## 1 Introduction
The existing literature considers various network configurations and parking arrangements in its modeling and analysis. By incorporating privately owned parking slots during peak periods, [PERSON] et al. [10] formulate the commuters' parking location and departure time choice and investigate impacts of parking provision strategies on the total travel cost. In addition to extending the classic bottleneck models, various alternative approaches can be utilized in modeling commuters' decisions in morning commute problems. Following the UE principle, traffic assignment models are formulated to predict the flow distribution over road networks. By developing a static traffic assignment model, [PERSON] et al. [11] study how commuters' joint decisions on route, mode, and parking choices affect the congestion distribution under a partially autonomous environment. In a generous case, the time-of-day features of road networks are significant. In the meantime, multiple bottlenecks may be observed in a road network and the congestion may correlate spatially. In this case, dynamic traffic assignment models are established to account for the time-varying properties of road networks and spatial interaction of traffic congestion, e.g., [12; 13]. The travel patterns during the morning peak period have also been investigated using data analysis methods, e.g., [14]. [PERSON] et al. [15] explore the dynamic congestion pricing scheme design by integrating reinforcement learning techniques with dynamic traffic assignment modeling. However, AVs are not widely adopted by commuters in the real world, which incurs the lack of historical trip data of AVs. Currently, the historical trip data of AVs are limited since AVs are not widely adopted by commuters in the real world. In this case, simulated data of AVs may be used when data analysis methods are used [16]. With the emergence of autonomous vehicle technologies, the bottleneck model has been extended to study how AV technologies change equilibrium travel patterns. [PERSON] et al. [17] study the influences of different penetration rates of AVs on traffic congestion. By establishing a many-to-one network, [PERSON] and [PERSON] [18] investigate a parking sharing problem in which AV commuters have different parking options. In their framework, commuters can choose to park at home, use shared parking slots, or share their own parking slots out, and in the meantime park their own vehicles at a public parking lot. [PERSON] and [PERSON] [19] formulate the departure time and parking location decisions of commuters in a mixed traffic environment with both AVs and HVs. [PERSON] et al. [20] explore the impacts of different road capacity strategies on morning commuting patterns when commuters make combined departure time and parking decisions. Various parking management policies are developed to improve system performance measured by system-wide travel costs or total traffic congestion. When ridesharing services are introduced into the morning commute, [PERSON] and [PERSON] [21] design combined ridesharing compensation and dynamic parking pricing schemes to eliminate traffic congestion. [PERSON] et al. [10] conclude that both individual welfare and the total system cost can be decreased through parking regulation strategies, including price-ceiling and quantity subsidy. Assuming that the parking slots near the workplace can be grouped into two clusters, [PERSON] et al. [22] model the joint decisions of morning commuters on departure times and parking locations. After examining the parking profiles at equilibrium, they explore the optimal design of parking provision strategies regarding parking pricing and parking capacity setting. After AVs are introduced to road networks, parking management policies are designed to manage traffic congestion and parking patterns. [PERSON] et al. [23] propose a tradable credit scheme to manage traffic congestion in morning commute by assuming that all commuters travel with AVs.