In 2018, 55% of car journeys in the UK were under 8 miles. In order to reduce emissions, it is vital that we see a drastic increase in the number of people cycling and walking for everyday purposes. However, without adequate cycling and walking infrastructure that provides a real alternative to cars, this reliance on vehicles for short journeys will remain.
Effective cycle infrastructure planning is vital to increasing active travel uptake and ensuring that we achieve the modal shift towards cycling that is necessary if we are to achieve our climate targets. Without quality cycling networks that are coherent, safe and attractive, we simply won’t see a large enough increase in cycling.
In order to plan and build effective cycling infrastructure, one thing is clear; high-quality, insightful data is needed. While methodology and approaches to cycle network planning vary across countries, the need for data remains consistent. In England, Local Cycling and Walking Infrastructure Plans (LCWIPs) state that all infrastructure planning must be ‘evidence-led’. Throughout the LCWIP process decisions must be based on data, and data must be utilised to justify the proposed investments. Something that Active Travel England will be monitoring in its new remit
See.Sense data insights can aid cycle infrastructure planning by supplementing existing data sets, such as the Propensity to Cycle tool. Our lights contain sensor technology that monitors the environment over 800 times a second, allowing See.Sense to provide highly granular anonymised insights into the rider’s experience, including breaking, swerving, collisions and road surface conditions, while our dashboard can also show route popularity, average speeds and dwell times. As a result, See.Sense can provide valuable, targeted insights that act as key evidence for cycle infrastructure planning.
See.Sense can provide data on current cycling transport networks, as our data provides insights into route popularity and movement patterns. This means we are able to identify information such as transport patterns, travel patterns, and the location of significant trip generators. Crucially, unlike most existing data sets, the data insights we provide here are relevant and up to date, as this data is collected continually through our lights.
See.Sense data also enables the origins and destinations of cycle journeys to be identified, and for this to be developed into a Cycle Network Map which can subsequently be used to pinpoint the location of infrastructure improvements. Our data can provide a visualisation of route popularity and movement patterns, allowing for easy identification of these popular origin and destination points.
Additionally, our data insights can ensure a proposed cycle route conforms to the five key design principles identified by the Dutch CROW Bike Design Manual. That is, that a route is coherent, direct, safe, comfortable, and attractive. By examining route popularity, we can view how coherent and direct a cycle route is. We can determine if cyclists are diverging from a particular route, and if a proposed route adequately connects popular origin and destination points. Through dwell time data, it is also possible to view junctions that hold up cyclists for too long - another determining factor into how coherent a route is.
Where See.Sense Data Adds Insight
See.Sense lights measure incidents of swerving and heavy braking, allowing us to also determine the safety of a proposed cycle route. In previous studies, our heavy breaking data has been consistently shown to correlate with locations of officially recorded collisions. By identifying hotspots of swerving and heavy braking, our data is able to earmark specific unsafe locations. Additionally, we can assess and map road surface quality. This not only aids a route's safety, but also a route’s comfort, as rough areas that may deter cyclists can be flagged for maintenance.
Case Study: North East Lincolnshire Council LCWIP
The potential of See.Sense data to aid cycle infrastructure planning has been highlighted by City Science and North East Lincolnshire Council, who have successfully employed See.Sense road roughness and swerving data as part of their LCWIP process. City Science and NE Lincolnshire Council used See.Sense’s ‘innovative’ route vibration data to supplement existing conventional datasets, such as the Propensity to Cycle tool. As seen in the images below, See.Sense data was used to inform the NE Lincolnshire Council of areas that most urgently needed infrastructure improvements. By using targeted road roughness and swerve data, NE Lincolnshire Council could easily identify the routes that were most problematic to local cyclists, and take account of this in their infrastructure planning.
The data used was collected in November 2019 as part of a See.Sense smart cycling project involving around 80 local cyclists. Through the project, participants used the patented See.Sense connected bike light and accompanying app to collect crowdsourced sensor data and insights. These aggregated and de-personalised insights were then shared with city planners to gain a better understanding of the conditions faced by cyclists.
Anthony Snell, Senior Transport Officer at Places & Communities, North East Lincolnshire Council, stated:
"The data we got from See.Sense has been really useful. The road roughness and swerving data helped to put some real life context to the development of our LCWIP which wasn’t available from other sources.”
To find out how See.Sense technology and data services can help inform your transport planning and usage monitoring please email@example.com and a member of our team will be in touch.
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