Pilot Naturalistic Riding Study (NRS) with VOI e-scooters to improve traffic safety
Projektarbete, avancerad nivå
Pai, Rahul Rajendra
Increase in traffic and emissions resulting from the rise in vehicle population and the first/last mile issue associated with the use of public transport has led to a rise in the micro-mobility market. The e-scooters take a major share of this market and can be attributed to the e-scooter rental service. Introduction of e-scooters into the traffic environment has resulted in new traffic conflicts hence, possible hazards that can lead to new types of near-crashes or crashes. To better understand how e-scooters interact with other road users and identify underlying risk factors that may lead to a crash, naturalistic data collection is a suitable tool for proactive traffic safety work. In fact, the naturalistic data offer the unique opportunity to understand the cause of crashes and the genuine road user behaviour in critical situations. In the past, naturalistic data has been collected mainly from motorized vehicles. In this pilot project an e-scooter has been equipped with a data logger that is connected to cameras and numerous on-board sensors. The sensors provide kinematic information of the e-scooter while the cameras provide a visual representation of the ride environment. Each of the hardware components is placed in a casing developed with additive manufacturing technologies. The data collected is stored locally and then can be transferred to a computer for data analysis. The data collection has been carried out in two stages with the initial one being to identify any vulnerability of the system. The second set of data collection include participant-based riding data. Graphical User Interface (GUI) has been developed to enable easier analysis and visualisation of the data. Several other tools required for data processing along with the GUI have been developed using MATLAB. These tools will enable frame by frame analysis of the ride and aid in the understanding of the cause of every critical event recorded. This thereby enables detection of the causation mechanism behind the safety-critical scenarios. The project has proven that it is fully possible to equip an e-scooter with instrumentation for naturalistic data collection. A GUI has been proven to be a necessary asset when it comes to evaluating the logged data. The results from this pilot study may be the basis for a large naturalistic data collection, that may cast light on safety of e-scooters.
Naturalistic Data , E-scooters , Vehicle Safety , Data Logger , E-scooter Safety