Organisation Name Smart Cambridge
Main contact Dan Clarke
Name of challenge The Movement Measurement Challenge: Design data collection for cycling and pedestrian movements.
Brief summary of challenge To inform transport policy and scheme design, and to measure the success of schemes, it’s necessary to measure the movement of people by all modes of transport. Smart Cambridge wish to improve cycling and walking movement data collection.  The focus of the data collected should include start and finish points, reason for journey and whether the journey was made in combination with other modes of transport (with overall start and finish points).  Additional data would include desire lines, displaced trips, new trips etc.
Context

Greater Cambridge is growing an estimated 36,000 new homes will be built by 2031 and an additional 44,000 jobs created. The transport infrastructure is already nearing capacity and congestion is a significant issue for the city. The Greater Cambridge Partnership (GCP) has a programme of significant transport schemes which aim to alleviate congestion and improve mobility for residents. The schemes are designed to shift behaviours away from cars and to encourage the use of more sustainable modes such buses, bikes and walking.

Smart Cambridge is part of Cambridgeshire County Council (CCC), a partner in GCP.  To inform policy and scheme design, and to measure the success of schemes, GCP needs to be able to measure the movement of people by all modes of transport.  Most transport modes offer some measurement possibilities relatively easily, but cycling and walking present particular challenges and there is no adequately robust data collected or available.
The benefits to be gained from addressing this challenge are… There are significant quality of life benefits, congestion means many people are wasting unproductive hours sat in cars. This is a key concern for employers as it impacts on staff retention. A reduction in congestion will help to address the issues of pollution the city faces particularly in the city centre.
There will also be significant economic benefit as congestion impacts on productivity and if unaddressed will mean that Cambridge is less attractive to new companies looking to relocate to the city.
Any work already done and status The Smart Cambridge programme recently undertook an audit to catalogue all the transport data currently held. Both cycling and walking were identified as areas where there is a shortfall of data.  The data that is currently held is in organisational data silos and is difficult to find and re-use.  The City has built an IoT network which consists of:

  • Real-time data platform – smartcambridge.org
  • A LoraWAN network with a self-service portal
  • A Sigfox network
Success would look like… A model for data collection showing explicitly:

  • Method of data collection
  • Hardware and software required
  • Data collected
  • Method of visualising the data
  • Method of analysing the data
  • Any data collection or functionality not envisaged by the Challenge Owner
Support available
  • Automatic inclusion as a participant in a planned city-wide trial
  • CCC can provide advice and support on the transport challenges as well as access to static data and infrastructure.
  • The Smart Cambridge programme has a network that includes local government, central government, other cities and access to tech mentors and VC funders that we would use to support the development of your idea. Our network includes big tech players like Amazon and Microsoft as well as small companies who lead the field in transport innovation and we have close relationships with networks such as Cambridge Wireless and Cambridge Network.
  • University of Cambridge can provide help and support on accessing the cities IoT infrastructure and real time data
  • We have a good understanding of current funding opportunities and can help to develop funding bids.
Key skills and technology sought
  • Data collection and analytics (particularly collecting transport flow data cycles, pedestrian traffic etc)
  • Machine learning and AI
  • Behavioural science
  • An understanding of transport systems
Key attributes of successful challenge teams Willing to work collaboratively with the Cambridge ecosystem.
An understanding of how new and emerging technologies can be deployed in cities to address challenges
Wanting to move to an actual trial deployment within the city.

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