Intelligent Campus and coronavirus planning

solitary
Photo by Philippe Bout on Unsplash

For a few years at Jisc I was working on the Intelligent Campus project and then got a new role as Head of HE and Student Experience. I still have an interest in the space and when I read this recent post from WonkHE, Can we plan for a socially distanced campus? interesting and useful for the planning for September.

We know how to operate a traditional on-campus model, and we are very quickly developing a better understanding of how to facilitate off-campus working and learning, but how can we best support social distancing on a functioning campus?

Is this what social distancing looks like in a lecture theatre? via WonkHE Seminar.

https://wonkhe.com/blogs/can-we-plan-for-a-socially-distanced-campus/

I was reflecting how if the concept of the intelligent campus was further advanced than it is, how potentially helpful it could be to support universities planning for a socially distanced campus.

I published a use case a year ago, on people flows and congestion,  and it gave me an idea of updating it to reflect the current challenges that universities and colleges will face in September.

With the impact of the coronavirus and the need for social distancing and tracing contacts, if there was ever a use case for the concept of the intelligent campus then this is it.

What’s the issue?

The flow of people through campus and beyond is complex and not well understood outside of known peak times such as class changes or lunchtime. The density of people at any one place and time, and the speed of their movement, can have a big impact on how easily people can get in and around campus buildings and facilities. This can have an impact on the need for effective social distancing. Universities need to avoid situations arising which result in large numbers of people congregating in areas which could result in failure to maintain social distancing.

What could be done?

Pedestrian flow could affect the time for journeys between classes, waiting times at cafes or sudden changes in how busy the library is. Location trackers such as used by mobile phones can provide data on flow, and also people counters, such as using video systems, can be placed around campus to collect data on the numbers of people in that location at any time. Such data can have a number of applications, including combining with other contexts to improve services, as well as ensure social distancing.

Monitoring the increasing numbers of people towards a known destination could anticipate potential problems with congestion and queueing. For example, students heading towards the cafeteria could indicate an unusually high demand for food and trigger staffing or stocking changes to cope with higher numbers. You could also use the information to alert students that the space will be busier than normal and due to social distancing there would be longer queues and waiting times.

Timetabling data indicates when classes are scheduled to end, but real time data on movement could indicate that some classes finish earlier or later, leading to changing patterns in availability of services. This could be critical if you are using timetables to stagger the movement of people to ensure social distancing and avoid congesting and crowding.

Library
Image by RHMemoria from Pixabay

Usage data could show that the library is already busy when one class ends, and students could be directed towards other study areas or computer rooms that have more availability and more space.

Where campuses interact with local towns and cities, for example crossing roads or using transport services, or where students are using their cars. The changing flow of people could be used to increase the capacity or timing of pedestrian crossings, to avoid congestion. Likewise the  frequency of transport services could ensure that sufficient public transport is in place for both local people and students. Real time traffic information could allow students to make decisions about when to arrive for university on time or when would be the best time to leave.

Tram

Over time the data may suggest interesting patterns of behaviour that could be used to further predict, anticipate and respond to congestion. One example might be the impact of weather – on sunny days students may spend more time outside, whereas when it’s rainy they may congregate in specific spaces. This behaviour will impact on those trying to ensure social distancing in spaces such as corridors and learning spaces such as the library.

Using room utilisation data, spare rooms could be opened up to accommodate social interaction and refreshment breaks, or pop up library or IT services could be opened. Ensuring that social distancing guidelines are kept to.

What examples are there?

Many of the existing examples are from “Smart cities”, involving vehicular and pedestrian traffic, to aid safety, improve health and environmental concerns, and also inform retail and business. However, such applications can be easily applied to campus routes and facilities.

Google maps is one of the best known examples of tracking the location of mobile devices (typically in cars) to show congestion on traffic routes. The mapping service then can suggest the best/quickest route for the traffic conditions at the time and provide alternatives if congestion is estimated to lead to a slower journey time. Waze (owned by Google) does something similar, but allows individuals to add information about congestion. This type of system could be really useful in a campus context.

Other methods of “people counting” include video cameras, which can also combine with CCTV, recognising an image of a person and transmitting the numbers (usually not the images). Such systems could be used to flag spaces which are getting congested or filling up.

Image by Free-Photos from Pixabay
Image by Free-Photos from Pixabay

In Las Vegas, not only do they track vehicles through a junction but also count the number of pedestrians crossing the streets and also “jaywalking”, and then re-routing vehicular traffic when the numbers of pedestrians is high. Could a similar system ensure that students are re-routed when their chosen route is getting crowded.

People counters are often used in business and retail areas for example in Manchester to better understand queuing time and which areas of a store are popular. The data also contributes to strategies to improve walkability and transport, understand the impact of events and marketing campaigns, and assist businesses and community services in adopting appropriate staffing and security arrangements. These systems could be adapted to ensure safe spaces for students on university campuses.

Sphere
Image by Picography from Pixabay

What about ethical and other issues?

In principle, data on people movement tends to be aggregated to use the total numbers and changes to those numbers rather than knowledge about a specific individual. This is similar to the way google uses your location to provide mapping data, and is widely accepted. However, images of individuals may be being captured along with their movements and this information could be used inappropriately without strict controls and clear consent rules. Similarly, as data becomes combined, it begins to create a picture of a person’s behaviour that could be considered more of an invasion of privacy – for example which cafe are they going to, who else is there and what do they drink?

It’s important that the ethical aspects of this are taken seriously, and the excuse “it’s a crisis” shouldn’t be used to increase surveillance of individuals and impact negatively on privacy. Transparency of what the university is doing and why is key.

University of Leeds - Leeds Business School
Leeds Business School

Conclusions

With the impact of the coronavirus and the need for social distancing and tracing contacts, if there was ever a use case for the concept of the intelligent campus then this is it.

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