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This paper focuses on finding and analyzing the relevant parameters affecting traffic flow when autonomous vehicles are introduced for ride hailing applications and autonomous shuttles are introduced for circulator applications in geo-fenced urban areas. For this purpose, different scenarios have been created in traffic simulation software that model the different levels of autonomy, traffic density, routes, and other traffic elements. Similarly, software that specializes in vehicle dynamics, physical limitations, and vehicle control has been used to closely simulate realistic autonomous vehicle behavior under such scenarios. Different simulation tools for realistic autonomous vehicle simulation and traffic simulation have been merged together in this paper, creating a realistic simulator with Hardware-in-the-Loop (HiL), Traffic-in-the-Loop (TiL), and Software in-the-Loop (SiL) simulation capabilities.

Researchers and engineers are utilizing big data analytics to draw further insights into transportation systems. Large amounts of data at the individual vehicle trip level are being collected and stored. The true potential of such data is still to be determined. In this paper, we are presenting a data-driven, novel, and intuitive approach to model driver behaviors using microscopic traffic simulation. Our approach utilizes metaheuristic methods to create an analytical tool to assess vehicle performance. Secondly, we show how microscopic simulation run outputs can be post-processed to obtain vehicle and trip level performance metrics. The methodology will form the basis for a data-driven approach to unearthing trip experiences as realized by drivers in the real world. The methodology will contribute to, A.) Using vehicle trajectory traces to identify underlying vehicle maneuver distributions as obtained from real-world driver data, B.)

Historical driver behavior and drive style are crucial inputs in addition to V2X connectivity data to predict future events as well as fuel consumption of the vehicle on a trip. A trip is a combination of different maneuvers a driver executes to navigate a route and interact with his/her environment including traffic, geography, topography, and weather. This study leverages big data analytics on real-world customer driving data to develop analytical modeling methodologies and algorithms to extract maneuver-based driving characteristics and generate a corresponding maneuver distribution. The distributions are further segmented by additional categories such as customer group and type of vehicle. These maneuver distributions are used to build an aggressivity distribution database which will serve as the parameter basis for further analysis with traffic simulation models.

The interaction of Connect and Automated vehicles (CAV) with regular vehicles in the traffic stream has been extensively researched. Most studies, however, focus on calibrating driver behavior models for CAVs based on various levels of automation and driver aggressiveness. Other related studies largely focus on the coordination of CAVs and infrastructure like traffic signals to optimize traffic. However, the effects of different strategic flow management of CAVs in the traffic stream in the comparative scenario-based analysis is understudied. Thus, this study develops a framework and simulations for integrating CAVs in a corridor section. We developed a calibrated model with CAVs for a corridor section in Corktown, Michigan, and simulate how dedicated CAV lane operations can be implemented without significant change in existing infrastructure.

Autonomous vehicles are expected to change our lives with significant applications like on-demand, shared autonomous taxi operations. Considering that most vehicles in a fleet are parked and hence idle resources when they are not used, shared on-demand services can utilize them much more efficiently. While ride hailing of autonomous vehicles is still very costly due to the initial investment, a shared autonomous vehicle fleet can lower its long-term cost such that it becomes economically feasible. This requires the Shared Autonomous Vehicles (SAV) in the fleet to be in operation as much as possible. Motivated by these applications, this paper presents a simulation environment to model and simulate shared autonomous vehicles in a geo-fenced urban setting.