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Sustainability
Volume 13
Issue 20
10.3390/su132011372
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Article

Computational Solutions Based on Bayesian Networks to Hierarchize and to Predict Factors Influencing Gender Fairness in the Transport System: Four Use Cases

by
Gemma Dolores Molero
1,*,
Sara Poveda-Reyes
1,
Ashwani Kumar Malviya
1,
Elena García-Jiménez
1,
Maria Chiara Leva
2 and
Francisco Enrique Santarremigia
1
1
Research & Innovation Projects, AITEC, Paterna, 46980 Valencia, Spain
2
School of food Science and Environmental Health, Technological University Dublin, D07 H6K8 Dublin, Ireland
*
Author to whom correspondence should be addressed.
Sustainability 2021, 13(20), 11372; https://doi.org/10.3390/su132011372
Submission received: 10 September 2021 / Revised: 1 October 2021 / Accepted: 7 October 2021 / Published: 14 October 2021
(This article belongs to the Special Issue Mobility for Sustainable Societies: Challenges and Opportunities)
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Abstract

Previous studies have highlighted inequalities and gender differences in the transport system. Some factors or fairness characteristics (FCs) strongly influence gender fairness in the transport system. The difference with previous studies, which focus on general concepts, is the incorporation of level 3 FCs, which are more detailed aspects or measures that can be implemented by companies or infrastructure managers and operators in order to increase fairness and inclusion in each use case. The aim of this paper is to find computational solutions, Bayesian networks, and analytic hierarchy processes capable of hierarchizing level 3 FCs and to predict by simulation their values in the case of applying some improvements. This methodology was applied to data from women in four use cases: railway transport, autonomous vehicles, bicycle sharing stations, and transport employment. The results showed that fairer railway transport requires increased personal space, hospitality rooms, help points, and helpline numbers. For autonomous vehicles, the perception of safety, security, and sustainability should be increased. The priorities for bicycle sharing stations are safer cycling paths avoiding hilly terrains and introducing electric bicycles, child seats, or trailers to carry cargo. In transport employment, the priorities are fair recruitment and promotion processes and the development of family-friendly policies.
Keywords: fairness; transport; gender; railway stations; bicycle sharing; autonomous vehicles; transport employment; Bayesian networks fairness; transport; gender; railway stations; bicycle sharing; autonomous vehicles; transport employment; Bayesian networks

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MDPI and ACS Style

Molero, G.D.; Poveda-Reyes, S.; Malviya, A.K.; García-Jiménez, E.; Leva, M.C.; Santarremigia, F.E. Computational Solutions Based on Bayesian Networks to Hierarchize and to Predict Factors Influencing Gender Fairness in the Transport System: Four Use Cases. Sustainability 2021, 13, 11372. https://doi.org/10.3390/su132011372

AMA Style

Molero GD, Poveda-Reyes S, Malviya AK, García-Jiménez E, Leva MC, Santarremigia FE. Computational Solutions Based on Bayesian Networks to Hierarchize and to Predict Factors Influencing Gender Fairness in the Transport System: Four Use Cases. Sustainability. 2021; 13(20):11372. https://doi.org/10.3390/su132011372

Chicago/Turabian Style

Molero, Gemma Dolores, Sara Poveda-Reyes, Ashwani Kumar Malviya, Elena García-Jiménez, Maria Chiara Leva, and Francisco Enrique Santarremigia. 2021. "Computational Solutions Based on Bayesian Networks to Hierarchize and to Predict Factors Influencing Gender Fairness in the Transport System: Four Use Cases" Sustainability 13, no. 20: 11372. https://doi.org/10.3390/su132011372

APA Style

Molero, G. D., Poveda-Reyes, S., Malviya, A. K., García-Jiménez, E., Leva, M. C., & Santarremigia, F. E. (2021). Computational Solutions Based on Bayesian Networks to Hierarchize and to Predict Factors Influencing Gender Fairness in the Transport System: Four Use Cases. Sustainability, 13(20), 11372. https://doi.org/10.3390/su132011372

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