Demand-Responsive Mobility for Rural Areas: A Review

Martí, Pasqual; Jordán, Jaume; Julián, Vicente

doi:10.1007/978-3-031-18697-4_11

Cited by 5 publications

(3 citation statements)

References 26 publications

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“…Demand-responsive public transportation solutions also use artificial intelligence to optimize on-demand bus services which operate under flexible schedule and routes for both the driver and the passenger [60][61][62]. Many examples of these innovative services are operative in different metropolitan areas [63,64].…”

Section: Demand-responsive Transportmentioning

confidence: 99%

Mobility Control Centre and Artificial Intelligence for Sustainable Urban Districts

Cirianni,

Comi,

Quattrone

2023

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The application of artificial intelligence (AI) to dynamic mobility management can support the achievement of efficiency and sustainability goals. AI can help to model alternative mobility system scenarios in real time (by processing big data from heterogeneous sources in a very short time) and to identify network and service configurations by comparing phenomena in similar contexts, as well as support the implementation of measures for managing demand that achieve sustainable goals. In this paper, an in-depth analysis of scenarios, with an IT (Information Technology) framework based on emerging technologies and AI to support sustainable and cooperative digital mobility, is provided. Therefore, the definition of the functional architecture of an AI-based mobility control centre is defined, and the process that has been implemented in a medium-large city is presented.

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Section: Demand-responsive Transportmentioning

confidence: 99%

Mobility Control Centre and Artificial Intelligence for Sustainable Urban Districts

Cirianni,

Comi,

Quattrone

2023

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“…The use of data-driven approaches and simulation to inform operation of commercial DRT services is less common compared to qualitative methods, capturing views of specific segments of the population [9][10][11][12][13][14].…”

Section: Literature Reviewmentioning

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

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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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“…Artificial intelligence (AI) introduces a multifaceted approach to address the dynamic transfer point allocation challenge in rural transportation, as detailed in recent research [5].…”

Section: Artificial Intelligence For Transfer Point Allocationmentioning

confidence: 99%

Optimization of Rural Demand-Responsive Transportation through Transfer Point Allocation

Martí,

Jordán,

De la Prieta

et al. 2023

Electronics

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Rural mobility has a lack of innovative proposals in contrast with its urban counterpart. This research aims to bring solutions that ease the implementation of reliable and flexible rural transportation. Demand-responsive transportation is chosen to develop a public transportation service providing interurban trips among several rural settlements. Given the characteristics of rural displacement demand, a novel approach is introduced to optimize the service’s economic costs: the dynamic transfer point allocation. The problem is fully formulated and an architecture is introduced describing the workflow of the whole system. Data from an interurban bus transportation service are used to build a case study of a rural area of Valencia, Spain, and develop several examples illustrating the benefits of the proposed approach. The results reveal that the dynamic creation of transfer points can simplify the transportation fleet’s itineraries and boost the amount of served travel requests. Finally, a discussion of the benefits and dangers of flexible features in rural transportation is developed, underscoring the need to achieve a balance between dynamic operation and service quality.

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Demand-Responsive Mobility for Rural Areas: A Review

Cited by 5 publications

References 26 publications

Mobility Control Centre and Artificial Intelligence for Sustainable Urban Districts

Mobility Control Centre and Artificial Intelligence for Sustainable Urban Districts

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

Optimization of Rural Demand-Responsive Transportation through Transfer Point Allocation

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