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

Gemma Dolores Molero, Sara Poveda-Reyes, Ashwani Kumar Malviya, Elena García-Jiménez, Maria Chiara Leva and Francisco Enrique Santarremigia
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Gemma Dolores Molero: Research & Innovation Projects, AITEC, Paterna, 46980 Valencia, Spain
Sara Poveda-Reyes: Research & Innovation Projects, AITEC, Paterna, 46980 Valencia, Spain
Ashwani Kumar Malviya: Research & Innovation Projects, AITEC, Paterna, 46980 Valencia, Spain
Elena García-Jiménez: Research & Innovation Projects, AITEC, Paterna, 46980 Valencia, Spain
Maria Chiara Leva: School of food Science and Environmental Health, Technological University Dublin, D07 H6K8 Dublin, Ireland
Francisco Enrique Santarremigia: Research & Innovation Projects, AITEC, Paterna, 46980 Valencia, Spain

Sustainability, 2021, vol. 13, issue 20, 1-22

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 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (1)

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