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The current research work is based on an extended spatial analysis executed through the application of GIS, aimingat assessing the level of walkability in the city of Bologna (Italy). In particular, the research focused on walkabilityfor children (aged between 5 to 13 years old), as they experience the city differently than adult pedestrians,since they are more vulnerable to road accidents and they need proper infrastructures to freely play outdoorsand walk independently. The GIS analysis was based on a series of location-based data retrieved from differentopen source repositories and focused on the level of usefulness, comfort, safety, and attractiveness of Bolognafor child pedestrians. The proposed Walkability for Children Index was aimed at identifying the neighborhoodscharacterized by the lowest level of pedestrian friendliness in relation to the childrens needs while walking.Results helped to identify and characterize a short list of suitable areas where to prioritize interventions focusing,for example, on guaranteeing the presence of relevant public services within a walkable distance of 15 minutesfrom place of residence and on the implementation of urban regeneration projects through the tactical urbanismapproach.

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.

The recent development of location detection systems allows to monitor, understand and predict the activity patterns of the city users. In this framework, the research focuses on the analysis of a sample of aggregated traffic data, based on the number of mobile devices detected through a network of 55 Wi-Fi Access Points in Milan. Data was collected over 7 months (January to July 2020), allowing for a study on the impact of the Covid-19 pandemic on activity patterns. Data analysis was based on merging: (i) time series analysis of trends, peak hours and mobility profiles; (ii) GIS-based spatial analysis of land data and Public Transport data. Results showed the effectiveness of Wi-Fi location data to monitor and characterize long-term trends about activity patterns in large scale urban scenarios. Results also showed a significant correlation between Wi-Fi data and the density distribution of residential buildings, service and transportation facilities, entertainment, financial amenities, department stores and bike-sharing docking stations. In this context, a Suitability Analysis Index is proposed, aiming at identifying the areas of Milan which could be exploited for more extensive data collection campaigns by means of the installation of additional Wi-Fi sensors. Future work is based on the development of Wi-Fi sensing applications for monitoring mobility data in real time.

In the Milan experience, Covid-19 emergency crucial issues were already hidden weaknesses of the city and its region: the limited capacity of transit transport, roads and public spaces, with crowding problems for both work and leisure. The challenge is to regenerate the competitive “human measure” of Milan, based on its unique relationship between public spaces and mobility, overcoming its health risk. The report raises a question on the established transit-oriented development approach, focusing on spaces “in between” and not only on nodes and networks. The traditional “invariants” welcome changes: the spatial structure of the public realm becomes a platform for ever-changing mobility and services, providing quality of life for communities, users and tourists. With this respect, streets represent by far the most strategic asset of the urban public realm. They can be reshaped in resilient infrastructure capable to respond to new forms of mobility based on a renewed Mobility-as-A-Service paradigm, as final result of different travel behaviors of the post pandemic scenario, among which an expected reduction of the overall “mobility consumption” (space) and new temporal urban rhythms (time). To this end, short-term and responsive planning becomes a crucial opportunity to enable rapidly deployed responses, through an extensive use of new analytical tools based on Open and Big data analytics and computer-based simulations., TeMA - Journal of Land Use, Mobility and Environment, 2020: Special Issue. Covid-19 vs City-20

Urbanization represents a huge opportunity for computer applications enabling cities to be managed more efficiently while, at the same time, improving the life quality of their citizens. One of the potential application of this kind of systems is a bottom-up evaluation of the level of walkability of the city (namely the level of usefulness, comfort, safety and attractiveness of an urban area for walking). This is based on the usage of data from social media for the computation of structured indicators describing the actual usage of areas by pedestrians. This paper will present an experimentation of analysis of data about the city of Milano (Italy) acquired from Flickr and Foursquare. The over 500 thousand points, which represent the photos and the POIs collected from the above mentioned social meda, were clustered through an iterative approach based on the DBSCAN algorithm, in order to achieve homogeneous areas defined by the actual activity of inhabitants and tourists rather than by a top down administrative procedure and to supply useful indications on the level of walkability of the city of Milan., Pre-print of a paper presented at the 12th International Conference on Traffic and Granular Flow - TGF 2017, 19-22 July 2017, Washington DC, USA (2017)

The dynamics of agent-based systems provide a framework to face the complexity of pedestrian-vehicle interactions in future cities, in which the compliance to traffic norms plays a fundamental role. The data of an observation performed at a non-signalized intersection are presented to provide useful insights for supporting the future development of agent-based models. Results focus on drivers' compliance to crossing pedestrians, describing potentially conflictual interactions among heterogeneous agents. The discussion closes with the potential applications of the collected data set for modelling the phenomenon., Pre-print of a paper presented at the 2nd International Workshop on Agent-based modelling of urban systems - ABMUS 2017, at the 16th International Conference on Autonomous Agents and Multiagent Systems - AAMAS 2017, 8-12 May 2017, Sao Paulo, Brazil (in press, 2017)

Environmental, demographical and psychological factors have a demonstrated impact on risky crossing behaviour. In this work we focus on the potential influence of social factors on the considered phenomenon (i.e. group crossing decision). We present the results of a video-recorded observation about the crossing behaviour of singles and dyads at non-signalized intersections. Results showed that crossing behaviour is characterized by three distinct phases: (i) approaching, (ii) appraising (decision making) and (iii) crossing. Dyads walk slower than single pedestrians in all phases. The crossing behaviour of dyads is characterized by the emergence of a leader who takes the decision to cross first, followed by the companion. However, there is no difference between the accepted safety gap of singles and dyads. Understanding factors influencing the crossing decision of social groups represents an important result supporting the development of agent-based simulations of pedestrian-vehicle interactions., Pre-print of a paper presented at the 12th International Conference on Traffic and Granular Flow - TGF 2017, 19-22 July 2017, Washington DC, USA (2017)

The influence of cohesion among members of dyads is investigated in scenarios characterized by uni-directional flow by means of a discrete model: a corridor and the egress from a room with a bottleneck of varying width are simulated. The model manages the dynamics of simulated group members with an adaptive mechanism, balancing the probability of movement according to the dispersion of the group; the cohesion mechanism is calibrated through the parameters $\kappa_c$ and $\delta$. All scenarios are simulated with two procedures: (Proc. 1) population composed of individual pedestrians, in order to validate the simulation model and to provide baseline data; (Proc. 2) population including dyads (50% of the simulated pedestrians), in order to verify their impact. In the corridor scenario, the presence of dyads causes a reduction of the velocities and specific flow at medium-high densities. Egress from a square room with a unique central exit produces results in line with recent studies in the literature, but also shows that the dyads negatively affect the dynamics, leading generally to a slower walking speed and a lower pedestrian flow. Ignoring the presence of dyads would lead to an overestimation of egress flows., Comment: Pre-print of a paper presented at the 12th International Conference on Traffic and Granular Flow - TGF 2017, 19-22 July 2017, Washington DC, USA (2017)

Although it is widely recognised that the presence of groups influences microscopic and aggregated pedestrian dynamics, a precise characterisation of the phenomenon still calls for evidences and insights. The present paper describes micro and macro level original analyses on data characterising pedestrian behaviour in presence of counter-flows and grouping, in particular dyads, acquired through controlled experiments. Results suggest that the presence of dyads and their tendency to walk in a line-abreast formation influences the formation of lanes and, in turn, aggregated observables, such as overall specific flow., Comment: Pre-print of a paper presented at the 12th International Conference on Traffic and Granular Flow - TGF 2017, 19-22 July 2017, Washington DC, USA (2017)

The modelling and simulation of the interaction among vehicles and pedestrians during cross-walking is an open challenge for both research and practical computational solutions supporting urban/traffic decision makers and managers. The social cost of pedestrians' risky behaviour pushes the development of a new generation of computational models integrating analytical knowledge, data and experience about the complex dynamics occurring in pedestrian/vehicle interactions, which are not completely understood despite recent efforts. This paper presents the results of a significant data gathering campaign realised at an unsignalized zebra crossing. The selected area of the city of Milan (Italy) is characterised by a significant presence of elderly inhabitants and pedestrian-vehicle risky interactions, testified by a high number of accidents involving pedestrians in the past years. The results concern the analysis of: (i) vehicular and pedestrian traffic volumes; (ii) level of service; (iii) pedestrian-vehicle interactions, considering the impact of ageing on crossing behaviour. Results showed that the phenomenon is characterised by three main phases: approaching, appraising (evaluation of the distance and speed of oncoming vehicles) and crossing. The final objective of the research is to support the development of a microscopic agent-based tool for simulating pedestrian behaviour at unsignalized crosswalks, focusing on the specific needs of the elderly pedestrians., Comment: Pre-print of a paper presented at the 8th International Conference on Pedestrian and Evacuation Dynamics (PED2016), Hefei, China - Oct 17-21, 2016

The calibration and validation of pedestrian simulations require the acquisition of empirical evidences of human behaviour. The current work presents the results of an experiment focused on the potentially combined effect of counter flow and grouping on pedestrian dynamics. In particular, we focused on: (i) four different configurations of flow ratio (the rate between the minor flow and the total flow in bidirectional scenarios); (ii) dyads, as the most frequently observed and basic social groups of crowds. Results showed that the increase of flow ratio negatively impacted the speed of pedestrians. Dyads walked significantly slower than singletons, due to the difficulty in movement coordination among group members (proxemics) in case of counter flow. The collected results represent an useful contribution towards the validation of pedestrian simulations., Comment: Pre-print of a paper presented at the 8th International Conference on Pedestrian and Evacuation Dynamics (PED2016), Hefei, China - Oct 17-21, 2016

Recent crowd disasters highlight the importance of properly planning and designing large urban events and public spaces to enhance the safety of people in a crowd during evacuations. Pedestrian crowd dynamics are empirically investigated with an interdisciplinary approach (i.e., social sciences, computer science, and traffic engineering) focusing on the effect of groups and their proxemic behavior and interactions while walking. Empirical evidence achieved from urban in-field observations and laboratory experiments are presented and compared. Results indicate that the proxemic behavior of walking groups has a negative effect on walking speed when flow is irregular (primarily because of the need for members to maintain spatial cohesion during locomotion). These results have important implications for the design of common metrics to characterize spatial interactions among pedestrians and for the validation of models to replicate crowd dynamics that consider the effects of groups under normal and emergency conditions

New generations of computer-based simulations of pedestrian crowd dynamics are aimed at improving both scientific knowledge about such complex social phenomena and practical use of the results for more efficient, safer management of pedestrian circulation dynamics in urban scenarios. Within this twofold framework, the validation of the developed models with field data plays a central role, and the acquisition of empirical evidence about human behavior during locomotion is mandatory. An empirical investigation of pedestrian personal space (i.e., the area surrounding an individual while walking and into which strangers cannot intrude without causing discomfort) is reported. The investigation was developed in an ad hoc experimental setting to collect meaningful data (e.g., measurements of the front zone of personal space under static and moving conditions). The main results are presented to illustrate both the adopted experimental setting and the related data-collection method. These results represent an innovative contribution toward defining a metric for the characterization of spatial interactions among pedestrians and allowing the validation of simulation models dedicated to the study of the dynamic behavior of pedestrian crowd phenomena

The paper reports the results of an empirical investigation on pedestrian dynamics in presence of groups. We consider a real world scenario in which an analysis of the impact of groups on the overall observed system dynamics was performed through manual counting and tracking. The simulation results are compared to empirical data and they show that the adopted model is able to produce quantitatively plausible results in situations characterised by the presence of groups of pedestrians

This work analyzes gendered processes by a methodology based on clustering factors with influence in the decision-making process of women as users or employees of the transport system. Considering gender as a social construction which changes over time and space, this study is based on the concept of a woman as a person who adopts this role in society. This paper performs a deep analysis of those factors women consider as needs and barriers to use or work in the transport system in four scenarios: railway public transport infrastructures, automated vehicles, bicycle sharing, and jobholders. A literature review and focus group discussions were performed under the consideration that the definition of woman includes the addition of several personal characteristics (age, sexual orientation, family responsibilities, and culture). The data analysis allowed the identification of fairness characteristics (FCs) that affect the interaction of women with the transport system for each scenario. A methodology for clustering the fairness characteristics identified the main areas of action to improve the inclusion of women within each use case. Further studies will be focused on the quantification and prioritization of the FCs through mathematical methods and the suggestion of inclusive measures by an interdisciplinary panel.

Pedestrian flows are characterised by the preponderant presence of groups, with particular reference to dyads. The paper presents a research focused on group and age-driven pedestrian behaviour in an urban crowded scenario. Data analysis was performed by using an open source tracker tool. Results showed that in situation of irregular flows (LOS B) dyads walked 30 % slower than singles, and that elderly walked 40 % slower than adults. The achieved results have been used towards the validation of the simulation platform ELIAS 38, with reference to the representation of the granulometric distribution of groups and heterogeneous speed profiles.