Application of Mathematical and Computational Methods to Identify Women’s Priorities in Transport

Abstract

It is well established that the transport sector is not an equalitarian sector. To develop a sustainable society, a more equalitarian and safe transport system for both users and transport sector employees is needed. This work prioritizes the needs and barriers previously identified as relevant among transport system users and employees for four different transport scenarios (railways, autonomous vehicles (AVs), bicycle-sharing services (BSSs), and employment). The aim of this paper is to prioritize the factors affecting women in these four transport scenarios with the help of a survey followed by the application of mathematical and computational algorithms based on the analytic hierarchy process (AHP) methodology. The identification of factors with higher influence in the fair participation of women in the transport sector will help transport planners, bike-sharing system owners, decision-makers, transport companies, and regulatory professionals to develop measures that could plausibly increase the proportion of women as users of BSSs, users of rail public transport, and AVs, as well as employees in the transport sector for a sustainable society. The results indicated that safety and security were the most challenging factors for railways. Weather, topography, and family responsibilities were shown to have a high influence on the use of BSSs. In the case of autonomous vehicles, the simultaneity and trust in the technology are the main opportunities to influence the acceptance of such vehicles. Finally, for transport employment, caring and parenting responsibilities were the factors that had the largest effect. Some differences in priorities were found for different profiles of women.

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

“…In this paper, we analyzed more specific aspects that sometimes are by themselves measures to implement and what we call in this paper level 3 fairness characteristics, characteristics that further develop the level 2 FCs defined in [1]. We weighed level 1 and level 2 FCs using a multi-criteria decision-making method, the analytic hierarchy process (AHP) [33,34], used in a previous publication [35]. Levels 1 and 2 FCs are qualitative factors.…”

“…To hierarchize the different factors that influence a fair transport system in four different transport scenarios or UCs, we examined the AHP weights of level 1 and level 2 FCs [35] and combined them with those obtained through BN analysis for level 3 FCs. The combination of results from the AHP and BN analyses has been reported previously [38].…”

“…In green are highlighted those aspects that will be addressed within this paper. The other steps were developed in our previous publication [35].…”

“…Finally, the global weights of level 3 FCs were obtained by multiplying the local weight obtained by BNs and their corresponding level 1 and level 2 FC weights obtained in the AHP analysis, and normalizing the results. Level 1 and level 2 FC weights were obtained from a previous publication [35]. This combination of BN and AHP weights was automated by applying MATLAB R2020b to allow an intersectional analysis according to the different possible profiles defined in the PI [40,41].…”

“…The third to sixth factors show a very small difference in weight, so they can be considered to have similar importance. This group of factors includes a perception of reducing the ecological impact of the use of cars due, for example, to higher efficiency in accelerations and decelerations and thus optimized driving [35,54]. Other safety factors included in the top 10 list are a perception of a reduction in accidents; feeling comfortable and trusting the system when sharing data with others, including other users of the transport system and the infrastructure; a reduction in accidents thanks to the use of AVs; and the perception of safety in any conditions.…”