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Last Updated: 2-2017


There has been a growing interest in making cycling a competitive mode of transport, as it is a healthy, environmental friendly alternative and cities are coping with severe problems resulting from the high levels of motorized traffic. In the process of bicycle facility planning, tackling issues regarding cyclist’ comfort and safety levels is of utmost importance in order to stimulate and promote cycling as a transportation mode. Especially poor pavement surface conditions appears to strongly affect these levels. The pavement surface conditions are currently assessed through visual inspections, which is an subjective and outdated approach that does not tackle maintenance issues in a resource efficient manner. Since the condition of bicycle surfaces can be manifested in terms of bicycle vibrations, a Bicycle Infrastructure Monitoring System (BIMS) is proposed, which intends to objectively assess the bicycle pavement surface conditions by measuring these bicycle vibrations with the sophisticated sensors embedded in modern day smartphones. By collecting this vibration data in
combination with location data from cyclists’ smartphones, BIMS will be able to continuously monitor the asset condition data regarding bicycle pavement surfaces. The valuable information resulting from BIMS will help road managers to make more efficient use of their limited resources in relation to operation and maintenance, renewal and auditing of the bicycle environment. This study presents a strong foundation for BIMS by describing all the processes involved and what they entail. This study has further focused particularly on the feasibility of BIMS assessment component, by first conducting a case study which examined if the current sensor quality is sufficient in order to provide reliable data for BIMS. Based on five
tests, this case study has shown that smartphones’ motion sensors are capable measuring accelerations with a high level of precision and that the location sensors provide GPS data with an acceptable level of accuracy. Next, a field experiment was undertaken in order to prove the feasibility of assessing the pavement surface conditions based on smartphone data, and to
examine if an already existing method of dynamic comfort mapping can be used for converting the vibration measurement’s outcome into a dynamic comfort index (DCI). The field experiment has demonstrated that it is possible to distinguish different pavement surfaces, and of different qualities based on vibrations data collected from smartphones. Regarding the method of dynamic comfort mapping, more detailed research is necessary in order for BIMS
to adopt this method. This study may be seen as a first step towards more objective and sophisticated methods to collect asset condition data regarding the bicycle environment.

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