As part of CityVerve, the UK’s largest smart city demonstrator, See.Sense undertook a pilot project in conjunction with BT, with the objective of generating a source of data and insights on cycling in Manchester. These insights would help Manchester make data-driven decisions on the provision of cycling infrastructure, assist in future planning and would also encourage more people to cycle in the city.
Over the course of 9 months, in collaboration with our CityVerve partners (including BT, TFGM, Manchester City Council and Ordnance Survey), we worked with 180 participants to map cycling in Manchester like never before. Members of our closed city trial opted in to share their anonymised and aggregated data (collected via their See.Sense ICON bike light) to help map and measure safety, road conditions, journey mapping and perceptual information. The real-time data was then visualised in the BT IoT data hub, and combined with further data sources.
The See.Sense Community
With our background as a product company we make sure that the people using our products are at the centre of everything we do. This philosophy is also ingrained in our Smart Cities work and our approach to communication ensured a high level of engagement throughout the pilot project. There was a high participation rate of cyclists actively collecting data, meaning that over 5,000 journeys were recorded and over 30,300km logged. Furthermore, through the survey within our App over 385 ‘annoyances’ were recorded and pinpointed in our database
Through the trial we held a number of community events to better understand how the data and insights from our trial could be used to help both cyclists and cities. Through these events we found that there was a strong correlation between our data and the experience of our community out on the streets. Alongside participant workshops and open hackathons we also published regular newsletters sharing insights gathered from the trial data.
So What Did We Find Out?
One of the challenges typically faced by app-based collection of cycling data is that it tends to be skewed towards a narrowly defined group of users. In our trial, we were able to demonstrate a profile that was broadly representative of commuters in Manchester, with most trips occurring on weekdays during the AM and PM peak periods. We were able to establish a diverse group of participants with higher than average numbers of female cyclists and a representative mix of age groups.
Measuring The Impact Of Cycling Infrastructure
Through our trial we were able to measure the impact of cycling infrastructure. The image on the left shows a section of street without cycling infrastructure with yellow lines representing cyclists moving north and blue lines showing cyclists moving south. The image on the right shows the direction of travel along Oxford Road which has detected cycling infrastructure. Not only is there far greater delineation between the direction of travel but the efficiency of moving cyclists with dedicated infrastructure is highlighted with the higher average speed resulting from greater confidence. These observations may seem obvious but this type of information has never been quantified before in Manchester and has the potential to help decision makers allocate resources for future funding.
Perception Data Through our app our community were also able to share their thoughts and feelings on their rides, sharing positive experiences about their journey or highlighting areas they felt scared or annoyed. Over 385 of these data points were collected over the course of the trial, giving us valuable insights which helped qualify the patterns we found in our quantitative data sets.
Roughness Using the patented sensor technology within the ICON light unit we were able to map levels of road roughness across the city. With road surface conditions being one of the top contributory factors towards collisions, as well as massively affecting the comfort of the cyclist's ride, this improved understanding will greatly help with planning for cycle infrastructure investment.
Our data also highlights key junctions and intersections where cyclists had to wait an extended period of time before being allowed to continue with their journey. By accurately mapping these locations (including areas of acceleration and deceleration), we are able to inform decisions around removing barriers to momentum.
This video is a data visualisation produced by OS, using See.Sense data. It was presented at the CityVerve Marketplace Demo Day.
Through the trial we were able to rapidly provide a highly accurate baseline of the conditions faced by cyclists in Manchester with the data gathered helping monitor future changes to the cycling network. Not only this, but the trial provided an effective tool for engaging with cyclists and promoting cycling in the city to a wider audience.
This has changed the way planners in Manchester understand where everyday cyclists are going, their environment and how they respond to variances in cycling provision in the city. Using the data, they can attend to road surface problems and change traffic management systems, reviewing the data before and after to measure the impact of their interventions. This is a valuable resource that will enable Manchester to reach its ambition of making the city a better place to live by getting more people on bikes more often.
The project is a great example of organisations of all types and sizes collaborating within the CityVerve project to deliver outcomes benefiting the citizens and businesses of Manchester, now has the potential to be replicated in cities around the world.
As ever, a huge thank you to our 180 participants of the trial, thank you for making this possible and for making cycling better in Manchester!
Features And Recognition
The achievements of the project have been recognised in Manchester and beyond: