Logging Data From E-Scooters To Improve Traffic Safety
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The transport sector has seen a major overhaul in recent years with the emergence of
several new forms of transport. The increasing environmental concerns, fuel prices and
traffic congestion have had people recon substitute means of transportation such as
electric cars, e-bikes, e-mopeds etc. E-scooters due to the rapid growth of the scooter-sharing system have emerged as an attractive solution. These motorized scooters also
help mitigate the first/last mile issue associated with the use of public transport.
However, the introduction of any new mode of transport leads to new conflicts which
thereby can result in new types of crashes. While the regulations in several parts of the
world treat the e-scooters similar to bicycles, numerous studies have proven several
dissimilarities in the overall dynamics between them. To understand the causes of
conflicts, data about the usage of e-scooters is essential. Naturalistic data is data
collected using instrumented vehicles in road traffic, by users performing their day-to-day activities. These datasets are less likely to suffer from bias as compared to data
collected in a regulated environment such as a test track. Naturalistic data are a widely
recognized source to analyze and model the behavior of road users, to improve traffic
safety. Naturalistic data collected on e-scooters will provide a unique understanding of
the details of e-scooterist riding behavior, interactions with the surrounding road users,
and reactions in different situations. This thesis aims at developing a ride data logger
which will enable naturalistic data collection when extended to a large fleet of e-scooters. In addition, preliminary data collection and analysis are conducted to test the
effectiveness of the logger and identify the potential issues.
Data corresponding to 15 variables is logged at a frequency of 10 Hz from a plethora
of sensors on the e-scooter, providing detailed information about the kinematics of the
ride. In addition, video data is captured which provides a visual information of the
riding environment and the rider. When the logger is connected to the internet the data
stored in the local memory storage of the data logger is automatically transferred to the
cloud storage. This not only automates the process but also minimizes human
dependencies and interventions. To facilitate the analysis of the datasets collected as a
part of the process, a Python-based graphical user interface, which allows visualization
of each datapoint, has been developed. This help in analyzing the kinematics and video
data simultaneously.
A pilot data collection involving participants has been carried out based on which a
preliminary data analysis has been conducted to indicate the potential of the new data
logger to serve for a large-scale naturalistic riding study. Exposing the prototype to
different road surfaces, lighting conditions, riding styles, and ride durations over the
span of 350 km has facilitated the identification of points of improvement for a furture
large-fleet data collection.
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Naturalistic Riding Study, Naturalistic Data, E-scooters, Data Logging, E-scooter safety, Vehicle and Traffic Safety, Naturalistic Data Collection