While most of us take wayfinding and orientation for granted, instinctively utilizing our visual channels to do so, millions of blind people around the world face challenges and obstacles when attempting to perform the most basic tasks, such as walking to the corner store or using public transportation. As blind pedestrians lack critical information about the space they traverse outside the familiarity of their home, they are restricted, dependent on others, and have decreased quality of life. While assistive technologies for providing specific navigation solutions do exist, research is still limited regarding customized wayfinding solutions for blind pedestrians. This research aims at developing a wayfinding algorithm that relies on the OpenStreetMap mapping catalogue for planning accessible and safe routes specifically suited to blind pedestrians. In-depth investigations, observations, and interviews were conducted with blind people and with orientation and mobility instructors, in order to define and categorize spatial criteria relating to mobility, accessibility, and safety. Using controlled iterative experiments, weighted network graph criteria were defined, leading to the development of a route planning software that generates optimized routes for blind pedestrians. The developed software was then tested on a variety of routes, with the help of blind volunteers and orientation and mobility instructors. The results show that the optimal routes generated by the software were identical or very similar to those suggested by the experienced orientation and mobility instructors. Moreover, the blind volunteers also stated that the software planned routes were indeed more accessible and safer for them to walk along compared to routes suggested by existing commercial software developed for seeing pedestrians. The findings of this research indicate that our solution, based on OpenStreetMap and developed for the benefit of blind pedestrians, is effective and practical, and could improve the mobility, independence, and quality of life of this population, as well as increasing their integration into society.

The spread of COVID-19 pandemic provoked new policies and restrictions, which had an unprecedented impact on urban mobility and traffic on local and global scales. While changes in motorized traffic were investigated and monitored throughout the recent pandemic crisis in many cities around the world, not much was done on the changes in pedestrian street-traffic and walking patterns during this time. This study aims to identify, quantify, and analyze the changes in pedestrian traffic and walking patterns induced by COVID-19 policies. The “first wave” period of COVID-19 policies in Tel-Aviv, Israel, is used as a case study in this work. The analysis includes over 116 million pedestrian movement records documented by a network of 65 Bluetooth sensors, between 1.2.2020 and 26.7.2020, with a comparison to the equivalent time in 2019 that signifies “normal” pre-COVID-19 conditions. The results show clear correlation between the various COVID-19 policy restrictions and pedestrian count. The shifts to work-from-home and closure of businesses were highly correlated with changes in walking patterns during weekdays, while distinguishing changes in commercial and residential street segments. Nevertheless, while the restrictions dramatically influenced pedestrian movement volume and time of walking, it did not significantly change where people chose to walk, signifying the essentialness of attractive streets, parks and squares for citizens living in urban areas. This study shows how policy affects walking behavior in cities, demonstrating the potential of passive crowdsourced sensing technologies to provide urban planners and decision makers an efficient tool for monitoring and evaluating pedestrian infrastructure implementation in cities.

Pedestrian navigation is often guided by points-of-interest and visibility, yet most planning and design models ignore these, solely addressing street networks. Our innovative ‘POI VizNet’ tool utilises open-source geographical data for integrating points-of-interest and visibility into network-based framework. The tool was applied to the Fitzrovia redevelopment project in London, to support the reallocation of urban activities based on desired locations of various assets. Our results demonstrate the quantifying of location patterns according to the planning project goals, and the examining of urban activities while controlling visibility and accessibility. The developed method is aimed to assist researchers and developers in making more informed planning decisions intended to promote neighbourhood vibrancy and create a sustainable urban context with mixed land use that is desirable for pedestrians.