Congestion ahead

Use case: people flows and congestion

people

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. Knowledge about pedestrian movements, and ways to respond to them, could be valuable to help provide better services and a tailored experience, within campus and connecting to the wider local environment.

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, such as:

  • 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
  • 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. 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
  • Where campuses interact with local towns and cities, for example crossing roads, using transport services or local shops and cafes, the changing flow of people could be used to increase the capacity/timing of pedestrian crossings, frequency of transport services or adding extra staff on to the tills and counters
  • 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 more take away coffee might be ordered, and campus cafes could expand capacity using picnic tables and pop up kiosks.
  • Similarly if rain is starting as lectures finish, students might be re-routed away from slippy paths, and may be expected to congregate in sheltered areas. 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
  • It would also be possible to combine individual preferences (based on specified likes and dislikes or modelled on their behaviour) to anticipate more specifically what an individual is likely to do next. From the above examples, it might be known what drink is normally ordered from the cafe, what bus route is taken and when an individual tends to do their work in the IT suites. Combining this data across multiple individuals provides a greater level of detail in planning what bus services to run, what drinks to stock and the usage of computer rooms.

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.

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 are used on some buses and trains for example to tally against fare income or check usage of different routes.

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.

A prototype LED smart pedestrian crossing in London only reveals the crossing when pedestrians appear and can expand to cope with higher numbers of people, or to move the stop lines further back if weather conditions are poor, to allow for increased stopping distances of vehicles.

People counters are 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.

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?

Who needs to be involved?

Pedestrian flow information could be valuable to a wide range of audiences, both within campus and in the wider community. The providers of academic services (such as timetabling), social facilities (bars and cafes) and retail businesses could all be users of the data. In addition, integration with events planning and transport services could impact on local authorities, transport providers and the police.

It’s too noisy!

Use case: noise and sound

radio

What’s the issue?

A significant amount of teaching is still delivered aurally. Face to face learning activities, for example collaborative group work, also typically use forms of communication between students that rely on a conducive acoustic environment. In addition, many students live in close proximity to each other, and engage in social activities that can create noise. So sounds are both an important element of learning and the university experience for most students and also a source of annoyance.

High levels of noise can be damaging to health, and noise pollution or noise nuisance reflect unreasonable amounts of noise within an environment. These can sometimes result in the local authority taking action against those creating the noise. However, in most cases noise is more of a distraction or annoyance, affecting your ability to carry out the activity you intended. The impacts can include disruptions to sleep, increased stress or anxiety, or simply poor concentration or productivity. Historically, responses to noise problems are infrequent and complaint driven, so have limited impact on the situation at the time.

What can be done?

The level of “annoyance” experienced as a result of the noise is subjective, and can be moderated by a number of factors including sensitivity to noise, the ability to control the noise, the predictability of the noise event, and other environmental “comfort” factors.

The Intelligent campus offers opportunities to provide control and information that can reduce the amount of annoyance. Equally, responding in real time to triggers of certain noise levels could result in additional mechanisms to reduce the noise at its source or by insulating the sound from those who are impacted.

The idea of quiet study spaces already exists, and even some halls of residence are designated as quieter spaces, although they are typically static, not responding to environmental changes, and relying on self-regulation or human monitoring and intervention. What would be more effective would be to tailor spaces to the individual or group’s activities and preferences, and respond immediately to noises that impact negatively.

Environmental sensors can continuously monitor the acoustic levels of both indoor and outdoor spaces, and report this information through a variety of interfaces including mobile devices. For example apps could show you the current noise levels in busy bars, study rooms or computer suites, enabling the individual to choose a location appropriate to their activity.

Newer sensors also sometimes have processing capability, including being able to identify and categorise different types of noise, for example recognising the difference between traffic noise, live music from a band in the student union, or the sound of shouting and screaming. This allows for a differentiated approach to various acoustic triggers.

Making it intelligent

Integrating different types of data can provide a rich source of information about noise events and their context, allowing more informed responses. Knowledge of event timings (eg music concerts), and the movement and congregation of people across campus (through location information), can start to build a picture of where noise might build up and what neighbouring groups or activities might be impacted (people sleeping, or doing quiet study).

Nudging strategies, for example recommending different routes or transport options could help to disperse noisy crowds, suggest less busy bars, or direct crowds away from exam rooms. Less direct than alternative routing could be signposting special offers or “things you like” nearby to entice you elsewhere – food in the cafe, a lunchtime club attended by people you know, a book you wanted just arrived in the library… Knowledge of the intended timing (and the actual happening) of noisy events or quiet study activities could also influence the schedule of maintenance works.

Noise cancelling technology exists in more sophisticated headphones, and could be applied to larger environments such as study rooms where external noise is detected and unwanted. The data identifying noise events could be combined with data on scheduled quiet events to effectively target noise cancelling, or even respond to mobile controls by individual users. Weather could also be incorporated into the decision strategies, for example if windows are more likely to be open on a hot day, allowing noise to carry further.

headphones

Are there any examples?

A research partnership between the City of Calgary and The University of Calgary in Canada used acoustic sensors to autonomously detect and categorise acoustic events. The technology was able to pinpoint the location and time of the noise, and use machine learning to differentiate between different noise types including music and construction. The authorities were notified when legal levels were exceeded, allowing action to be taken, and noise patterns over time were mapped and correlated to better understand acoustic impacts of different activities.

In Louisville Kentucky, similar technology allows a drone response to gunshots. Thankfully such incidents are rare in the UK, however other applications could be imagined, for example providing monitoring and aid to individuals in distress or subject to attacks.

In Singapore, at Nanyang Technological University, researchers have applied noise control technology (such as used in headphones) to cancel external noise. They formed a grid-like array of units on an open window, which reduced up to 50% of noise from traffic and construction. It detects the noise and models it in real time, producing an inverted waveform to cancel out the noise as it happens.

Ethical issues

In some of the real examples above, the perception of the introduction of “surveillance” was opposed by some, but the advantages of accurately targeting serious incidents were argued as being better for privacy than blanket CCTV coverage. As with all examples involving sharing of data, the collection and interpretation of information about individuals, such as their location or behaviours, is sensitive and needs to follow a strict ethical code.

Who needs to be involved?

With the increasing integration of data to monitor, model and respond to acoustic events and the wider environment, a broad range of stakeholders and data could be involved. This includes academic services such as timetabling; estates services including catering outlets and buildings maintenance; student social activities such as clubs and events; campus events including concerts; local authorities for roadworks, construction activity, traffic management and transport operators.

Intelligent Campus Community Event – University of Birmingham – 7 May 2019

Photo by Peter Clarkson on Unsplash

If you are working in the area of the Intelligent campus and are interested in work being undertaken in this space by others, then we would like to invite you to attend one of our community events.

The community of practice gives people an opportunity to network, share practice, hear what various institutions are doing and what Jisc is doing in this space.

  • Smart City
  • Smart Campus
  • Wayfinding
  • Wi-Fi Heat Mapping
  • Mapping
  • Space Utilisation
  • Smart Buildings
  • RFID tracking
  • Wi-Fi tracking
  • Facial recognition
  • Chatbots
  • Robots
  • Artificial Intelligence
  • Learning Spaces

The fourth of these events is being hosted and  taking place at the University of Birmingham’s Conference Centre, Edgbaston Park Hotel in
Birmingham on the 7 May 2019 from 10:00 to 4:00, and lunch will be provided.

Please put this date in your diary, you can book onto the event using this link

https://www.jisc.ac.uk/events/intelligent-campus-community-event-07-may-2019

You will have the opportunity to discover more about the Jisc project that is being undertaken in the Intelligent Campus space as well as hear from others about their work in this exciting topic. There will be plenty of opportunities for discussion and networking.

Take part in our Internet of Things competition – Deadline 8 March

Following our recent collaboration with The Things Network (TTN) in equipping the Janet Network with a dedicated infrastructure and a portal for long range wide area network (LoRaWAN) technology, we have started a new IoT programme to raise awareness about potentials of this technology among our community. We want to hear new ideas and existing challenges that can be addressed by LoRaWAN technology. Under Jisc and Digital Catapult initiatives, up to ten winners will receive LoRaWAN Gateways on a long-term loan. These can be used to test your idea as a small-scale pilot.

We want to hear from you if you have an idea that could improve:

  • Teaching
  • Learning
  • Campus operation
  • Student experience
  • How a university or college plays a role in enhancing citizens’ quality of life

Find out more

https://www.jisc.ac.uk/rd/get-involved/take-part-in-our-iot-competition

The deadline is the 8 March 2019.

Reflections on the Intelligent Campus Community Event at City

The Oliver Thompson Lecture Theatre at City, University of London was the venue for the third of our Intelligent Campus community events. These events give people a chance to network, share practice and hear what various institutions are doing. You will have the opportunity to discover more about our intelligent campus project and our work in this space. We had over a hundred people turn up for the event, for many of whom this was their first community event.

After a joint welcome with Dom Pates from City, Jisc’s James Clay gave an introduction to the Intelligent Campus, explaining where the project came from, what we were doing and where we intended to go. Continue reading

Consultation for the Intelligent Campus Data Protection Impact Assessment Toolkit

Andrew Cormack, Jisc’s Chief Regulatory Adviser has been working on a Data Protection Impact Assessment Toolkit for the Intelligent Campus.

DPIA Toolkit v0-11

This is a draft of a Toolkit for applying Data Protection Impact Assessments to Intelligent Campus applications. This has been derived from an RFID Toolkit approved by European Regulators in 2011, with input from other experts within Jisc.

We are seeking input from the community on the toolkit and welcome feedback.

Please either e-mail feedback to Andrew Cormack, or post in the comments.

Internet of Things (IoT) workshop – Friday 15th February 2019

We would like to invite colleagues from higher and further education to our Internet of Things Workshop in London on the 15th February 2019.

This workshop offers an introduction to LPWAN and LoRaWAN technology, in collaboration with Digital Catapult.

Those who have undertaken work in the intelligent campus space will realise the potential benefits of using IoT networks in connecting sensors, vehicles equipment, and other connected devices to a network.

The workshop has two purposes:

  • To learn more about LPWAN and LoRaWAN and to meet SMEs working with the technology
  • To help shape a Jisc and Digital Catapult initiative which will lend gateways to a small group of universities and organise SMEs to respond to IoT challenges that the universities set

The event takes place at Digital Catapult, 101 Euston Road, London, NW1 2RA – Friday 15th February 2019.

Book now

Time for a story

It was raining and Leda was off to her University for the day. Her phone had already sent her notification to leave for campus early as there was a lot of traffic on the roads and the buses were being delayed. She got to the bus stop earlier than usual and within a few minutes the bus arrived. On the bus, on her phone using the University App, she looked over her schedule for the day. There were lectures, a seminar and she also had a window to get to the library to find those additional books for the essay she needed to hand in next month. She was hoping to catch up with some friends over coffee. There were some notifications in the app, the seminar room had been changed, there was a high chance that the library would be busy today. Leda looked out of the window of the bus at the rain. Today was going to be a good day.

The bus arrived at the campus and Leda got off, she checked her app and started to walk to her first lecture. As she passed one of the campus coffee shops she was sent a notification that three of her friends from the course were in there, so she checked the time, she had the time, popped in and found her friends. Her app let her know that she had enough loyalty points for a free coffee, well why not, Leda thought to herself, she could check if there were any additional resources for the lectures today.

coffee

As Leda drank her coffee, she reflected on why she had chosen thus university. One of the things that had attracted her was the positive reviews and feedback that had come from existing and previous students on the whole student experience. This positive view of the university had resulted in her putting in an application. She was reminded though of one of the induction sessions where the University had taken the time to discuss the whole concept of the gathering of data, the processing of that data, the what interventions were possible and the importance of consent at all three stages. She did worry about this and wondered if all appropriate mechanisms and security was in place to protect her personal data. As she finished off her coffee, she did think was all this data gathering really necessary?

Leda’s phone buzzed, she needed to be at her lecture in ten minutes, however the room was different to the one she was usually in. Leda didn’t concern herself with this, as she knew that the phone would direct her to the room quickly and efficiently. What was so great about this, Leda thought to herself, was that the sessions she attended were always in the right kind space. Sometimes her lecturer wanted to do group work and the usual lecture theatre wasn’t appropriate, so having that in a more suitable room allowed her and her friends to focus on the learning.

As Leda walked around the campus she noticed that there was a lot of devices attached to ceilings and walls. She recognised the CCTV style cameras, though some looked more like speed cameras with some kind of sensor. She had also seen devices with lights in the classrooms and the lecture theatres. Leda made her way to her next session, she used the Wayfinding app on her phone as she knew due to building work on the campus, her usual route was closed. The app would give her the fastest route to get there. As she walked into her seminar room she touched her RFID enabled smartphone to the touchpad by the door. This registered her attendance, but the app recognising her location, started to download the resources for the seminar to her phone and registered her device for the polling and audience response system. Leda found the process much more transparent than being given a clicker. She liked being able to use a single device, her phone for all her smart campus interactions, rather than using a range of devices, cards and equipment to do so.

When Leda had started her degree programme she had been concerned about how data on her was being gathered, processed and acted upon. It was apparent from the start that her journey through the university, both academically and physically would be tracked. She was happy though that the University had published a guide for students on the ethical use of data. She was aware of what data she had to provide and other data about her for which she had a choice on whether it was collected or not. Leda with her friends had been looking at the open algorithms the University used and had been playing with some of them to see if there were any interesting insights into the way her and her friends interacted with the university systems and the campus.

Though Leda had concerns about her personal privacy with all the data gathering happening on campus, her and her friends had noticed a reduction in crime and vandalism. When incidents happened on campus, reaction time from the campus security officers was really fast they could get to the right place much quicker. Leda did think it was all a bit Big Brother, but did feel safer.

Leda was sitting in the library reading through the book she had borrowed, her phone buzzed with a notification, her bus home was due shortly and if she left now, she would be able to catch it. Leda really liked this as though there was a bus timetable, the realities of traffic and weather meant that the buses weren’t always on time. The bus company used GPS to identify the exact location of their buses and this data could then be used by the university app to help learners catch their buses on time. One of the reasons Leda liked this was that it was raining and it saved having to stand in the rain for too long. As Leda sat down in the bus, her phone buzzed again, as she had walked from the library to the bus stop, the phone had downloaded an interesting podcast related to the lecture she had been to ready for her to listen on the journey home.

As Leda settled down for the evening, she reflected on her day. What kind of day would have it been without her phone, without it connected to the different services on campus, the way it worked in a smart or even intelligent way. It was making her whole experience better, she could focus on her studies and spend a lot less time trying to find rooms. The university called it the intelligent campus, in Leda’s view it was more than that, it was a campus that improved the whole student experience. Well for her it did.

The Intelligent Learning Space

Photo by Philippe Bout on Unsplash

So what do we mean by a learning space and how is an intelligent learning space different?

Though the main thrust of the Jisc Intelligent Campus project is looking at how we can extend learning analytics to include and incorporate physical data, there is also space to discuss peripheral and related issues to the work. One aspect of this is the development and design of learning spaces as well as the use of data gathered from the use of learning spaces.

Generally most learning spaces are static spaces designed to allow for particular kinds of learning. Some have an element of flexibility allowing for different kinds of learning activity.

Often the pedagogy is shoe-horned into the space that is available and even if more appropriate spaces are available on campus, often they are unavailable for that particular slot or cohort.

Photo by Nathan Dumlao on Unsplash

A smart learning space would taken into account historical usage of the room and how people felt that the space either contributed or hindered the learning taking place there. You can imagine how users of the room could add to a dataset about the activities taking place in the room and how they felt it went.

You would think that data from the timetable could allow for this automatically, but timetabling data tells us about the cohort, the course they are on and the academic leading the session, most timetabling software doesn’t have the granular activity data in it.

The course module information may have the plans of the activity data within it, but may not have the room data from the timetable, nor may it have cohort details. You could easily imagine that some cohorts may be quite happy with undertaking group activities in a lecture theatre space, but there may be other cohorts of students who would work more effectively if the space was better at facilitating the proposed learning activity.

Likewise when it comes to adding feedback about the session, where does that live? What dataset contains that data?

Then there are environmental conditions such as heat, temperature, humidity, CO2 levels, which can also impact on the learning process.

So an actual smart learning space would be able to access data about the session from multiple sources and build a picture of what kinds of learning spaces work best for different kinds of learning activities, taking into account factors such as cohort, environmental conditions, the academic leading the session and so on…

These datasets could also be used to inform future space planning and new builds, but smart learning spaces are only the beginning. Taking a smart space and making it intelligent is an obvious next step.

An intelligent learning space would take this data, and then start to make suggestions based on the data. It would identify possible issues with the learning plan and make recommendations to either change the learning activities planned, or recommend a more appropriate space. An intelligent learning space would adjust the environmental conditions to suit the activities planned for that spaces, rather than users of the space having to manually adjust the conditions when it becomes too cold, too hot, too bright, stuffy, etc….

classroom

Making the timetabling software intelligent, well dynamic, could mean that rooms are not allocated to cohorts of students for a set amount of time, but rooms are allocated based on pedagogical need and student need and done as and when needed.

One of the key issues with all this is to collect and store the data somewhere, a centralised hub would be critical and that is something Jisc have built for the analytics service and would be used for the future Intelligent Campus service.

Intelligent Campus Community Event – City, University of London – 17th January 2019

vine-1010002_1920

If you are working in the area of the Intelligent campus and are interested in work being undertaken in this space by others, then we would like to invite you to attend one of our community events.

The community of practice gives people an opportunity to network, share practice, hear what various institutions are doing and what Jisc is doing in this space.

  • Smart City
  • Smart Campus
  • Wayfinding
  • Wi-Fi Heat Mapping
  • Mapping
  • Space Utilisation
  • Smart Buildings
  • RFID tracking
  • Wi-Fi tracking
  • Facial recognition
  • Chatbots
  • Robots
  • Artificial Intelligence
  • Learning Spaces

The third of these events is being hosted and  taking place at City, University of London on the 17th January 2019 from 10:00 to 4:00, and lunch will be provided.

Please put this date in your diary, you can book onto the event using this link

https://www.eventsforce.net/jiscevents/434/register

You will have the opportunity to discover more about the Jisc project that is being undertaken in the Intelligent Campus space as well as hear from others about their work in this exciting topic. There will be plenty of opportunities for discussion and networking.