Cycling as a mode of transportation has been recording an upward trend in both the U.S. and Europe. Unfortunately, the safety of cyclists has been a point of growing concern. Data from the National Highway Traffic Safety Administration (NHTSA) show that the crashes that occur during the events of motorists overtaking cyclists was one of the leading categories involving cyclists in fatal crashes. In support of the efforts to understand the driving behavior of drivers of motorized vehicles while overtaking cyclists, this research project is aimed at developing an algorithm to identify the overtaking events. Most existing quantitative studies on cycling safety leverage instrumented bicycles or vehicles with sensors for extracting naturalistic driving trajectories. Whereas we use data from a recent research that provides naturalistic driving trajectories of road users collected at select intersections in urban areas in Germany using drones equipped with cameras. Using these videos with a data frequency of 25 Hz, the authors of this study have output inD dataset. The inD dataset contains trajectories of road users that are captured in form of coordinates on a two-dimensional plane obtained from the ariel or bird's eye view of the road. Additionally, the data also captures velocity, acceleration, heading angles, dimensions of driver's vehicle etc. Overtaking can be thought of as four phases of approaching, steering away, passing, and returning. Using the inD dataset, we have developed an algorithm to identify events when a driver of motor vehicle overtakes a cyclist. This work fits into our broader goal to contribute to the body of knowledge for improving road safety of cyclists. The work is expected to provide inputs to governmental/ traffic authorities in aspects such as design of intersections and design of bicycle lanes by providing insights into overtaking events. [from Indroduction]

Mobility has changed in recent years in cities worldwide, th.anks to tb.e strong rise in vehicles of micromobility. Bicycle riding is the most widespread micromobility transport mode, followed by stand-up electric scooters (e-scooters). This increase in its use has also led to an increase in related crashes. Both cyclists and e-scooter riders are vulnerable road users and are lik.ely to sustain severe injuries in crashes, especially with motor vehicles. The crashes consequences involving cyclists and other micromobility users have already investigated using numerical simulation software, such as MADYMO and PC-Crash. Most of them have been focused on bicycles and electric bicycles, whereas only few of tbem have analyzed e-scooter crashes consequences. Posirisuk: et al. [1] carried out a computational prediction ofhead-ground impact k:inematics :in e-scooter falls. Ptak et al. [2] analyzed the e-scooter user kinematics after a crash against SUV when the e-scooter chives into the sidefront of tbe vehicle, a side B-pillar crash and a frontal impact initiated by tbe e-scooter to tbe front-end of the vehicle. However, they did not study the consequ.ences of a car to e-scooter side impact crashes. Xu et al. [3] did study these crashes but considering electric self-balancing scooters that are less widespread than e-scooters. Current study focuses on the consequences of a car to micromobility user (cyclist and e-scooter rider) side impact crashes. The analysis is based on numerical simulations with PC-Crash software.

IAB one solution of micromobility, e-scooters have become a trend in Germany. However, the concems about the safety of e-scooter riders, influence on pedestrians and the parking issues are growing. In 2020, 2,155 e-scooters involved personal injury accidents were recorded in Germany. The number rose to 5,502 in 2021 meaning an increase of 155.31 %. Compared to cyclists (incl. pedelec cyclists), the increasing rate of personal injury accidents in the same period decreased by 8.75 % [1, 2]. Against the background of accidents with e-scooters in cities, prior studies analysed severity and patterns of injuries caused by such accidents [3, 4]. In addition, comparisons are drawn to the consequences of accidents with other vehicles [5, 6]. Some studies also consider the risk of injuries in relation to the miles travelled [7]. The studies provide valuable findings but the approaches focus on the severe consequences of occurred accidents. At the same time, compared to bicycles, the centre of gravity of e-scooters is lower, they are more manoeuvrable and can still reach speeds of up to 20 km/h [8]. The question remains, if these vehicle characteristics are associated with different interaction behaviour. Hence, the aim of the present study is to reveal the riding behaviour profile in different contexts and investigate e-scooter riders' criticality in interaction behaviour compared with cyclists using surrogate safety measures. We aim to figure out if the interaction behaviour of the two modes differ and what the effects of potential differences are for safety considerations in the system of active mobility.

International crash statistics indicate a decrease of bicycle crashes, but at a slower pace compared to total crash numbers. The share of crashes with involved cyclists is above the modal share (see [1] for an overview). Depending on sources, types of analyses, and geographic regions, crash statistics suggest high rates of singlebike crashes and crashes between cyclists and other vulnerable road users (VRUs) [2], while cars are opponents in more than half of all fatal crashes in the European Union [3]. The design of th.e road environment is of particular relevance for crash risks. A study from London found three times higher injury odds for cyclists at intersections [4]. Connected and automated vehicles (CAV) are frequently said to increase the safety level in road traffic since they are less prone to human errors [5]. This might hold true in transport systems with little complexity, such as highways [6]. However, when it comes to complex situations in multimodal systems with multiple interactions between different road users, such as intersections in urban environments, existing solutions are not sufficient yet in terms of protecting VRUs. ... In order to contribute to the safety of VRUs in the interplay with CAVs in current systems, we propose a geospatial model, which delineates potential interaction risk zones from high definition (HD) maps and enriching these zones with additional information. These enriched risk zones are then provided as standardized OGC web service, which can be integrated in V2X systems. With this, we contribute to the visibility, and thus the safety of VRUs in connected transport systems. From a methodological point of view, the proposed model is a first step in integrating spatial context and semantic information explicitly into V2X communication. [From: Introduction]

E-scooters are a rather new mode of transport. Nevertheless, in recent years lots of studies have been published. Replaced modes and consequential environmental impacts as well as specific injury pattern are important topics. Regarding shape, speed and usage, e-scooters are most similar to bikes. As a consequence, by law e-scooters use the same road space or infrastructure than bikes do. Concurrently, in recent years we experience a boom of cycling in cities and a significant expansion of the bike infrastructure. Requirements and frequency of usage on the bike infrastructure are growing in cities caused by increasingly diverse cyclists. At the same time, the bike infrastructure is subject new requirements and additional pressure due to the implementation of e-scooters. In Germany, allowing e-scooters on bike infrastructure can be seen as a paradigm shift since for the first time a motorized vehicle is allowed to use the infrastructure. On this background, interrelation between e-scooters and active mobility (walking and cycling) are very important for the future use of the infrastructure and the ongoing transformation of urban mobility. Hence, we use a multi-method approach to investigate these potential conflicts and draw conclusions for regulation as well as improvement in the system.

Cycling as an active mode of transport is increasing across all Europe [1]. Multiple benefits are coming from cycling both for the single user and the society as a whole. With increasing cycling, we expect more conflicts to happen between cyclists and vehicles, as it is also shown by the increasing cyclists' share of fatalities, contrary to the passenger cars' share [2]. Understanding cyclists' behavioral patterns can help automated vehicles (AVs) to predict cyclist's behavior, and then behave safely and comfortably when they encounter them. As a result, developing reliable predictive models of cyclist behavior will help AVs to interact safely with cyclists.