Dynamic footfall data could be vital to inform local policy making…
Could the deployment of more public Wi-Fi help revive flagging High Streets? Two local authorities hope so: they’re teaming up in a public-private pilot that could prove central to reversing the tide — but it’s the data they’d generate that’s really crucial to their plans.
Harrogate Borough Council (HBC) and North Yorkshire County Council (NYCC) are currently co-tendering for a service provider (budget: circa £400,000) that can not only roll out a public Wi-Fi system, but can also use it for pedestrian monitoring.
Footfall/engagement data gleaned from those opting in to the public network could then be used to build data-driven public infrastructure and services. (With the nation’s High Streets in an ever more parlous state, retailers will be hoping for results — and supportive use of that data to underpin public policy [pdf] that supports local retail).
A simple example: data showing high levels of footfall outside of a given store at 7pm when it closes at 5pm could be used to incentivise changed hours of operation.
Matthew Roberts economy & transport officer for Harrogate Borough Council told Computer Business Review in a call: “It is intended that both NYCC and HBC will share the benefits of these networks as enablers for smart solutions to local challenges.”
“In that vein we have begun exploring shared opportunities around transport, health, destination management and waste management. A pedestrian monitoring system will allow the local authorities to monitor the health of our town centres over time and make better informed decisions around improvements and interventions.”
The two councils have set a budget of budget of £350,000-£400,000 to complete the project. Local businesses have also committed contributing to the project, in order to bring new technologies to the town centre that can drive economic outcomes.
Cities or towns trying to understanding the density and flow of pedestrians is not a new thing in itself. Traditionally, monitoring would be conducted by a particular junction, where a person or video software would count pedestrians, recording density and frequency. These methods have high costs, however, and are limited as they only give you data on very specific areas. Unlike vehicle traffic which is forced to move on predictable paths, people have free reign and at best select unpredictable paths.
Wi-Fi allows devices to connect to a wireless local area network using radio bands and is increasingly being seen as a way to augment existing approaches.
Whenever an electronic device has its Wi-Fi turned on it will send out a probe request seeking all Wi-Fi access points in the area. These requests can be harnessed in a manner that makes it possible to detect a pedestrian’s mobile devices and determine the direction they are traveling in, including how long they stay in certain locations.
Importantly this data can be collected in an anonymous manner that doesn’t break an individual’s privacy rights.
A research paper from New York University on estimating pedestrian densities noted that cities and towns have incredibly congested airwaves as an array of devices and sensors constantly ping each other for information.
The 2017 paper notes that “Wireless sensors can detect not only the mobile devices around them but also the non-mobile devices, access points, and other networks disseminating their presence to the network. Therefore, it is critical to build efficient algorithms to filter the raw data captured by the sensors.”
In 2016 Transport for London (TfL) meanwhile ran a public trial of Wi-Fi monitoring technology over the course of a month.
During that month they recorded more than 500 million de-personalised Wi-Fi connection requests from 5.6 million devices across 54 stations.
The data collected showed TfL what paths people were using to access stations as well as which platforms and lines they got onto. Importantly this system was able to track commuters throughout the network, revealing how passengers hop from train to train.
This information becomes highly useful when the train network is disrupted by line closures following an accident or signal failure.
This actually occurred during TfL’s trial when the Waterloo & City line was suspended. The system tracked over 4000 passengers as they changed their travel plans on the fly, selecting different routes.
TfL noted in its report on the pilot that “this data is valuable from both a customer and operational perspective as it enables a more informed response to changes in network conditions. It may help customers plan their journeys and allow us to look at the advice we give them, how and where we support them as they travel across the network, where we deploy our staff to offer assistance and how we operate our stations and trains to ensure the quickest and most comfortable alternative journeys.”
Whether it can help revive High Streets amid the seemingly unstoppable rise of internet shopping and in the face of often unhelpful councils (i.e. raising local parking fees, heightened local business rates, etc.) remains an open question.