Vehicles with different capabilities for automated driving will gradually be deployed in road transportation systems over the coming decades. Mixed traffic conditions may change the characteristics of the traffic flow dynamics. 

Microscopic traffic simulation is used for studying traffic flow dynamics in transportation systems. By simulating the interactions between individual vehicles, effects caused by changes in the road infrastructure, by road closures, or by the number and the types of vehicles can be investigated. Impacts on traffic performance can be analyzed in terms of travel times, travel time delays, queue formations, or vehicle throughput. To evaluate the impact of automated driving on traffic performance using microscopic traffic simulation, existing microscopic driving models need to be further developed to describe automated driving. 

The aim of this thesis is to investigate how to further develop microscopic traffic simulation models for automated driving. In this investigation, the aspects to consider in simulation experiments including automated driving are identified. These aspects are the vehicle system, the role of authorities, the role of the users, of the infrastructure, of connectivity features, and of the sensor-based perception of the vehicles. A microscopic traffic simulation experiment showing the possible effects on a motorway in terms of vehicle throughput and travel delays is presented. 

A conceptual model that describes how driving automation systems deal with the perception tasks is proposed. Future research directions will focus on implementing this model for perception in traffic simulation platforms and on the modeling of lateral tactical maneuvers. iii 

The introduction of automated driving systems (ADSs) in road transportation systems will affect the traffic flow characteristics, and have ripple effects which will lead to larger societal implications. The traffic flow is characterized by speed, density, and vehicular throughput, which determine the road capacity and the traffic performance in terms of, among others, travel times and delays. A tool used to study traffic flow dynamics and analyze traffic performance is microscopic traffic simulation, which works by describing the interactions between road users to simulate observed traffic phenomena.

To use microscopic traffic simulation to evaluate the impact of ADSs on traffic performance, driving models need to be able to simulate driving decisions and behavioral patterns of ADSs. Driving models have been proposed specifically for ADSs, however, it remains to be validated whether these driving models when used in combination with traditional human driving models adequately simulate mixed traffic that includes human drivers and ADSs. Ideally, a clear interpretation of the behavioral assumptions for each type of vehicle should be possible, as these determine the simulation results. However, it is challenging to compare behavioral assumptions when using different driving models to describe different vehicle types. Empirical research has validated that some driving models, such as the intelligent driver car-following model (IDM), are well-suited for describing both human or automated driving when calibrated with the proper data.

The aim of this thesis is two fold: to further develop microscopic traffic simulation for the study of mixed traffic, and to evaluate the effects of mixed traffic on motorway traffic performance. To enhance the modeling of mixed traffic, a model for perception is proposed which allows the explicit inclusion of perception errors in driving decisions. Its use, in combination with driving models capable of describing both human and automated driving, enables to make distinctions between human drivers and ADSs both in perception capabilities and in driving behavior. This modeling approach focuses on describing essential differences to simulate mixed traffic and removes risks involved in using different driving models.

Simulation experiments are conducted using state-of-the-art tools to evaluate the modeling of perception errors on traffic flow dynamics and to evaluate the effects of mixed traffic on motorway traffic performance.