# Criteria for Route Evaluation of Automated Buses
## 2 Literature Review  
Five sources have been identified in the literature that explain the criteria for the route evaluation of automated buses. These are explained below. The British Standards Institution published a standard for the specification of the ODD in 2020, which is designated as PAS 1883:2020. PAS 1883:2020 is not to be considered a British Standard, but its further development as a British Standard may be considered. Rather, this PAS is intended to support the various stakeholders in defining the ODD as quickly as possible. This standard, which is based on SAEJ3016, contains criteria for the specification of the ODD to enable the safe use of an automated driving system. All criteria influencing the ODD are categorized into scenery, environmental conditions, and dynamic elements. The scenery includes fixed road structures, parking situations, road surface conditions, and the number of lanes, among other things. Environmental conditions include weather, illumination, and connectivity (GPS and V2X communication). Dynamic elements include volume of traffic, density of agents, and the presence of special vehicles, such as ambulances or police vehicles [27]. In 2020, [PERSON] et al. described that there was no uniform scheme for evaluating drivability in the field of automated driving. Therefore, based on a literature analysis, seven criteria for the environment (weather, illumination, road geometry, road condition, lane marking, traffic condition, and objects) and four criteria for behavior (behavior of vehicles, pedestrians, drivers, and bicycles) were developed. Since the behavior of drivers depends, among other things, on road conditions, the interrelation of the criteria was also examined. The criteria were ultimately used to cluster data sets for training machine learning algorithms [28]. On the basis of the work on the walkability and bikeability index and a literature analysis related to the current state of development of automated vehicles as well as various street spaces and their characteristics, [PERSON] et al. identified 21 components that influenced the general drivability of automated vehicles [29]. These components were organized into five main categories (Table 1). Indicators were then formulated for each component. For example, the indicators \"number of pedestrians\", \"number of bicyclists\", and \"number of cars\" were derived from the component \"number of roadway users\". In addition, the influence (direct or indirect) and the data source for each indicator were added. The indicators were evaluated using the city of Vienna as an example. Of the 48 indicators, 20 indicators were selected for the evaluation, since data were only available for these indicators. Numerical, binary and categorical indicators were used, which were normalized after the survey and then weighted. The weighting was based on trends from the literature. The results for the entire city of Vienna were visualized using a map display with ArcGIS. The streets that were suitable for automated vehicles were marked in color [29]. [PERSON] et al. conducted mathematical modeling to optimize the road network for automated vehicles [30]. The modeling was tested on the basis of a use case in Amsterdam and provided a map showing areas that were suitable for automated vehicles [30]. These included highways, regional primary roads, and urban primary roads, as complex interactions between automated vehicles and vulnerable road users were avoided [30]. Complex interactions included, for example, intersections where different road users came together, which increased the risk of accidents [31]. Furthermore, urban roads, for example, were much more complicated for the system and led to more frequent system failures than on highways, for example [32].