Abstract
This study aims to improve Fairness from a gender perspective in 4 use cases in transport. Fairness Characteristics (FCs) were identified and clustered in 3 levels by literature review and FGs. A weighted hierarchy of FCs was defined after data collection and data analysis through the AHP and BNs. An interdisciplinary panel identified recommendations per each FC level 3. The Rasch model assesses Fairness, and Bayesian inferences predict the impact of implementing a recommendation. Connections and access points to all users in public transport, 1-minute safety training video in autonomous driving, availability of electric bikes, and flexibility against maternity in the job are critical recommendations for Fairness in transportation. Future studies should focus on specific profiles. This work is interesting to organizations in the transport sector, researchers, and policymakers.
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