The transport sector cannot evolve without including a gendered perspective. A sustainable society cannot be reached without improving the understanding of the relationships between gender and mobility and developing measures to achieve a fair transport system. Extensive work has been developed analyzing barriers and factors affecting the safe and secure use of the transport system by women [1
], as well as on the gender gap and the need of equal employment opportunities and conditions, and on improving these employment conditions to better adjust them to women’s needs [1
Within this paper, we wanted to go further than a qualitative analysis of the inclusion of women in the transport sector. We aimed to gather real data and to analyze it using probabilistic methods, such as Bayesian networks (BNs), to achieve a more complete picture of the current situation of society.
BN learning methods can be divided into parametric learning and structural learning. Parametric learning consists of obtaining the conditional probabilities given by the structure of the network using the observed frequencies of a database, while structural learning tries to find the graph that best represents the probability distribution of a given database. Structural learning methods can be divided into independence-based or constraint-based methods and search and score methods. On the one hand, independence-based or constraint-based methods involve detecting the probabilistic conditional independences present in the database; one of the most famous algorithms pertaining to this type is the PC (Peter and Clark) algorithm [13
]. On the other hand, search and score methods, also known as methods based on a heuristic search, involves performing a heuristic search through the space of possible structures using a metric that measures how well each structure can represent the probability distribution of the variables in the database. Several metrics have been used in the literature: Bayesian (which include K2, the Bayesian information criterion [BIC], the Bayesian Dirichlet equivalent uniform [BDe], and others), cross-entropy, the Akaike information criterion (AIC), or minimum description length (MDL) [14
], among others.
1.1. Bayesian Networks Applied to Public Transport
Many studies have indicated different aspects that affect women when using the public transport system. One of the main differing aspects of the use of the public transport by men and women is the fact that women suffer more episodes of harassment than men [17
]. Some studies have indicated that stations with poor lightning conditions or sidewalk maintenance have more probability of harassment or aggression episodes [17
]. Other aspects have been studied, such as the differing travel purposes between men and women, where studies have shown that women tend to do more trip chaining while men tend to go from point A to point B. This might be due a higher percentage of women being in charge of caring responsibilities and everyday household tasks or other errands.
Harvey et al. [18
] indicated that some of the factors that show differences between genders were: women are more concerned with travel security than men, women perceive less than men the importance of high-speed rail prestige, and women also value their use of travelling time more (it is important for them to do something when travelling).
There are not many studies using Bayesian networks applied to public transport. Nguyen et al. [19
] analyzed the ride comfort of bus passengers, because comfort is a critical factor when attracting users to a specific mode of public transport. They used as inputs vehicle-related parameters, passenger-related features (including posture, location, direction faced, gender, age, weight, and height), and ride comfort index based on ISO 2631-1997; they used the passenger rating (collected from a mobile application) as the output. They built an artificial neural network model and found that passenger-related factors contribute slightly higher than vehicle-related factors to the ride comfort estimation. The analysis between gender showed that the average comfort ratings are similar; however, there are some variations in the rating distribution, where it is more symmetric for men and skews toward lower values for women. The development of machine learning models is becoming an important tool in guiding an autonomous bus.
1.2. Bayesian Networks Applied to Autonomous Vehicles-Driver Interaction
Previous studies have highlighted some differences between men and women and their use and interaction with private and public transport; differences in driving behavior, gender-specific use (women tend to trip-chain more), economic level, technology acceptance, safety concerns, or the willingness to buy a new car are some of the aspects that have been mentioned as those influencing the interaction and willingness of women and men to buy or use a vehicle [2
]. New technologies used in vehicles should focus on meeting the real needs of citizens and then consider the different needs of society and men and women.
There has been limited quantitative analysis using BNs related to the analysis of gender differences in transport. Febres et al. [22
] analyzed how the sex and age of the driver could impact the probability of having a road traffic accident when driving a vehicle. They showed that men have a greater probability of suffering a serious and/or fatal injury. Regarding age, they found that male drivers <18 years of age are particularly affected on business trips, with a 20.1% probability of sustaining an injury, while the probability of young women drivers sustaining an injury is 18.7%. They also found that people >60 years of age have a lower risk of experiencing a serious accident. Catalina et al. [23
] analyzed the effect of music while driving in young men and women by using BN analysis. They found that when there is no music while driving, women drive at a more appropriate speed than men, while in the presence of music women have a greater chance of committing either a minor or a major speed violation, with music acting as a factor of distraction. Ji et al. [24
] used different tools including factor analysis, structural equation modeling (SEM), and BNs to evaluate factors influencing the willingness to use parking guidance and information (PGI) systems and the different perceptions of men and women. They found that female drivers are more likely to use PGI to get help when they realized that it is hard to find a parking space, with a level of willingness of 63.6% compared with 10.9% for male drivers.
1.3. Bayesian Networks Applied to Bicycle Sharing Services
The main differences between men and women in barriers to cycling that have been reported in the literature, including individual mobility patterns, convenience, harassment, or abuse by other road users; traveling with children or goods; cost, access, and logistics; and knowledge and experience.
Some studies using computational methods, such as Bayesian networks, can be found to be applied to shared mobility. Aman et al. [25
] analyzed e-scooter mobility of two micromobility companies using machine learning techniques to identify the factors that influence rider satisfaction. They used the latent Dirichl et al. location model to identify the topics discussed in 12,000 reviews of driver and logistic regression to identify the most significant factors. The factors with greater influence on the overall rider satisfaction for both men and women were refund, payment, battery, and customer service. When analyzing only men, the factors with greater influence on their satisfaction were refund, ease of use, payment, and pricing. For women, the factors with greater influence on their satisfaction were refund, payment, and pricing. They also found that safety (speed and riding lane) was not significant for male and female models. In general, women were more satisfied with the services and exhibited more positive sentiment than men, although the percentage of women using the service (29%) was less than the percentage of men (71%).
Le et al. [26
] analyzed the attitudes and perceptions of female cyclists from Canada and the United States. They used as input data an online survey and analyzed it through tree-based machine learning methods (e.g., bagging, random forests, and boosting) to select the most common motivations and concerns of these cyclists, and then they applied chi-squared and non-parametric tests to analyze differences between groups. The survey asked about aspects that could affect the decision of using a bicycle for transport or recreation such as cycling skills, attitude, perceptions of safety, and surrounding environment. Their results indicated that the most important factors for women to cycle for transport or recreation are the lack of bicycle facilities, cycling culture, the practicality of cycling, sustainability, and health. They also found that very few cyclists cycled by necessity, and that most cyclists preferred cycling in facilities that were separated from vehicular traffic (e.g., separated bicycle lanes or trails). Based on their results, they suggested that to enhance cycling rates, women’s safety should be improved by tailoring policy prescriptions for cyclists of different skill groups, investing in bicycle facilities, and building a cycling culture in communities and at the workplace.
1.4. Bayesian Networks Applied to Transport Employment
The involvement of women in transport-related jobs is much lower than the involvement of men. In 2020, only 22% of transport workers were women in the EU-27 [27
]. The barriers underlying this low involvement have been analyzed in other studies [1
] and include, among others, (i) sociocultural aspects related to the historical roles established for men and women in societies in which men have traditionally been seen as in charge of the family economy, and women have been viewed as in charge of taking care of the family; (ii) the increase in the percentage of women employed, a phenomenon that has produced new needs in the employment sector; and (iii) the fact that women tend to experience more harassment and feel more unsafe and unsecure than men when using public or private transport, and in their workplace. Society is progressing from this old perspective, but additional efforts should be made to change from this “antique organization of society” to an egalitarian society in which every person can have the same opportunities, without barriers, with the ability to feel free to make their own decisions, to work in what sector they want in a comfortable environment, with an employment system that can cover family needs related to care responsibilities, and to feel safe when developing their mobility needs.
Some computational methods can be found analyzing aspects influencing employment in the transport sector. Chen et al. [29
] analyzed inter-city commuting decisions in Germany using machine learning techniques (i.e., linear regression, decision trees, and random forest). They analyzed the influence of gross domestic product (GDP), housing, and the labor market on the decision to commute, and reached the conclusion that access to employment opportunities, housing prices, income, and the distribution of the location’s industry sectors are important factors in commuting decisions. Moreover, different age, gender, and income groups have different commuting patterns.
Other machine learning methods have been used to analyze the role of gender in the transport sector. Luo et al. [30
] analyzed the effects of transport infrastructure connectivity (TIC) on conflict resolution through dual machine learning using global conflict data from 2010 to 2017. Their results indicated that TIC, in addition to being a trade facilitator, could improve conflict resolution, gender employment, and income growth [30
Regarding employment, Esser et al. [31
] examined the impact of technological innovations (information and communication technology [ICT] and automation) on future professions and specializations in maritime and non-maritime jobs in the port of Antwerp, and identified the skills that need to be developed by education. They conducted a literature review, analyzed quantitative data on the characteristics of employment in the port, and performed a qualitative analysis through interviews. Regarding gender issues, they found that ICT introduction and automation would lead to the disappearance of a lot of middle-paying paperwork jobs, management jobs will become more and more complex with multi-skilling becoming a key, and there is a need to motivate and to host females and non-natives in the port job market.
Our objective in this paper is to hierarchize the different factors that influence a fair transport system in four different transport scenarios or use cases (UCs): railway stations, autonomous vehicles (AVs), bicycle sharing systems, and employment in the transport sector. The novelty with previous work is that we do not focus the analysis in general aspects but rather go a step forward and define specific aspects that service providers or employers of transport companies can understand and develop measures to improve them, in fact some level 3 FCs are themselves a fairness measure to be implement in order to increase the fairness and inclusion in each use case from a gendered perspective. The hierarchization of these detailed aspects allows the development of actionable knowledge. To develop this study, we have gathered quantitative data and analyzed it through BNs. Hail and McQuaid [32
] defined fairness for each of these transport scenarios. We previously defined a list of fairness characteristics (FCs) and prepared a hierarchical model with two levels of these FCs [1
]. In this previous work, we analyzed general concepts as service availability, travel purpose, facilities, harassment, vehicle behaviour, job segregation, etc. 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
], used in a previous publication [35
]. Levels 1 and 2 FCs are qualitative factors. In the case of level 3 FCs, each has been defined using one or more quantitative variables obtained by user satisfaction questionnaires, observations in railway stations and bicycle sharing docking stations, through simulation in an AV simulator, and through structured data sets from companies (railway and bicycle sharing companies involved in the DIAMOND project). We have analyzed the quantitative data obtained through these data collection tools by using BNs [36
We have presented a methodology to hierarchize and to predict factors influencing gender fairness in the transport system by applying BNs.
In addition, we have also presented computational solutions to automate algorithms underlying the methodology in this work. These solutions would allow additional analysis for specific profiles of women according to the PI approach [43
For UC 1, to develop fairer railway transport in which women would feel more comfortable and safer and increase the use of railways, efforts should focus on improving the layout of seating to reduce social interaction and increase personal space, increasing the availability of on-demand services as well as integrating shared mobility services for last-mile connections, building hospitality rooms or help points for users in case of aggression or need, advertising helpline numbers as well as promoting campaigns to reduce incidents, and finally increasing the availability and the reliability of the different service modes available at the station.
In UC 2, to increase the acceptance of AVs by women, efforts should focus first on increasing the users’ perception of safety of these vehicles. The use of AVs could reduce environmental impact and help build a more sustainable society. In different studies, women have shown that they are more concerned with the environment than men, and this factor could lead them to accept the use of AVs, and also increase their perception that using AVs could reduce accidents. Other issues to deal with are data security, so that personal data and control of the car do not get into the wrong hands, the fact that less training would be needed to use AVs, and the fact that using AVs will permit doing additional things while the car drives by itself (e.g., watching a film or working on a laptop). An improvement in the perception of the safety of AVs in any driving conditions would be translated into an increase in the satisfaction with the safety of AVs and an increase in the comfort of sharing data with others.
For UC 3, to increase the use of bicycle sharing stations by women, the FCs with the highest priority or influence concern aspects such as developing cycling networks that avoid hilly terrain as much as possible, the introduction of electric vehicles to make travel easier, developing weather friendly infrastructure, as well as making rain ponchos available. Other factors consider family responsibilities and include bicycles with child seats and trailers for carrying kids and carting cargo, education on cycling with children and cargo, development of safe routes when cycling with children, and incorporating end-of-trip cycling facilities in the stations. When selecting only those people who consider that a route including hilly terrain does not deter them from using the bicycle, there was a higher percentage of people evaluating more positively cycling in rainy or bad weather conditions as well as the presence of end of trip facilities compared with the overall sample. We noted the same outcome with the perception of having a safe cycling network for cycling with children: If the routes do not have hilly terrain or if there are measures to make it more comfortable, it would be more suitable for cycling with children.
Inclusion of women in the transport sector is very low. The results showed that to enhance the employment of women in the transport sector, first, recruitment processes should be fair, with all positions advertised widely and all the applications welcomed no matter the sociodemographic characteristics of the person applying for the position (e.g., blind CV), ensuring that employment decisions are based on objective issues related to the job. Measures should also focus on reducing the gender gap, ensuring gender pay equality for equivalent positions, and achieving a more equal balance between genders across all occupations and levels (e.g., through new HR policies and CSR protocols). Family-friendly policies should also be developed to provide more support to maternity and paternity leave, flexible working conditions, and allow leave for taking care of the family. The presence of security staff in some positions that are in greater contact with people and in which the probability that women could be harassed is higher would increase the perception of safety and would provide women with support. Finally, because the low presence of women in transport-related jobs is a result of the societal perceptions, special training should be developed for both women and men to negate cultural stereotypes associated with women and specific roles and tasks associated with men in the transport sector.
Future studies should focus on exploring other predictive methods to compare the results regarding the simulation and to validate these conclusions. Moreover, this same methodology and algorithms should be applied to filtered datasets to examine specific profiles of women to conduct an intersectional analysis and draw conclusions that go beyond considering women as a homogeneous group.