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Advanced urban and transport planning activities are shifting towards sustainable urban mobility solutions and walkability (Buhrmann, Wefering and Rupprecht, 2019), namely referring to how friendly the urban environment is for walking in terms of proximity service availability (i.e.,15-min city), street connectivity, comfort of public spaces, and road safety. Although traditional approaches tend to focus on the spatial dimension, individual characteristics of city users are found to have a significant impact on the perceived level of walkability. In particular, the measures currently in place do not sufficiently consider population groups in vulnerable situations (i.e.,SDG 11.2-Sustainable Transport for All) (United Nations, 2016), including women. The research project ‘STEP UP - Walkability for Women in Milan' (awarded by Fondazione Cariplo under the call “INEQUALITIES RESEARCH” - Grant No. 2022-1643, focuses on the needs and expectations of women while walking. As highlighted by Golan et al. (2019), women experience the city differently than men, in part because they are more concerned with security issues related to aggression and harassment. These constraints take the form of precautionary or avoidance behaviors due to fear of violence, perception of risk, and sense of vulnerability, as a major inhibitor of mobility for women in public spaces especially at nighttime. STEP UP aims to assess the level of walkability for women focusing on the case study of Milan, Italy. First, a thematic literature review will be conducted on the most relevant scientific contributions and policy guidelines about this topic. The results of the literature review will be exploited to select a series of relevant geolocated datasets, which will be retrieved, sorted, and filtered from open data repositories and geoportals. Data regarding the perceived level of safety of women while walking will be collected through ‘Wher' - a route planner application operated by Walk21 Foundation. All these gender-disaggregated data sets will be analyzed through GIS-Geographic Information Systems to design a multi-layer map of Milan focused on several walkability criteria, which will be then validated through survey questionnaires and focus groups. The results of the project will help to identify challenging areas or neighborhoods in the city of Milan, which can serve as samples of analysis to develop a set of policy recommendations aimed at enhancing the level of walkability for women in cities. References Buhrmann, S., Wefering, F., Rupprecht, S. (2019).Guidelines for Developing and implementing a sustainable urban mobility plan – 2nd edition. Rupprecht Consult-Forschung und Beratung GmbH. Available at:https://www.eltis.org/mobility-plans/sump-guidelines Golan, Y., Wilkinson, N., Henderson, J.M., and Weverka, A. (2019). Gendered walkability: Building a daytime walkability index for women.Journal of Transport and Land Use, 12(1).https://doi.org/10.5198/jtlu.2019.1472 United Nations (2016).Transforming Our World: The 2030 Agenda for Sustainable Development. United Nations Secretariat. Available at:https://sdgs.un.org/2030agenda

The research was aimed at defining an innovative nature-based approach to effectively tackle vehicle emissions by considering the role of urban green areas, relying on the capacity of trees to absorb pollutants. The project combined two datasets describing the city of Milan (Italy) in relation to public trees inventory and private transport pollutants. A series of open and proprietary data was collected and analyzed to develop a GIS model, including green areas and traffic volumes. The main outcomes of the research are: (i) developing a GIS model linking public greenery environmental performance with vehicle-related PM2.5 emissions, allowing analysis, mapping and visualizations of datasets; (ii) defining green spaces provision in terms of trees necessary to absorb the rate of pollutants emitted by vehicles. Such integrated data-driven research could support decision makers in the integrated planning of SUMPs and Urban Green Infrastructures (UGI) to leverage on their co-benefits for climate change mitigation and adaptation.

This document reports the results of the different computational analyses performed on the data collected in the project. After a brief introduction, the document includes several sections devoted to different kinds of analysis. In each section, we have first an introduction on the objectives of the analysis presented in the context of the Diamond project, then it offers a description of the methodology, and the presentation of the results obtained, organized by use case. Finally, a last section is devoted to discussion and general conclusions. The computational methods employed for the data analysis include: Analysis of structured open data to define indicators that characterize the area surrounding each urban railway station (use case I) or docking station (Use case III) along socio-demographic, territory and mobility aspects, and to select relevant stations for the subsequent steps of the analysis (Section 2). Analysis of observations, to characterize the selected urban railways infrastructures (Use case I) and docking stations (Use case III) (Section 3). Analysis of experimental sessions in a simulator laboratory setting, to validate the impact of autonomous vehicles driving experience and passengers’ emotional appraisal, considering gender and other relevant intersectional variables (Use case II), combined with the analysis of user satisfaction surveys for Use case II (Section 4). Analysis of social media data collected from Twitter, to get insights on the concerns of men and women regarding topics related to Use case I, II and III (Section 5) Analysis of user satisfaction surveys on service provisions (Use cases I and III) and employment conditions (Use case IV), exploring and assessing differences in user satisfaction according to their socio-demographic characteristics, by means of statistical tests, regression analysis, factor analysis (Section 6). Analysis of users and employees' priorities from Dynamic Augmentative Delphi surveys, employing Analytic Hierarchy Processes to assess the ranking between priorities for different groups of respondents (Section 7). Bayesian network analysis to obtain a weighted hierarchy of fairness characteristics, based on open and proprietary structured data, observations and UESI questionnaires, and a statistical assessment of significant differences between users along socio-demographic variables (Section 8). While the document is structured along the different kinds of analysis performed, the final conclusions present a summary of the main results, organized per use case (Section 9).

The following document is ‘Deliverable 3.3 Report on data collection campaigns’ in the framework of the project titled ‘Revealing fair and actionable knowledge from data to support women’s inclusion in transport systems’ (Acronym: DIAMOND; Grant Agreement No 824326). The document describes the data collection campaigns executed within ‘Work Package 3 –Data collection’ (from M9-July 2019 to M25-November 2020), focusing on each Use Case: Use Case I: Public Transport Infrastructures (Railways); Use Case II: (Emotion in) Autonomous Passenger Car; Use Case III: Vehicle (Bike) Sharing Fleet Management; Use Case IV: Employment of Women in Rail Industry and Freight/CSR Protocols. Data collection campaigns have been based on the methodological approach of the DIAMOND project already presented in Deliverable 3.1 and Deliverable 3.21. This relies on the Fairness Characteristics identified through Thematic Analysis and on a series of interdisciplinary tools. A series of heterogeneous and disaggregated information have been gathered through the executed data collection campaigns and classified in three main categories: Thematic Analysis: Literature review (all Use Cases); Focus group discussions (all Use Cases) Semi-structured interviews (all Use Cases). Service and Employers’ Provision Data: Structured data (Use Cases I, III and IV); Observations (Use Cases I, II and III); Users and Employees’ Satisfaction Index questionnaires (all Use Cases); Social media data (Use Cases I, II and III). Users and Employees’ Perception Data: Dynamic Argumentative Delphi survey questionnaires (all Use Cases). Deliverable 3.3 is aimed at planning data collection and reporting the executed data collection campaigns with reference to timing and means deployed, results on user engagement level, surveys obtained and data from Web and other sources.Within the objectives of ‘Work Package 4 – Interdisciplinary model creation’, the collected Service and Employers’ Database will be analysed through Factor and Regression Analysis and Bayesian Network techniques, while the Users and Employees Perception Database is going to be analysed using Analytic Hierarchy Process (AHP) approach.

Covid-19 pandemic is deeply affecting urban mobility: the social-distancing strategies adopted to cope with the virus transmission pushed the majority of city users into avoiding public transport services in favour of safe and contactless travel options. To investigate this phenomenon, this paper proposes an Urban Informatics approach to understand passengers’ opinions and expressed polarity through user-generated social-media data (Twitter) in the Greater London Area. The analysed corpus consists of 27,700 tweets posted between January 13rd, 2020 and May 17th, 2021, and geolocated in immediate proximity of a short sample of selected Tube Stations. Data was segmented in several phases, based on the restriction measures enforced by the UK National Authorities. Each subset is then analysed through texts structuring and semantic analyses, with LDA topic modelling and Sentiment Analysis. Finally, the outcomes of these processes are interpreted in their chronological succession and compared to demand data and service-disruption data.