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2024 | OriginalPaper | Chapter

Optimizing Urban Infrastructure for E-Scooter Mobility

Authors : Diego Daniel Pedroza-Perez, Jamal Toutouh, Gabriel Luque

Published in: Applications of Evolutionary Computation

Publisher: 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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previous chapter A Simple Statistical Test Against Origin-Biased Metaheuristics

next chapter 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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Title: Optimizing Urban Infrastructure for E-Scooter Mobility
Authors: Diego Daniel Pedroza-Perez
Jamal Toutouh
Gabriel Luque
Publisher: Springer Nature Switzerland
Book: Applications of Evolutionary Computation
Print ISBN: 978-3-031-56851-0

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

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

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