Women in Transport: Policies and Practice for Inclusive Mobility Planning

Lucía Recio Eurecat Technological Center ABOUT DIAMOND

2 ABOUT DIAMOND Revealing fair data and actionable knowledge from data for more inclusive and efficient transport systems

• H2020-EU.3.4 ‘Societal Challenges - Smart, Green And Integrated Transport’; • MG-4-3-2018 ‘Demographic change and participation of women in transport’; • 3 years duration, from 1/11/2018 to 31/10/2021; • Budget: 2,6 M€, Grant Agreement No. 824326;

• GOAL: turning big and small data from different sources into actionable knowledge for addressing gender-specific needs in current and future transport systems; • CONSORTIUM: 14 partners based in 7 European Countries.

3 ABOUT DIAMOND Research beyond the state of the art

Holistic research on women in transport Fairness applied to women in transport analysis 1 Analysis in areas of transport going beyond 3 Data analysis algorithms that incorporate parameters, current studies with results based on real data weights and constraints avoiding gender bias. analyses.

Methods for gender analysis Novel self-diagnosis and DSS tools 2 Development of multi-level models to explain the 4 A self-diagnosis toolbox and decision support travel behaviour of different women sub-groups. system (DSS) able to detect weaknesses in a specific transport mode.

4 ABOUT DIAMOND Validation in four specific public and private transport sector scenarios

1. Railways and public 2. Autonomous vehicles 3. Vehicle (bike) sharing 4. Corporate social multimodal transport responsibility and employment Analysis of the comfort and Evidence of needs and Research study on stations safety of automated vehicles to requirements in the planning of Research on women’s participation safety, accessibility and comfort adapt the design and vehicle sharing services in the transport and logistics from a gender perspective. Validation of the self-diagnose functionality of electronic addressing gender. Validation sectors crossed with concrete job and DSS tool for infrastructure stability controllers (ESC) to and diagnosis of DIAMOND’s positions and new opportunities to and transport service planning. gender through algorithms and Decision Support System for translate this understanding into machine learning. vehicles points and fleet concrete better gender-oriented job distribution and location descriptions and use of adapted planning. candidate search processes.

5 DIAMOND METHODOLOGY

6 DIAMOND METHODOLOGY

7 THEMATIC ANALYSIS

8 THEMATIC ANALYSIS Use case I Identification of the FCs-Fairness Characteristics Use Case I

Accessibility of the Design of the Safety & Security Service Infrastructure

Service Availability Harassment & Universal Design & Efficiency Pickpocketing

Railways and multimodal public transport Travel & Wayfinding Overcrowding & Cleanliness & Information Emergency Maintenance • Focus Groups: issues of equality in the Provision Situations transport sector in different EU countries; • Literature review: Fairness Characteristics and Ticketing Options & Furniture & Fares Facilities relevant parameters.

Travel Purpose

9 STRUCTURED DATA

10 STRUCTURED DATA Inclusion of women’s needs in public transport using urban (Big) data

DATA TYPOLOGY INDICATORS DATA SOURCE

Land Use (Urban fabric) Copernicus Land Monitoring Service TERRITORIAL DATA Points of Interest OpenStreetMap

Total population National Statistics Institute

Age of population National Statistics Institute SOCIO-DEMOGRAPHIC DATA Gender of population National Statistics Institute LINE - Nationality of population National Statistics Institute

Public transport OpenStreetMap

MOBILITY DATA Road Infrastructures OpenStreetMap

Parking services OpenStreetMap

LINE BARCELONA-VALLÈS TRAVEL DEMAND DATA Number of passengers FGC

Choubassi, R., Gorrini, A., Rezaallah, A., Boratto, L. (2019). Seeking Fair Inclusion of Women in Public Transport Using Urban (Big) Data: The Case of H2020 DIAMOND Project. In: 3rd International Conference on “Smart and Sustainable Planning for Cities and Regions – SSPCR 2019’, 9-13 December, 2019, Bolzano, Italy (accepted for oral presentation) 11 STRUCTURED DATA Selection of a short list of relevant stations

4,0

3,0

2,0

1,0 Normalized Values Values Normalized

0,0

RUBÍ

PIERA

SARRIÀ

PÀDUA

PALLEJÀ

ABRERA

GRÀCIA

ALMEDA

GORNAL

EL PALAU EL

CANROS

MIRA-SOL

SANT BOI SANT

LES FONTS LES

EL PUTXET EL

IGUALADA

MASQUEFA

LES PLANESLES

PROVENÇA

SANT JOAN SANT

SANT JOSEP SANT

LABEGUDA

MUNTANER

PL. ESPANYA PL.

BELLATERRA

VALLDOREIX

VOLPELLERES

LAFLORESTA

SANT CUGAT SANT

AV.TIBIDABO

SANT QUIRZE SANT

SANT GERVASI SANT

EUROPAFIRA /

LA CREU ALTALACREU

PLAÇA MOLINA PLAÇA

LABONANOVA

MANRESA ALTA MANRESA

PL. CATALUNYA PL.

REINA ELISENDA REINA

COLÒNIAGÜELL

LES TRESLESTORRES

CANPARELLADA

QUATRE CAMINS QUATRE

CORNELLÀ RIERA CORNELLÀ

ILDEFONS CERDÀ ILDEFONS

SABADELL NORD SABADELL

MARTORELL VILA /…

CANGRÀCIA / FEU

TERRASSA RAMBLA TERRASSA

MOLÍ NOU CIUTAT… NOU MOLÍ

HOSPITAL GENERAL HOSPITAL

SANTA COLOMA DE… COLOMA SANTA

MARTORELL ENLLAÇ MARTORELL

SABADELL PL. MAJOR PL. SABADELL

VILANOVA DEL CAMÍ VILANOVA

MARTORELL CENTRAL MARTORELL

AERIDEMONTSERRAT

MANRESA VILADORDIS MANRESA

CASTELLBELL I EL VILAR ELI CASTELLBELL

TERRASSA ESTACIÓ DEL… TERRASSA

MAGÒRIA LA CAMPANA LA MAGÒRIA

OLESA DE MONTSERRAT OLESA

SANT ESTEVE SESROVIRES ESTEVE SANT

UNIVERSITATAUTÒNOMA

LA POBLA DECLARAMUNT LA POBLA

SANT VICENÇ DELS HORTS DELS VICENÇ SANT

VALLPARADÍS UNIVERSITAT VALLPARADÍS

L'HOSPITALET AV.CARRILET L'HOSPITALET

SABADELL PARC DEL NORD PARC SABADELL

TERRASSA NACIONS UNIDES NACIONS TERRASSA

BAIXADOR DE VALLVIDRERA BAIXADOR

SANT VICENÇ / CASTELLGALÍ / VICENÇ SANT

SANT ANDREU DE LA BARCA ANDREU DE SANT MONISTROL DE MONTSERRAT DE MONISTROL Highest Quintile TDDI FGC metro and urban railway stations Lowest Quintile TDDI

TERRITORIAL DATA INDEX SOCIO-DEMO DATA INDEX MOBILITY DATA INDEX TRAVEL DEMAND DATA INDEX

12 Modelling Approach

13 Methodology to achieve these Goals part 1

Data collection Tools in several levels

• Collection of Data for Fairness Characteristics Level 3 ▪ Datasets in a Member State and Company Level

▪ Observations • Collection of Data for Fairness Characteristics Level 3 in a Station and Docking Level

▪ UESIs (User Satisfaction Index • Collection of Data for Fairness Characteristics Level 2 questionnaires) and 3 at User Level

▪ DAD (Dynamic Argumentative • Collection of Data for Fairness Characteristics Level 1 Delphi) Surveys + 2 at User Level

14 Methodology to achieve these Goals part 1

Actionable knowledge for theToolbox from 1. Self-diagnosis module based on suites of observations protocols, satisfaction questionnaire and Self WP3 and WP4 audit tools (able to deliver a Maturity Model ranking)

Per each vertex of the Inclusion Diamond 2. Decision support system based on results obtained from above

3. Prediction of improvement of perceived Fairness ((based on regression analysis) could be based on Module 1 regression model fit to data of each organization) Observation protocol(e.g. Score based on percentage of stations alligned with required conditions )

Module 4 Module 5 Module 6 Triangulation for Identification of areas of improvements Suggestion box for Module 2 Multidisciplinary analysis within each organization and best practices User satisfaction questionnaires using the Fairness Maturity benchmarking possibilities across collected form Model ranking organizations to share best practices literature and industry

Module 3 Self evaluation audit

15 USE CASE I CASE STUDY: A rail service provider in South Europe

16 USE CASE I: METHODOLOGY

• The survey was conducted in person by the DIAMOND research team on Rail Service operator on trains and stations at pre-approved routes and times.

• Approximately 50 questions covered the following themes: – Capable of meeting required needs (ability to travel in an appropriately designed transport system meeting basic expectations and being environmentally sound) – Accessible (ability to access activities and services that people have reason to value; accessible to all groups also in terms of design and comfort provided, e.g. those with children, and in terms of fares and costs for those with lower income, including distribution of costs and benefits) – Safe and Secure (ability to travel safely and securely for all type of users)

17 USE CASE I: CAPABLE

• The means for each question by gender are presented. Again, there is evidence that male and female respondents’ needs are generally being met by this public transport service (Irish Rail). Although there is some discrepancy in mean scores across gender for each statement (e.g. nonbinary response), correlational analysis and one-way ANOVA showed no significant difference between the genders. It is important to note that the number of nonbinary respondents was quite small (N = 3) and this can impact data analyses for this group. Capable of Meeting Required Needs x Gender 7

6,5

6

5,5

5

4,5

4

3,5

1 = 1= Strongly Disagree, 7= Strongly Agree 3 It is easy to get The frequency & Operational I am pleased with The availability of I am satisfied with I am satisfied that Public transport Public transport I can easily travel Fare discounts & where I want to service efficiency hours are provided travel travle info & ticketing, all exits, platforms fulfils my daily fulfiles my daily to my workplace ticket options are go … is adequate … sufficent … info … directions … timetabling & are clearly sign travel needs for travel needs for … more affordable & route planning on posted work commute time, price & better value than smart devices security travel by private car

Male Female Nonbinary Prefer not to say

18 USE CASE I: ACCESSIBLE

• The means for each question by gender are presented. Although there is some discrepancy in mean scores across gender for each statement (e.g. nonbinary response), correlational analysis and one-way ANOVA showed no significant difference between the genders except for one statement. • Gender differences: I can easily get to my destination from the station by walking or using other means of transport, F(3,318) = 2.86, p = .037 Accessible x Gender 7

6,5

6

5,5

5

4,5

4

3,5

3

2,5 1 = 1= Strongly Disagree, 7= Strongly Agree 2 1. Access within & 2. There is 3. The cleanliness 4. Facilities such 5. The facility & 6. There are quiet 7. I feel 8. I feel 9. I can easily get 10. At all times of around the adequate lighting & maintenance of as seating, service availability or private facilities comfortable with comfortable with to my destination the day, there are station… in the station… the backrests, & within the station available to air-conditioning atthe personal space from the station safe and easily environment… restrooms are is approp… parents for infant stations & in available to me at by walking or accessible adequate… feeding… carriages. stations and in using other means parking… carriages. of transport.

Male Female Nonbinary Prefer not to say 19 USE CASE I: SAFE AND SECURE

• The means for each question by gender are presented. Although there is some discrepancy in mean scores across gender for each statement (e.g. nonbinary response), correlational analysis and one-way ANOVA showed no significant difference between the genders except for one statement. • Gender differences:When arriving at or leaving the station I feel safe at any time of the day F(3,319) = 6.12, p < .001

Safe and Secure x Gender

6,5

5,5

4,5

3,5

2,5

1,5 1 = 1= Strongly Disagree, 7= Strongly Agree

0,5 1.When arriving at or leaving the 2.Within the station I feel that my 3.There are enough CCTV systems 4.If needed, I am satisfied that I could 5.I am confident that I would be able station I feel safe at any time of the belongings and I are safe at any time and other security measures visible get help or make a complaint straight to evacuate the station/train in an day. of the day. to make me feel safe away (e.g. security staff, emergency emergency situation (with help if phone number). necessary).

Male Female Nonbinary Prefer not to say

20 INCLUSIVENESS: • Chronic illness Most respondents (97.1%) did not have a chronic illness with only 2.9% of respondents stating they had a chronic illness (including mental illness) or physical, learning or sensory disability that affected their capacity to use public transport. There was no significant difference between genders. There was significant difference between chronic and non-chronic respondents for two statements: I am pleased with the provided travel information at the railway station, z = -2.39, p =.017; I feel comfortable with air-conditioning (temperature, ventilation, humidity) at stations and in carriages, , z = -2.22, p =.026. • Dependents (living with dependents and travelling with dependents) Most respondents (74.2%) did not live with any dependent person (e.g. children, elderly, caring for disabled person) nor traveled on a weekly basis with a dependent person (93.9%). There was no significant difference between genders. There was no significant difference between respondents living/travelling with dependents and those who do not across the three themes. • Discriminated against for using/working in transport for your appearance (aesthetics/the way you look) Most respondents (92.6%) report they have not experienced discrimination for their appearance. There was no significant differences for gender.

21 CONCLUSIONS AND FUTURE WORKS Towards policies and practice for inclusive mobility planning

• These resulst are goign to be discussed in focus groups within each organization to help idnetify also mor eind epth qualitative reason behind them and also explore possisble plans for improvements • Current transport systems do not sufficiently take into account women in the design of products and services, and in fostering women’s employability in the industry. • DIAMOND project analyses and converts data into knowledge with notions of impartiality to support gender inclusion in current and future transport systems from the perspective of women as transport users and as professionals in the sector.

Focus groups Structured Data Observations UESI Questionaires Social Media Data DAD Survey

22 Thank you!

www.diamond-project.eu

@DIAMOND_H2020

[email protected] [email protected]

EU Horizon 2020 research and innovation programme Grant agreement No 824326