Across the world, growing population sizes and increasing urbanisation cause transportation networks to reach their capacity limits. In addition, the environmental impact of the transport sector, contributing an estimated 33% of carbon dioxide emissions in the UK for 2018, needs to decrease. Thus, environmental considerations and transportation needs necessitate an increase in trips completed by active, low-emission transport, such as walking.
Walking is healthy, sustainable and plays a crucial role in how urban places of work, leisure and living are accessed and used. According to the National Travel Survey 2017 for England over 80% of trips under one mile are completed on foot and considering that over 70% of trips between one and five miles long are completed by car, the potential for an increase in walking is substantial.
Getting more people to walk requires better infrastructure or policy interventions, such as clean air schemes, parking fees, or incentives for walking. Currently, planners and policy makers have to make do with data from surveys or localised pedestrian counts to inform their work. However, to decide which policies or infrastructure investments will work best in promoting walking, it is necessary to consider how pedestrian traffic varies over time across the entire street network of cities. For example, making walking more attractive in one part of a city centre may influence the footfall in other, potentially unexpected locations and possibly only at certain times, such as outside of rush-hour.
Despite the evident use for such information, pedestrian traffic is currently not mapped over time for cities. This project aims to change this and develop a theoretical framework for robustly constructing time-dependent pedestrian traffic maps at the scale of cities.
To future-proof the methodology, it will use pedestrian counts observed at distinct locations. These can be recorded via different, privacy-preserving technologies and do not rely on the voluntary participation of individuals or private sector service providers, as is the case for data obtained from personal devices, such as mobile phones.
Crucially, to ensure the traffic maps are robust to sensor failures and the occurrence of events or unscheduled disruptions, the theoretical framework will incorporate several predictive methods, each of which contributes different desirable properties, such as accurately capturing regular patterns based on historic data, efficiently interpolating between count locations and the capability to predict traffic dynamics from initial values without further data input. To directly inform the deployment of measurement devices, suitable data collection protocols will be established.
Outputs of this project will be useful beyond traffic monitoring. The ability of the methodology to forecast changes in pedestrian traffic caused by construction projects will be demonstrated and the relevance of pedestrian maps for assessing pedestrian exposure to poor air quality and for evaluating the success of businesses relying on passing trade will be shown.
This project will develop our understanding of city-wide pedestrian traffic and will therefore be directly useful for monitoring, across large spatial scales, long-term transport developments, short-term effects of disruptions or planned alterations and it will help the economy by informing the positioning and running of businesses that rely on passing trade, for example.
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