Automated shuttles are designed to provide a clean transportation and improve access to areas such as where travelers have to walk long distances to/from bus stops. The introduction of automated shuttles in the road network might affect the safety of pedestrians and cyclists as well as traffic performance of motorized vehicles. Several demonstration trials are being conducted to study how automated shuttles operate in real traffic conditions, but they are limited to few vehicles and evaluations of traffic effects at higher penetration rates are not possible. Traffic simulation is a tool that can be used to study effects on traffic performances at different penetration rates of e.g., automated shuttles. However, automated shuttles have not yet been modeled, calibrated, and validated in microscopic traffic simulation tools. Therefore, the objective of this thesis is to model, calibrate and validate automated shuttle’s behavior using the simulation tool SUMO and data collected from the demonstration trial on the area of campus Valla Linköping University, Sweden. The pilot study consists of two automated shuttles, and they operate on a 2.1 km fixed route. The collected data by one of the automated shuttles is analyzed with a focus on the free driving behavior. The analysis shows that the automated shuttle has different maximum operation speeds at different locations and defining one value for the maximum speed when setting up the simulation is not enough. Therefore, virtual speed limits are derived by mimicking the maximum operation speed of the shuttle from the data and used to define segment specific speed limits in the simulation. Additionally, the data is used to calibrate the acceleration and deceleration parameters. The Krauss and the IDM car-following models have been investigated by calibrating the acceleration and deceleration parameters for the free driving situation. The results indicate that both the Krauss and IDM car-following models follows the general trend of the speed and acceleration profiles. The speed profiles produced with the IDM model have smoother profiles at the start and end of acceleration and deceleration phases while in Krauss model the transition of the speed change is more direct and there are in principle no delays for reaction. Although the IDM model performs slightly better for the free driving situation, it can be of interest to consider both models for the calibration of interactions with other roads users since both models are able to capture the general trend of the speed and acceleration profiles. / <p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>

Discourse about the real-world effects of automated vehicles has intensified over the last decade, but few observational studies have been made examining their integration in real traffic. This research is based on the dataset prepared by Beauchamp et al. in [1] where video footage from two pilot projects involving automated shuttles in Montreal and Candiac in 2019 was analyzed to compute safety indicators from road user trajectories. The study showed that automated shuttles have safer interactions with other road users compared to human drivers following the same trajectories. Yet, this may not be the only characteristic of automated shuttles. These vehicles are notoriously slow, 10 to 15 km/h slower than human-driven cars in city traffic [1], which on city streets is bound to influence other road users, in particular following cars. lt is therefore hypothesized that automated shuttles may have a traflic calming effect, slowing other motorized vehicles [2]. Slower speed and the predictability of automated shuttles, obeying the rules of the road and yielding more willingly to vulnerable road users (pedestrians and cyclists) may also have an impact on these users' behavior [3]: for example, cyclists may pass the shuttle, pedestrians may cross outside of crosswalks. The present study aims to explore the potential effects of automated shuttles, with their slower spceds and more predictable behavior, on the behavior of other road users. [from Introduction]