Our Vision
Pelation created REBO with a clear vision in mind...
To get people moving (whether they are commuters, families, children, or elderly) on a bike from one place to the next without a fear of safety on the road in a healthy city with clean air and open streets.
Cycling is a low-carbon and cost effective solution to emission and congestion problems in cities. TfL’s cycling analysis has predicted that 63% of car journeys can switch to bikes...so why is it that only about 3% of the UK cycle?
It’s actually not that cycling is unsafe, statistics have shown that cycling in cities such as London is nearly as safe as walking. The problem is that cycling doesn’t look or feel safe.
Whilst collisions are rare, jam-packed traffic grids have never been the friendliest place for cyclists. A Near Miss Study conducted in 2015 by Rachel Aldred indicates that on average, a UK cycle commuter experiences 450 near misses per year.
The hostile road environment in cities and a combination of near miss encounters prevent people from getting on bikes or choosing cycling as their first choice of transport.
A mature cycle infrastructure (such as the Dutch cycle infrastructure) is an obvious way to increase uptake, but these plans are often slow with targets misaligned. The use of current existing cycling road data on the market give an overview of the problem areas but require leap-of-faith inference and assumption on specific incidents.
Focused on the elimination of dangerous near misses and close passes, Pelation’s REBO uses IoT sensors, self-learning technology, and video analysis on bikes to improve the quality, robustness, and usability of spatial road incident data captured.
REBO is a cyclist near miss prevention dashcam that not only actively prevents near misses on road journeys through behavioural change but also allows cyclists to capture dangerous incident details (video footage, plate numbers, time, date, location) with just a click of the handlebar button.
Authorities have found that whilst they are able to reach a better understanding of general problems areas using existing cycling data, it is difficult to gain confidence in issue root causes without a more comprehensive understanding of full incident context. The use of REBO increases the availability and understanding of road data, generating not only a visualisation of these data points but also robust video data of specific incidents to allow authorities and stakeholders to prioritise challenge areas
It was very important to us that, on top of the technology and data, the fundamental design of REBO promotes safety and is preventative of incidents in nature.
We’ve injected the “watching eye effect”, a psychological effect to subconsciously prompt positive and more considerate behaviour, into the unique eye design of our product to nudge safer behaviour of those moving around cyclists and to humanise cyclists as road users. The application of this design is commonly used in the security and surveillance industry; we have also seen this used in the transport industry.
Meet Pedal and Post.
Pedal and Post is an eco-friendly zero emission cycle courier company in Oxford and our partners in this trial. They use Pelation REBOs to ensure the safety of their team members and accountability of those moving around their cycle couriers.
“keeping our team safe is the priority and we've experienced a large spike in incidents in Oxford which is very concerning” - Chris Benton, Director
Oxford was selected as a test bed for this trial due to its size, abundance in cyclists, and general sentiments regarding existing cycle infrastructure. Pedal and Post uses push fleets and e-bikes to deliver across a large variety of routes (throughout both Oxford and Oxfordshire) on a daily basis - rain or shine. Throughout the initial lockdown caused by COVID-19, cycle couriers were also classified as essential workers and they continued to support local communities and deliver medical supplies with new measures in place. We ran REBO prototype units with Pedal and Post for two months - capturing all dangerous, illegal, and unsafe incidents their riders had.
Over the length of the pilot, we were able to generate a data set of 104 data points. With 77 more notable incidents sorted into eight key categories:
The incidents captured from the pilot were manually sorted into types of incident categories and exported into an open source geospatial analysis mapping tool.
Each data point displays a preview of the unique incident number, unit the footage was extracted from, date and time of incident, incident type, notes, video preview, and most importantly, link to a one minute video of the actual incident.
Map users are able to see Oxford’s key incidents with the added feature of being able to click through to the video link to view the real time footage.
Users can obtain additional context on each incident from the video and determine the actual relevance of this datapoint based on their purpose.
Initial Findings
Through this we were able to extract some obvious initial findings including:
These findings can be further adapted towards specific objectives and use cases.
The next stage of the technology roadmap is to develop capabilities to automatically identify and analyse cycling near misses using our device’s video footage and data.
Current cycling road data on the market give an overview of problem areas but require leap-of-faith inference and assumption on specific incidents. It is difficult to gain confidence in incident root causes without the incident context - our video footage captures this missing information which enables the determination of how near misses develop.
This aims to produce, for these near misses, actionable insights to determine their root causes and potential fixes. This will allow local authorities to more easily understand, prioritise, and implement action plans faster and with more impact and value for money.
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