At the moment, the majority of road repairs are based on the needs of car users. Knowledge of how cyclists use the road is often based on anecdotal evidence, yet cyclists are amongst the most vulnerable road users affected bypoor infrastructure conditions. In the UK, there’s an average of one road defect for every 110 metres of road, and defects such as potholes pose potentially fatal risks to cyclists. The risks associated with potholes are costly for local authorities too. A CyclingUK study has found that while the average compensation payout per motorist made by local authorities in the UK between 2013 and 2018 was around £340, authorities paid on average £8,800 per cyclist for incidents involving potholes in the same period. Add onto this the cost of simply maintaining and repairing damaged roads, and the imperative for road surface monitoring solutions is clear.
With a large increase in cycling in the UK following the pandemic, cyclists more than ever need to be taken into account when evaluating our roads and monitoring road surface conditions. Indeed, cycling is increasingly the focus of transport planners and local authorities, with KPIs in place to monitor the number of people cycling, the number of accidents, and to assess the overall safety and effectiveness of the cycling network. With this shift to cycling, cities need to find new ways to maintain the road network, using data to inform agile governance and policy making.
There have been many approaches to obtaining road surface condition data. However, almost all of them are limited in their application in relation to the needs of cyclists or other forms of micromobility such as e-scooters. Visual inspection techniques are often slow and require intensive human intervention after damage to the surface has occurred. Techniques that require advanced measurement equipment (sophisticated profilers) come with high costs and require skilled operators. As a result, most roads are reviewed infrequently (less than annually), compounding the issue and leading to expensive, reactive repairs.
With See.Sense, road surface quality can be accurately and rapidly mapped across entire city regions, enabling local authorities to identify problem areas and prioritise infrastructure investment accordingly.
How See.Sense gather road surface condition insights
The dedicated sensors and patented technology within our lights enables See.Sense to generate large scale, never-before-seen data across a city in near real-time. By measuring the cyclist’s environment over 800 times per second, the sensors are able to detect small variations in the condition of the road surface. This data is then aggregated and visualised based on a grading system identifying road surface quality issues in the city.
Use Case - Cycleway Monitoring and Maintenance
To deliver high quality cycle networks in cities it is critical that highway maintenance regimes pay particular regard to cyclists’ needs. It is recommended that inspection frequencies and response levels should be higher on the most important parts of the cycle network, even where these routes are minor as far as motor vehicles are concerned, e.g. quiet lanes or back streets.
Regular inspection and condition surveys are also a requirement under the ‘Well-managed Highways Infrastructure’ Code of Practice’ (2016), which requires a risk-based management approach to maintenance, taking into account usage of routes by cyclists, collisions and records of complaint. Yet, highway surface condition is usually geared to supporting motor vehicle performance thresholds, rather than cycle performance thresholds (Parkin, 2019). Many cities struggle to maintain inspection frequencies at a rate to support the requirements of the code and require an efficient and cost-effective way to gather and record data. See.Sense technology offers an approach to measuring the condition of road and cycle path surfaces that is objective, and at the same time, correlates with human perceptions of comfort reported via the app. These insights will enable data driven prioritisation of highway maintenance, allowing the maximum return on investment and cost savings.
Data was collected in Dublin as part of the See.Sense Smart Cycling project, facilitated through the EU SynchroniCity initiative. This project saw 200 participants purchase a discounted See.Sense ACE light and opt in to share their ride insights over the course of three months from July to August 2019. The project results highlighted road surfaces that were particularly challenging for cyclists, such as Clanwilliam Terrace, and the information gathered from the project was used to plan the development of Dublin’s most recent cycle infrastructure improvements.
The road surface insights collected through this project were subsequently utilised by engineering firm AECOM to conduct a Quality of Service assessment for Dublin’s cycling network. AECOM identified that the ride insights collected through the See.Sense project with Smart Dublin could provide information on the more challenging, and time consuming, aspects of assessing the QoS for an urban area, and identified Pavement Condition as a characteristic that could be highlighted through See.Sense data.
The See.Sense data highlighted sections of road of varying quality for AECOM to explore. An AECOM engineer conducted a visual inspection and compared the See.Sense data with the rating (A-D) Aecom would ordinarily apply. This showed there was a strong correlation between the See.Sense road surface data and the visual inspection carried out by Aecom. The results of the collaboration were jointly presented at the European Transport Conference 2018.
AECOM ultimately found that “the data provided by the lights is a useful tool for planners to develop cycle networks and also identify maintenance priorities, and that the data can be used to get a better understanding of a cities cycle network in terms of: Pavement Condition; Junction Delay; and Desire Lines.”
This joint project demonstrated the value of the See.Sense data as a useful resource for city planners and transport officials in their quest to develop high quality cycle networks alongside identifying road surface issues and effectively prioritising maintenance.
See.Sense in Action - Hasselt
The value of See.Sense lights in monitoring road surface has also been proven in a study by the Transportation Research Institute (IMOB) at Hasselt University in Belgium. The university conducted a case study to assess the See.Sense ACE bike lights’ ability to monitor road surface roughness (vibration) levels of cycling infrastructure, while also quantifying the level of cyclist comfort associated with the measured vibration levels. The study focused on 28 cycle paths and streets around the city with the help of 20 volunteer cyclists who connected their light to the See.Sense app. The collected data was analysed by the team at Hasselt University.
The results from the study were clear - See.Sense lights were able to detect problematic areas of rough road surface, areas of smoother cycling and areas that might be on their way towards needing improving. Analysts were able to correlate vibration levels recorded by the light with road textures and cyclist comfort levels.
Officials from the university stated that the data recorded “can provide transport planners and road authorities with easy-to-follow, science-based guidelines for monitoring pavement quality and improving cycling experiences on urban roads. In turn, this will encourage local economic development and sustainable urban transportation.”
For more information on this case study, please take a look at our in depth Hasselt University Case Study, which also includes a link to the University’s report on the study.
See.Sense in Action - London
Our road surface insights have also been utilised as part of our project with e-scooter fleet operator Dott in London, which you can read more on our E-Scooter Trial Results blog.
To find out how See.Sense technology and data services can help with road service quality assessments please email firstname.lastname@example.org and a member of our team will be in touch.
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