Traflic safety and accessibility have been two important subjects in transportation research. On the one hand traffic crashes bring about high societal costs and serious health risks for urban road users. The cost oftraffic crashes is estimated to be 17 billion euros per year only in the Netherlands while over 600 people were killed in traffic, of whom 229 were cyclists and 195 were car users [l, 2]. Accessibility, on the other band, is regarded as one of the indicators of the quality of the transport system serving the public. There is comprehensive literature investigating the relationship between traffic crashes and factors associated with traffic, roadway design, built environment, and human factors. Similarly, several studies assessed and evaluated accessibility levels of individuals, communities, and regions by utilizing the aforementioned. factors. Nevertheless, there is a scarcity ofliterature investigating the relationships between accessibility and traffic safety. This is especially surprising considering that both subjects are associated with a similar set of factors, including land use and transport systems, as weil as individual and temporal factors [3-7]. The relationships between accessibility and traffic safety can be an adverse one; for example, improved accessibility by increasing the travel speeds (i.e., declining travel time) intensifies the crash risks which also deteriorates equity. Furthermore, levels ofboth accessibility and traffic safety are not homogeneous throughout urban areas and among different population groups. Based on the literature, it is obvious that accessibility is associated with economic equity [8]. lt is revealed that accessibility of lower-income groups is substantially worse than the higher-income groups as these groups have less mobility [9]. Previous studies also showed. that lower-income groups usually suffer from traffic safety problems more than other socio-economic groups [10-12]. Therefore, this research aims to address the aforementioned gap in the literature in understanding the relationships between accessibility levels and traffic safety with a focus on social equity perspecti.ves. For this purpose, a Gravity model and risk exposure evaluation approaches are utilix.ed to analyze traffic safety and accessibility to jobs by bicycle via extending the traditional definition of accessibility based on only travel time or proximity to a location.

Connected and automated vebicles (CA Vs) will shape traffic patterns in the future and greatly influence urban mobility. A particular challenge for CAVs is to anticipate the movements of other road users. This applies especially to micromobility vehicles (bicycles, smaU electric vehicles), whose traffic behaviour is difficult to predict and shaped from individual behaviour. The increasing coexistence of CAVs and other, conventionally driven modes of transport thus has a growing impact as well as multiple consequences for urban structures and public space. The following fundamental trends will shape the way people live together in cities in the coming years: 1. increasing share of CAVs and micromobility, 2. renaissance ofthe mixed and liveable city, 3. changes in mobility behaviour and the appreciation of public space ( especially due to climate change and the Covid 19-pandemic), as weil as 4. technical upgrading of infrastructure. These parallel developments will lead to both conflicts and opportunities for cities.[from Introduction]

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].