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2024 | OriginalPaper | Buchkapitel

Optimizing Urban Infrastructure for E-Scooter Mobility

verfasst von : Diego Daniel Pedroza-Perez, Jamal Toutouh, Gabriel Luque

Erschienen in: Applications of Evolutionary Computation

Verlag: Springer Nature Switzerland

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Abstract

This paper addresses the optimization of urban infrastructure for e-scooter mobility through a multi-criteria approach. The proposed problem considers redesigning road infrastructure to integrate e-scooters into a city’s multimodal transportation system. The objectives involve improving cycle lane coverage for e-scooters while minimizing installation costs. A parallel multi-objective evolutionary algorithm is introduced to solve this problem, applied to a real-world instance based on Málaga city data. The results showcase the algorithm’s effectiveness in exploring the Pareto front, offering diverse trade-off solutions. Key solutions are analyzed, highlighting different zones with varying trade-offs between travel time improvement and installation costs. Visualization of proposed infrastructure changes illustrates significant reductions in travel time and enhanced multimodality. Computational efficiency analysis indicates successful parallelization, achieving substantial speedup and high efficiency with up to 32 processing elements.

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Vorheriges Kapitel A Simple Statistical Test Against Origin-Biased Metaheuristics

Nächstes Kapitel Simple Efficient Evolutionary Ensemble Learning on Network Intrusion Detection Benchmarks

Málaga Subway website: www.metromalaga.es.

Github of the project: https://github.com/pedrozad/e-scooter-way.

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Titel: Optimizing Urban Infrastructure for E-Scooter Mobility
verfasst von: Diego Daniel Pedroza-Perez
Jamal Toutouh
Gabriel Luque
Verlag: Springer Nature Switzerland
Buch: Applications of Evolutionary Computation
Print ISBN: 978-3-031-56851-0

Electronic ISBN: 978-3-031-56852-7

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-56852-7_22

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