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Optimization of E-bike networks.

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Τίτλος: Optimization of E-bike networks.
Συγγραφείς: Belotti, Pietro, Errico, Fausto, Malucelli, Federico, Massetti, Antonio Tommaso
Πηγή: Transportation Planning & Technology; Oct2024, Vol. 47 Issue 6, p922-943, 22p
Θεματικοί όροι: Infrastructure (Economics), Renewable energy transition (Government policy), Electric automobiles, Scientific literature, Electric bicycles
Περίληψη: Battery-assisted bicycles, or E-bikes, are part of a disruptive wave of transportation technology that uses electricity and rechargeable batteries to increase the velocity, the traveled distance and, as a consequence, the ridership. Biking and E-biking are globally recognized to have the potential to play an important role in the transition to a Net-Zero society. The widespread availability of E-bikes is significantly impacting several sectors of the tourist industry. Therefore, Touristic Administrations (TAs) now provide tourists with trail options and the corresponding charging infrastructure for E-bikers with different profiles. Our main objective is to provide TAs with a suitable decision-support tool that serves two purposes: (1) finding locations for charging stations by considering the difficulty and the cost of installing such stations in remote, often off-the-road locations; and (2) designing itineraries that are suitable for different categories of E-bikers. In the scientific literature, the first decision component has been mostly addressed in the context of electric cars, and it is not suitable for E-bikes. On the other hand, works on the second decision focused on muscular bikes, thus ignoring the first decision component. In this paper, we aim at closing this gap. We formulate this problem as a mixed-integer linear program. We develop an efficient branch-and-cut algorithm and present a comprehensive computational experiment. In particular, we provide a case study in the Asiago Sette Comuni Plateau in Italy, where the obtained charging stations and bike trails maximize a measure of attractiveness for three types of users. [ABSTRACT FROM AUTHOR]
Copyright of Transportation Planning & Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
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  Data: Battery-assisted bicycles, or E-bikes, are part of a disruptive wave of transportation technology that uses electricity and rechargeable batteries to increase the velocity, the traveled distance and, as a consequence, the ridership. Biking and E-biking are globally recognized to have the potential to play an important role in the transition to a Net-Zero society. The widespread availability of E-bikes is significantly impacting several sectors of the tourist industry. Therefore, Touristic Administrations (TAs) now provide tourists with trail options and the corresponding charging infrastructure for E-bikers with different profiles. Our main objective is to provide TAs with a suitable decision-support tool that serves two purposes: (1) finding locations for charging stations by considering the difficulty and the cost of installing such stations in remote, often off-the-road locations; and (2) designing itineraries that are suitable for different categories of E-bikers. In the scientific literature, the first decision component has been mostly addressed in the context of electric cars, and it is not suitable for E-bikes. On the other hand, works on the second decision focused on muscular bikes, thus ignoring the first decision component. In this paper, we aim at closing this gap. We formulate this problem as a mixed-integer linear program. We develop an efficient branch-and-cut algorithm and present a comprehensive computational experiment. In particular, we provide a case study in the Asiago Sette Comuni Plateau in Italy, where the obtained charging stations and bike trails maximize a measure of attractiveness for three types of users. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Transportation Planning & Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Oct2024
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