northern IrelandModelling and Appraisal Connected Places Catapult Milton Keynes United Kingdom of Great Britain and Northern IrelandAbstract 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].https://doi.org/10.1049/itr2.12339data acquisitiondata analysisdata miningdecision makinghuman factorsmulti‐agent systems |
| spellingShingle | Patrizia Franco Djibril Kaba Steve Close Shyma Jundi Introduction of emerging mobility services in rural areas through the use of mobile network data combined with activity‐based travel demand modelling IET Intelligent Transport Systems data acquisition data analysis data mining decision making human factors multi‐agent systems |
| title | Introduction of emerging mobility services in rural areas through the use of mobile network data combined with activity‐based travel demand modelling |
| title_full | Introduction of emerging mobility services in rural areas through the use of mobile network data combined with activity‐based travel demand modelling |
| title_fullStr | Introduction of emerging mobility services in rural areas through the use of mobile network data combined with activity‐based travel demand modelling |
| title_full_unstemmed | Introduction of emerging mobility services in rural areas through the use of mobile network data combined with activity‐based travel demand modelling |
| title_short | Introduction of emerging mobility services in rural areas through the use of mobile network data combined with activity‐based travel demand modelling |
| title_sort | introduction of emerging mobility services in rural areas through the use of mobile network data combined with activity based travel demand modelling |
| topic | data acquisition data analysis data mining decision making human factors multi‐agent systems |
| url | https://doi.org/10.1049/itr2.12339 |
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