1.1 Background

The transportation volume of passengers and goods is increasing with time to a greater extent than the growth of the population, which makes an impact on the environment and people’s life in the future [1]. Sustainable transport management is needed for the current transport modes and future industry to avoid the negative consequences of the continuous increase of demand for movements [2]. Providing high-quality transportation requires continuous research and the elaboration of studies since it is connected to people’s requirements. Innovation in technology and modelling tools makes the analysis of travel patterns possible, especially the travel behavior can be efficiently explored. People’s travel behavior is affected by the improvement of transportation technology, especially car manufacturing. Recently, vehicles have been developed, where different automation levels (Level 0 to Level 5) are introduced [3, 4][5]. Nowadays, the main challenge is to reach full driving automation (ie, Level 5) in an operational environment. A full driving automation vehicle is considered as an innovation that makes positive impacts on the environment, economics, safety, traffic, and travelers’ mobility, as stated by several scholars [6-10]. AVs are fully automated, self-driving vehicles, which provide the opportunity to use a car without restrictions, eliminate the time spent on parking, release the stress of driving, and make driving without driving licenses possible [6, 9, 10]. Travelers’ traditional mobility patterns are altered once AVs arrive on the market [11]. Litman [12] explains that AVs provide a door-to-door service, which influences the travelers’ arrival and departure times. The benefits of AVs are diverse, such as minimizing the travel time [13], removing the parking process time [14], conducting onboard activities [15], and utilizing the parking spaces generated from the decrease in parking demand [16, 17]. Zhong et al.[18] state that the acceptability of AV as a replacement transport mode depends on the value of travel time (VOT). The VOT is typically used as a reference point to understand the tradeoffs between the travel time and the travel cost, as well as it is often applied to evaluate transportation projects, policies and services. Small [19] concludes that the VOT is not equally valued by every traveler since travel time valuation is based on various factors; moreover, people might pay additional money to reduce the perceived travel time during traveling. Due to the absence of empirical studies on the impact of AVs on travel behavior, different approaches, such as agent-based models, discrete choice modeling, and multicriteria analysis approaches, should be applied to evaluate the effects of AVs on people’s travel behavior.

The integration of AVs with other conventional transport modes (CTMs)(ie, public transport, conventional cars, bicycle, train, walking, taxi, scooter, and motorcycle) requires the study of user behavior and the optimization possibilities of the multimodal travel chains. This optimization is presented by integrating the newly developed transport modes into the available activity-based models to study the consequences of future changes. Moreover, to understand the impact of AVs on the travelers’ behavior, two parts of the daily activities, ie, the travel