the impact of Covid-19 on transport planning
This booklet is a structured collection of the most insightful investigations developed by Transform Transport, the research unit of Systematica, since the Covid-19 outbreak. The unprecedented disruption caused by the pandemic has given rise to a large cultural and technical debate on upcoming new forms of urban development and mobility in an attempt to envision potential trajectories of new paradigms and keep up with the pace of our changing cities. Among all the possible urban challenges, walkability, universal accessibility, the public realm and living locally were recognized as crucial planning dimensions to tackle through a full-fledged approach. In particular, the recent focus on the ’15-minute citys’ paradigm represents a new perspective for evaluating sustainable, inclusive and resilient urban organisms, an inspiring point of contact between urban and mobility planning. The area of investigation, actual analytical test bed of this booklet, is Milan, which reacted promptly to Covid-19 through the implementation of its adaptation plan. Through innovative city reading instruments, multi-dimensional urban metrics and deep learning techniques, this publication explores the dense network of sidewalks as well as the actual accessibility to urban parks, other key components of the public realm and essential services; it investigates hidden people movement patterns through big data and video/image analytics, with the aim of arguing an emerging correlation that has identified, in the semi-central neighbourhoods of Milan, the most urgent urban issues to address. Calibrated on the city of Milan, this metric can be replicated in all global cities, with the aim of mapping urban structural gaps and inform effective intervention strategies.
Overcrowding and population density are by definition two crucial factors in the subject of pandemic risk. In a hyper-connected global world, inhabited by 7.5 billion people, 55% of whom are concentrated in urban areas (70% in 2050), emergency health risk situations are growing at an increasingly high rate.
Social distancing is the most effective measure to prevent and contain the spread of pandemics. The restrictions we are experiencing today may therefore become a continuous part of our lives and the way we inhabit and plan our cities in the future. Systematica took up this challenge by applying a methodology on Milan, replicable in any urban center, urban environment or metropolitan / urban area, for the mapping and the consequent adaptation of pedestrian infrastructures (sidewalks) in order to guarantee the fulfilment of the requirements of social distancing.
The preliminary results, that necessarily need to be verified in a later stage by a much more detailed data collection, is that more than 40% of Milan sidewalks area is not adequate to allow the necessary social distancing measures. It is therefore required, in line with the principles and objectives of “open street” plan currently being promoted by Milan Municipality, a capillary and diffuse series of interventions is required to retrofit our sidewalks and grant adequate comfort and safety levels for pedestrians.
The Covid-19 pandemic has dramatically influenced the way we live and move. Movement within the city has been dramatically reduced in both the number of trips and duration of movement with preference for the private automobile, rather than public transport. As a result, the neighbourhood scale has returned to being extremely important; ensuring density, proximity and diversity of services in a widespread manner within the city is beneficial in order to reduce the pressure on local public transport, while at the same time, granting accessibility to all those necessary functions that we need in our daily lives. This is a key topic, and was being addressed prior to the pandemic as a concept known as the ‘15-minute City’.
The ‘15-minute City’ is a term given to the vision adopted by the Mayor of Paris, Anne Hidalgo, whereby every resident is envisioned to be able to reach a number of essential services on foot or by bicycle within 15 minutes from their home. This concept rests on the idea of localized living, whereby residents could satisfy the bulk of their daily needs without the need for long motorized journeys. Across the globe, numerous cities have adopted similar models, ‘20-minute neighbourhood’ plans were already underway in Portland and Detroit in the United States, Ottawa in Canada and Melbourne, Australia. More and more cities are joining the movement. For example, the newly developed “Milan 2020 Adaptation Strategy” plan promoted by the Municipality of Milan for ‘Phase 2’ of post-lockdown containment makes reference to the same principles of the “15-minute City”: promoting the neighborhood scale guaranteeing services proximity, density and diversity.
Deep learning algorithms are a subset of machine learning algorithms, which process and combine their input in ever growing abstractions to obtain meaningful outputs. These are of particular interest in the field of computer vision, enabling the manipulation of large datasets in an automatic way. In the past years, the rising availability of deep learning techniques lead to new frontiers in the automatic understanding of images and estimating the number of objects within an image. In particular, deep learning methods obtained state-of-the-art results for image classification, object detection and instance/semantic segmentation tasks. Furthermore, in the past years, the availability of pre-trained weights for deep learning algorithms has grown.
This project focuses on the analysis of pre-trained deep learning models for object detection, image segmentation and crowd counting. The first two aim to recognize objects and locate them in an image, outputting their bounding boxes or shape masks; the latter aims to estimate the number of people.
The initial purpose was to assess their flaws and potentials given a dataset of images of Corso Buenos Aires, a well-known shopping street in Milan. First, a subset of images was selected, these represent different moments of the year and are used as a means of assessment for the performance of the models. The second goal was to investigate the use of the street for a year period. Thus, the best performing algorithms were used to perform an analysis of the use of the street from September 2019 until September 2020, with a focus on the Covid-19 pandemic lockdown period.
The development of ICT solutions for monitoring (Big) traffic data in real time enables transport planners and decision makers to understand and predict the travel behaviour of the city users. This research work is based on the analysis of the traffic data collected in Milan through a network of Wi-Fi sensors from the beginning of January 2020 to the end of July 2020. This was aimed at understanding the impact of the lockdown containment measures on urban mobility by merging time series analyses and GIS-based spatial analyses of traffic data.
Pedestrian Dynamics Simulation
In just a few months both nation-wide lockdown and post-lockdown phases have drastically changed citizens’ behaviors and mobility patterns in cities, neighborhoods, and streets. As such, it’s necessary to investigate the unprecedented changes and long-term effects of disruption on urban mobility. In this framework, Urban Informatics provides an innovative and multi-disciplinary walkability assessment tools and metrics for supporting the activity of mobility and transport planners in an efficient and effective manner within an evidenced-based approach.
In this framework, computer-based systems for the simulation of pedestrian dynamics provide optimized solutions for supporting the activity of transport planners and decision makers in the design of transport infrastructures. This is based on the possibility to evaluate key performance indicators (e.g. travel time, density condition, waiting time), to test the efficiency, comfort and safety of alternative spatial layouts and traffic management conditions (i.e. what-if scenarios) in a predictive and explanatory scheme.
Covid-19 has triggered a series of theoretical reflections and urban experiments, whether through research or physical urban interventions, which we, at Transform Transport, could not but be part of. We have witnessed these changes steered by the need to adapt to the new conditions that the pandemic imposed on us. We kicked off our research days after the outbreak of the pandemic and, today more than a year later, our efforts are relentless and our aim is to bring this urban research endeavour to a second level, which does not only look into the here-and-now, but also sheds light on how we plan our future cities. The pandemic pushed cities worldwide to change the way planners read cities, giving value to some assets more than others, such as the walkable networks, alternative modes of transportation, the benefits of changing working habits, and the immense capability of open space and the public realm in giving value to our daily lives and cities in general. The experiments and studies presented in this booklet will turn into a living legacy while planning out future cities, as we continuously introduce new parameters for measuring effectiveness and setting priorities. These studies will be further developed and consolidated to shift from being a simple analytical framework into a set of robust design principles that would steer the process of planning and city making. This is hinged on a profound understanding of our changing habits and the degree to which our cities need to adapt fast to abrupt changes. Our contribution to better cities through research might seem minimal however we firmly believe that developing new methods for reading the status quo will increase awareness and the sensitivity of our planning tools, thus paving the way for a gradual and everlasting change to the practice of urban and transportation planning.