When measuring how drivers overtake cyclists, one of the underlying problems is extracting the overtaking event from a time series of lateral distance readings. This note aims to describe a simple approach that seems effective in applications like ours. It consists of carefully transforming our problem into a network problem, then leveraging a community detection algorithm to extract subsequence candidates. Lastly, we choose the anomalous subsequence from the set of returned subsequences. To the best of our knowledge, this approach to anomaly detection does not appear in the literature even though it is intuitive, offers a fair amount of control, and is not computationally expensive. Our goal is to present the crux of the method with clarity and identify where more effort could improve it. We demonstrate our approach with modularity-based community detection and point out a shared nature of our approach with density-based cluster detection methods. 

Rural roads are bound to be shared between all users: drivers, cyclists, pedestrians, horse riders, etc. Overtaking (passing) of unprotected road users by drivers is a canonical interaction between them and it accounts for a large proportion of harassment and danger situations. Experiences from these situations are known to determine whether people cycle on rural roads or not. Lateral distance, vehicle speeds, manner of approach, and frequency of overtakes are key measurable parameters to consider.

Even though cycling is slowly starting to get recognised as the sustainable mode of transport it is, this is mostly limited to short trips in urban areas. However, also in rural environments the bicycle has potential to replace the car for many trips. Best practice and research findings from urban areas cannot necessarily be directly applied to the rural environment, as preconditions can differ substantially. Speeds are higher, most interactions are longitudinal, the road is often shared, and the trip purposes and road users are likely to have different characteristics. This is also reflected in the differences in crash typology for rural and urban environments. This project aimed at further describing conflicts on rural roads between motorists and active travellers in a Swedish context by analysing crash statistics. The development of a logger that provides measurements of overtaking and oncoming passes in detail and can in the future then be used to quantify the context for situations that do not result in a (documented) collision. This way, collisions can be put into perspective for example in relation to the context (road type, speed limit, average annual daily traffic, etc.). Objective and reliable measurements of longitudinal interactions between motorists and active road users – namely overtaking and oncoming passes – are one important ingredient to assessing the current situation and the effectiveness of potential countermeasures. The measurement devices used in research so far are usually custom-built and the data reduction process is often not fully transparent. Within this project, we built an expandable device for logging the lateral distance to overtaking and oncoming vehicles and the approach speed of overtaking vehicles. The process to extract the passing occasions and the correct overtaking distance is made available as open source.