TOC

  1. Preamble
  2. What are Footfall Sensors? How Do They Work?
  3. What Data Can Be Captured Using Footfall Sensors?
  4. What Can Yarra Trams Do With This Information?

Preamble

I saw something I’ve never seen before on a Melbourne Tram this morning. Aboard the 109 travelling down Collins Street, I looked up at the door of the of the tram I’d just stumbled onto and noticed a sensor. In fact, they were on each of the doors looking straight down. As a data-obsessed individual, and someone who is endlessly curious, I love looking around for infrastructure and objects with functions that are a mystery to me. Here’s what I saw:

The first time I saw a sensor that looked like this—two binocular cameras pointed down at an entrance—was at my old University RMIT. The shape of it gave hints at what it might do but it still took me a session of furious googling to figure it out. That sensor, and the ones I saw this morning on the Tram, are called footfall sensors or generically as people counters. Let’s dive into them a bit more.

What are Footfall Sensors? How Do They Work?

Footfall sensors count the number of people entering and exiting a space, whether it’s a building or a tram in this case. The next time you walk into a shop, particularly global retail brands like Nike or Adidas, look up and check if you can see something that looks similar to the photo above.

In the past, businesses would use something called a beam counter, which is an invisible laser pointed at a mirror that senses if the beam is interrupted and measures that. However, most beam counters can’t differentiate between people entering and exiting a space, but footfall sensors can.

FootfallCam 3D PRO2 Datasheet
If you’re interested, here’s a datasheet for a footfall sensor, which includes more detail about what they’re capable of. Datasheet: FootfallCam 3D Pro2 Datasheet.pdf Catalogue: FootfallCam 3D Pro2 Catalogue.pdf

Footfall sensors have two cameras for the same reason we have two eyes; depth perception. This most importantly improves accuracy and helps the processor differentiate between people and objects, as well as a bunch of other functionality.

What Data Can Be Captured Using Footfall Sensors?

Footfall sensors enable a rich portfolio of data to be collected, beyond simple entries and exits. Here are some examples:

  • Traffic through different entrances and exits
  • Dwell times; how long people are spending in a particular area
  • Queue times in congested areas or checkpoints
  • Total occupancy at any time
  • Bounce rate; people walking in and straight out
  • Busiest Time of Day
  • Differentiate between Adults and Children

This raw data becomes far more valuable when combined with other information from the business. I’ll leave you with some more ideas at the end of this post, but here’s something to whet your appetite:

  • Conversion rate; how many sales per customer are we making?
  • Wait times vs. Staff scheduling; do we need more staff on at busy periods?
  • Energy Usage vs. Occupancy; how is energy usage affected by occupancy?

What Can Yarra Trams Do With This Information?

Footfall sensors unlock so much potential for a company like Yarra Trams. With sensors on each door of the tram, and coupled with all the data that they would already be collecting, the possibilities are endless.

If these were deployed on a network scale, Yarra Trams would be able to unlock analyses that could make their services more efficient and effective. Here are some ideas from my perspective as a data analyst:

  • Which routes are the busiest, and when? Do we need to add more trams at these times?
  • How congested are our trams? Are there a lot of people congregating at the doors, and is that safe?
  • What is the ticketing compliance like? How many people are riding the trams vs. Myki touch-ons?
  • How many passengers try to get on but can’t due to congestion?

This addition to the Yarra Trams network is extremely exciting for someone like me, and I’m excited to see if there are any improvements that can come as a result of it. There are so many latent data opportunities just waiting to be tapped and this is one of so many.

If there’s anything I’d like you to take away from this, it’s to look up and around you. There are so many experiments and installations hiding in plain sight.

Until next time my friends.

Love,
Dan Lawson

PS. If you’d like to learn more about me check out my personal blog, LinkedIn Profile, or my Tableau Public Profile.

Daniel Lawson
Author: Daniel Lawson

Right off the bat I can tell you that I’m not your average data analyst. I’ve spent most of my career running my own business as a photographer and videographer, with a sprinkling of Web Development and SEO work as well. My approach to life and work is very T-shaped, in that I have a small set of specific skills complemented by a very broad range of interests; I like to think of myself as a dedicated non-specialist. Data Analytics, and Programming, started as a hobby that quickly grew into a passion. The more I learned the more I looked for opportunities to pull, manipulate, and join data from disparate sources in my life. I learned to interact with REST APIs for services I used, personal data from services I use like Spotify, and health data captured by my devices. I learned SQL to create and query databases, as well as analyse SQLite files containing my iMessages and Photos data on my Mac. Every technique I learned opened up more possibilities; now I’m hooked and there’s no turning back. Learn More About Me: https://danlsn.com.au