The road transport in Kenya is the most common means oftransport for people living in both urban and rural areas. The use of bicycles for transport dates back in the pre-colonial time and has been used as a mode of transport until 2008 when the use of motorcycles became a popular mode of travel in the rural and urban areas. However, the use of bicycle as a means of travel has declined consistently over the years until now and many have shifted to the use of car, public transport and most commonly motorcycles which are popularly known as 'boda boda' in Kenya. This modal shift can be attributed to a number of factors identified as challenges in the use of bicycles as a common mode of transport in comparison to other emerging modes of transport both in rural and urban areas. However, despite this modal shift, there are a substantial number of road users who would still prefer to use the bicycle mode amid prevalence in road traffic fatalities and injuries in Kenya. The government of Kenya has established initiatives to provide safe and inclusive transport system by investing in transport infrastructure that includes cycle tracks especially for roads located in the urban cities. This has been enabled by innovation in design, mixed traffic composition, change of legislation and road design standards especially in regards to non-motorized transport in Kenya. Cycling is still low in cities in Kenya despite this effort to improve geometric design of roads. This paper explores these challenges and opportunities in cycling in Kenya focusing on Nairobi city as a case study. [From: Introduction]

Since bicycles travel at high speeds and are frequently involved in traffic accidents, reducing bicycle fatalities and injuries is one of the most important issues in traffic safety. In car-to-cyclist collisions, the perpendicular configuration occupies the largest proportion of these collisions. Driver responses in lateral intrusions of cyclists at intersections have been examined [1,2], focusing on the drivers' braking reaction time and the time-to-collisions (TTC). Cyclist behavior can also have a significant intluence on car-to-cyclist collision occurrence. Cyclist bebavior has been investigated in naturalistic conditions and using in-depth accident data. In addition, the videos of drive recorders provide useful information on the cyclist behaviors [3]. This study investigated cyclist behavior with the drive recorder of cars in near-miss incidents and collisions. [From: Introduction]

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.

Cycling has always been considered a sustainable and healthy mode of transport. Moreover, during Covid-19 period, cycling was further appreciated. by citizens as an individual opportunity of mobility. As a counterpart of the growth in the num.ber ofbicyclists and of riding k:ilometres, bicyclist safety has become a challenge as the unique road transport mode with an increasing trend of crash fatalities in EU (Figure 1). When compared to the traditional road safety network screening. availability of suitable data for crashes involving bicyclists is more difficult because of underreporting and traffic flow issues. In such framework, new technologies and digital transformation in smart cities and communities is offering new opportunities of data availability which requires also different approaches for collection and analysis. An experimental test was carried out to collect data ftom different users with an instrumented bicycle equipped with Global Navigation Satellite Systems (GNSS) and cameras. A panel of experts was asked to review the collected data to identify and score the severity of the safety critical events (CSE) reaching a good consensus. Anyway, manual observation and classi.fication of CSE is a time consu.ming and unpractical approach when large amount of data must be analysed. Moreover, due to the complex correlation between precrash driving behaviour and due to high dimensionality of the data, traditional statistical methods might not be appropriate in t.bis context. Deep learning-based model have recently gained significant attention in the lit.erature for time series data analysis and for anomaly detection, but generally applied to vehicles' mobility and not to micro-mobility. We present and discuss data requirements and treatment to get suitable infonnation from the GNSS devices, the development of an experimental :framework: where convolutional neural networks (CNN) is applied to integrate multiple GPS data streams of bicycle kinematics to detect the occurrence of a CSE.

Experience and key data suggest that snow and ice lead to increased numbers of cyclist falls during the winter months. Reliable in-depth data concering the extent and characteristics of this issue are currently not available in most countries. In Germany, this is due to the high level of under-reporting in official statistics, particularly for incidents involving only one bicyclist. In combination with the lack of knowledge on exposure this causes difficulties to quantify risks for cyclist falls. This study addresses these gaps. lt aims at quantifying the risk of single bicycle accidents in inclement weather conditions. This study focusses on icy and snowy conditions as these are of relevance for the risk to fall. Cyclists are particularly affected by slippery icy and snowy road conditions; these might exist in clear, cloudy, or foggy weather, in situations with high or low humidity and with higher or lower wind speed. Variables from official weather data are purposefully combined in this study to identify time periods with snow or ice on the roads and to allow for the comparison of those with all other time periods ('other weather''). We address the above-mentioned problems of exposure and underreporting by using multiple data sources for quantifying the risk of falls. This approach allows to compute clear risk ratios for icy/snowy and the other weather conditions and thus contributes to the scarce and fragmented literature that has generated such values so far. [from Background, AIM]

Movements of cyclists and m.otor vehicles at intersections involve a wide variety of potential conflicting interactions. In Australia, the high numbers of motor vehicles, particularly light passenger vehicles, mixed with cyclists results in many bicycle-light passengervehicle (LPV) crashes (3,135 crashes during 2002-2014). About 68% of cyclist deaths at Australian intersections in 2016 were due to crashes between bicycles and LPVs (DITRLDG, 2016). The high number ofLPV crashes among fatalities among cyclists is an increasing safety concem. When an LPV collides with a cyclist, the resulting impact forces in.tluence the probability of cyclist injury severity outcom.e. Therefore, the goa1 at intersections should be to understand whether and which particular crash patterns are more injurious, in order to better inform approaches to reduce the impact forces to levels that do not result in severe injury outcomes. To examine how crash pattem (or mechanism) influences the injury severity of cyclists in bicycle-motor vehicle crashes at intersections, researchers typically describe the crash mechanism in terms of crash types, such as angle crashes, head--on crashes, rear-end crashes, and sideswipe crashes (e.g., Kim et al., 2007; Pai, 2011 ). While crash types explain crash mechanisms to some extent, this study hypothesiz.es that the trajectories of the crash involved vehicles may provide additional information because they better capture the movements of the vehicles prior to collision. Furthermore, it is argued that injury pattem might be in.tluenced by vehicle travel direction and manoeuvre (Isaksson-Hellman and Wemeke, 2017). For example, when a car is moving straight ahead it is likely to have a higher speed than when it is turning, and if cyclists are struck at a higher impact speed, they tend to sustain more severe injury (Badea-Romero and Lenard, 2013). While many studies have evaluated the association between cyclist injwy severity and crash types, the factors that might influence cyclist injury severity related to trajectory types (vehicle movement and travel direction) have not yet been thoroughly investigated. This study aims to examine the factors associated with cyclists' injury severity for 'trajectory types• compared with the typically used 'crash types' at intersections.

Falls are responsible for a large proportion of serious injuries and deaths among cyclists [1-4]. A common fall scenario is loss of balance during an emergency braking maneuver to avoid another vehicle [5-7]. Automated Vehicles (AV) have the potential to prevent these critical scenarios between bicycle and cars. However, current Threat Assessment Algorithms (TAA) used by AVs only consider collision avoidance to decide upon safe gaps and decelerations when interacting wih cyclists and do not consider bicycle specific balance-related constraints. To date, no studies have addressed this risk of falls in safety critical scenarios. Yet, given the bicycle dynamics, we hypothesized that the existing TAA may be inaccurate in predicting the threat of cyclist falls and misclassify unsafe interactions. To test this hypothesis, this study developed a simple Newtonian mechanics-based model that calculates the performance of two existing TAAs in four critical scenarios with two road conditions. Tue four scenarios are: (1) a crossing scenario and a bicycle following lead car scenario in which the car either (2) suddenly braked, (3) halted or (4) accelerated from standstill. These scenarios have been identified by bicycle-car conflict studies as common scenarios where the car driver elicits an emergency braking response of the cyclist [8-11] and are illustrated in Figure 1. The two TAAs are Time-to-Collision (TTC) and Headway (H). These TAAs are commonly used by AVs in the four critical scenarios that will be modelled. The two road conditions are a flat dry road and also a downhill wet road, which serves as a worst-case condition for loss of balance during emergency braking [12].

While cycling is promoted as a sustainable and healthy mode of transport in many eitles in the Global North [1, 2], there are increasing concerns about the safety of cyclists. The increasing bicycle use in urban areas leads to a more intensely used cycling network, resulting in safety risks for cyclists [3]. Since 2010, the number of bicycle fatalities stagnated and the number of severely injured cyclists increased by 28% until 2018 in the European Union [4]. lt is therefore necessary to examine how bicycle use and motorized vehicle use in cities affects the nunber of bicycle crashes. To investigate this, the effect of the network-wide hourly exposure to cyclists and motorized vehicles on bicycle crash frequency is examined. That is, the total number of cyclists and motorized vehicles in the whole road network for each hour of the week were estimated and used as the network-wide hourly exposure. This approach allowed us to capture safety impacts of temporal variation in the numbers of cyclists and motorized vehicles in the same network more accurately. lt is a different approach compared to most bicycle safety studies, which often only use the daily average of bicycle and motorized vehicle volumes. The work presented here is based on our publication in Safety Science [5].