paper: Add reading guide at the end of the introduction

paper/paper.md

@@ -54,7 +54,9 @@ To answer this overarching question, we explore several key sub-questions:
 - **C.** Which potential undesired system effects are amplified and which are reduced by the introduction of self-driving cars?
 - **D.** Which potential policies are most effective in minimizing which undesired system effects while maintaining benefits under different uncertainties?
 
-By addressing these questions, this research aims to provide valuable insights for urban planners, policymakers, and transportation engineers as they prepare for the advent of self-driving cars. Understanding the potential system-wide effects of AVs is crucial for developing proactive strategies to maximize their benefits while mitigating unintended negative consequences in our urban environments.
+By addressing these questions, this research aims to provide insights for urban planners, policymakers, and transportation engineers as they prepare for the advent of self-driving cars. Understanding the potential system-wide effects of AVs is crucial for developing proactive strategies to maximize their benefits while mitigating unintended negative consequences in our urban environments.
+
+The remainder of this thesis is structured as follows: Section 2 describes the methodological approach, including the rationale for combining agent-based modeling with mesoscopic traffic simulation. Section 3 presents the model design and validation, demonstrating how the system can be represented to explore AV adoption effects. Section 4 details the experimental design used to investigate different scenarios and policy interventions. Section 5 presents the results of these experiments, examining AV adoption patterns, system-level effects, and policy effectiveness. Finally, Section 6 discusses the implications of these findings for urban transportation planning and policy, while Section 7 concludes with key insights and recommendations for future research. Supporting material is provided in five appendices: Appendix A provides a complete model description following the ODD protocol, Appendix B lists key modeling assumptions, Appendix C discusses model limitations, Appendix D details the experimental setup, and Appendix E shows some additional results not included in the main text.
 
 # 2. Methods
 This study employs agent-based modeling (ABM) combined with mesoscopic traffic simulation to investigate the system-level effects of autonomous vehicle adoption in urban environments. Agent-based modeling was chosen over alternatives like pure equation-based approaches or aggregated flow models because it allows explicit representation of heterogeneous decision-making and captures emergent system behavior from individual choices. This is particularly important for studying AV adoption, where individual-level factors like value of time preferences and car ownership interact with system-level effects like congestion to create complex feedback loops. Alternative methods like system dynamics could capture some feedback mechanisms but would miss the spatial granularity and heterogeneity essential for understanding urban mobility patterns.