Hasselt University Road Surface Case Study

One of the most important factors in promoting the uptake of cycling, and making it a pleasant experience is rider comfort. There are many things that can influence rider comfort, from air and noise pollution, to mental stress from road sharing with motor vehicles, but one of the most crucial indicators of comfort while riding is the level of road surface roughness. Smoother roads are simply more comfortable to ride on.

Current methods for evaluating road surface roughness can be time consuming, expensive, infrequent, and resource intensive. However, See.Sense sensor-enabled technologies, with their ability to measure road surface roughness, along with multiple other metrics of the cycling experience, offers a viable alternative to standard evaluation techniques. 

A team of researchers at the Transportation Research Institute (IMOB) at Hasselt University, Belgium, recently conducted a case study using See.Sense ACE bike lights, testing their viability in assessing the road surface roughness (vibration) levels of cycling infrastructure, and, additionally, quantifying the level of cyclist comfort associated with the measured vibration levels. 

The case study was carried out in the city of Hasselt, Belgium, with the study focussed on assessing 28 different cycle paths and streets, as indicated in the image below:

20 volunteer cyclists were recruited to ride along these road sections, on a bicycle equipped with See.Sense ACE front and rear lights, connected to a smartphone via the See.Sense mobile app. The collected data was analysed by the team at Hasselt University, with the road surface roughness measured in terms of vibration level. The map below shows this vibration data grouped into 3 categories, with green dots representing low vibration readings (values < 10), yellow dots representing less smooth readings (values from 10 to 23), and red dots representing strong vibration readings (values > 23).

Areas with successive red dots were identified as problematic areas with rough road surface. 

The image below shows one such section with successive red dots which on closer inspection is seen to be a cobblestoned street.

Conversely, the image below shows a section of successive green dots, indicating a smooth surface, measured on a section of asphalt pavement.

The team at Hasselt categorised the different cycling infrastructure by road surface type, namely asphalt, cobblestones, concrete paving slabs, small paving elements/slabs, and unpaved. The vibration levels associated with each surface type were evaluated, with the average vibration levels displayed in the graph below:

The asphalt and concrete surfaces were seen to have the lowest vibration levels with average readings less than 5, while cobblestone surfaces registered the highest average vibration levels of 15.

Relating Road Surface Roughness to Cyclist Comfort

As user perception of cycling comfort may vary from person to person, the case study had the 20 volunteer cyclists ride along different road sections and record their perceptions of comfort. They were asked to rate their comfort level on a scale from 1 (very uncomfortable) to 5 (very comfortable). These comfort levels were then plotted against the measured vibration levels, producing the following graph:

As can be seen from this graph, when vibration levels increase, perceived discomfort levels also increase. Vibration values of 10 and under are located within the comfortable band, with values under 4 being reported as “extremely comfortable.” The vibration value of 15 appears to be the boundary point, above which discomfort is perceived, with values over 26 reported as “extremely uncomfortable.”

The Figure below shows two maps side-by-side. The map on the left shows See.Sense road surface roughness data, grouped into 3 categories of vibration level (as described previously) with red boxes highlighting areas of concern which have successive red dots. The map on the right shows the same streets colour-coded according to perceived rider comfort levels. It can be seen that there is a strong correlation between the rough sections measured by See.Sense and the sections of road perceived as uncomfortable by the volunteers.

In addition to the road surface roughness data measured by See.Sense hardware, volunteers also used the See.Sense mobile app to highlight other factors that impacted rider comfort. Upon completion of a trip, volunteers were able to use the app to mark the GPS location of an issue and leave a comment. The figure below shows a map of the issues raised by the volunteers, which include visual blockers, obstructed cycle paths and slippery surfaces.

In conclusion, the case study proved the ability of See.Sense technology to effectively measure road surface roughness levels and correlate these measurements with rider comfort. Talking about the potential of See.Sense data, the research team states that 

“The ability of See.Sense to monitor and revisit specific lengths of infrastructure at a considerably higher frequency may provide a far fuller picture of surface deterioration, allowing for a preventative maintenance strategy based on accurate and up-to-date data.”

They also stated that this data “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.”

The full research article can be read here.

Authors:

Tufail Ahmed

Ali Pirdavani, Ph.D.

Davy Janssens, Ph.D.