The deployment of Automated Vehicles (AV) is starting to become widespread throughout transportation, resulting in the recognition and awareness by legislative leaders of the potential impact on transportation operations. To assist transportation operators in making the needed preparations for these vehicles, an in-depth study regarding the impact of AV and Connected Automated Vehicles (CAV) is needed. In this research, the impact of AV and CAV on the highway setting is studied. This study addresses car-following models that are currently used for simulating AV and CAV. Diverse car-following models, such as the Intelligent Driver Model (IDM), the IDM with traffic adaptive driving Strategy (SIDM), the Improved IDM (IIDM), the IIDM with Constant-Acceleration Heuristic (CAH), and the MIcroscopic model for Simulation of Intelligent Cruise control (MIXIC) were examined with the state-of-the-art vehicle trajectory data. The Highway Drone dataset (HighD) were analyzed through the implementation of genetic algorithm to gain more insight about the trajectories of these vehicles. In 2020, there is no commercially available gully automated vehicle available to the public, although many companies are conducting in field testing. This research generated AV trajectories based on the actual vehicle trajectories from the High-D dataset and adjusts those trajectories to account for ideal AV operations. The analysis from the fitted trajectory data shows that the calibrated IIDM with CAH provides a best fit on AV behavior. Next, the AV and CAV were modeled in microscopic perspective to show the impact of these vehicles on a corridor. The traffic simulation software, VISSIM, modified by implementing an external driver model to govern the interactions between Legacy Vehicles (LV), AV, and CAV on a basic and merging highway segment as well as a model of the Interstate 95 corridor south of Richmond, Virginia. From the analysis, this research revealed that the AV and CAV could increase highway capacity significantly. Even with a small portion of AV or CAV, the roadway capacity increased. On I-95, CAV performed better than AV because of Cooperative Adaptive Cruise Control (CACC) and platooning due to CAV's ability to coordinate movement through communication; however, in weaving segments, CAV underperformed AV. This result indicates that the CAV algorithms would need to be flexible in order to maintain flow in areas with weaving sections. Lastly, diverse operational conditions, such as different heavy vehicle market penetration and different aggressiveness were examined to support traffic operators transition to the introduction of AV and CAV. Based on the analysis, the study concludes that the different aggressiveness could mitigate congestion in all cases if the proper aggressiveness level is selected considering the current traffic condition. Overall, the dissertation provides guidance to researchers, traffic operators, and lawmakers to model, simulate, and evaluate AV and CAV on highways. / Doctor of Philosophy / The deployment of Automated Vehicles (AV) is starting to become widespread throughout transportation, resulting in the recognition and awareness by legislative leaders of the potential impact on transportation operations. To assist transportation operators in making the needed preparations for these vehicles, an in-depth study regarding the impact of AV and Connected Automated Vehicles (CAV) is needed. In this research, the impact of AV and CAV on the highway setting is studied. This study addresses car-following models that are currently used for simulating AV and CAV. Diverse car-following models, such as the Intelligent Driver Model (IDM), the IDM with traffic adaptive driving Strategy (SIDM), the Improved IDM (IIDM), the IIDM with Constant-Acceleration Heuristic (CAH), and the MIcroscopic model for Simulation of Intelligent Cruise control (MIXIC) were examined with the state-of-the-art vehicle trajectory data. The Highway Drone dataset (HighD) were analyzed through the implementation of genetic algorithm to gain more insight about the trajectories of these vehicles. In 2020, there is no commercially available gully automated vehicle available to the public, although many companies are conducting in field testing. This research generated AV trajectories based on the actual vehicle trajectories from the High-D dataset and adjusts those trajectories to account for ideal AV operations. The analysis from the fitted trajectory data shows that the calibrated IIDM with CAH provides a best fit on AV behavior. Next, the AV and CAV were modeled in microscopic perspective to show the impact of these vehicles on a corridor. The traffic simulation software, VISSIM, modified by implementing an external driver model to govern the interactions between Legacy Vehicles (LV), AV, and CAV on a basic and merging highway segment as well as a model of the Interstate 95 corridor south of Richmond, Virginia. From the analysis, this research revealed that the AV and CAV could increase highway capacity significantly. Even with a small portion of AV or CAV, the roadway capacity increased. On I-95, CAV performed better than AV because of Cooperative Adaptive Cruise Control (CACC) and platooning due to CAV's ability to coordinate movement through communication; however, in weaving segments, CAV underperformed AV. This result indicates that the CAV algorithms would need to be flexible in order to maintain flow in areas with weaving sections. Lastly, diverse operational conditions, such as different heavy vehicle market penetration and different aggressiveness were examined to support traffic operators transition to the introduction of AV and CAV. Based on the analysis, the study concludes that the different aggressiveness could mitigate congestion in all cases if the proper aggressiveness level is selected considering the current traffic condition. Overall, the dissertation provides guidance to researchers, traffic operators, and lawmakers to model, simulate, and evaluate AV and CAV on highways.

In the context of automated vehicles, transparency of in-vehicle intelligent agents (IVIAs) is an important contributor to drivers' perception, situation awareness (SA), and driving performance. However, the effects of agent transparency on driver performance when the agent is unreliable have not been fully examined yet. The experiments in this Thesis focused on different aspects of IVIA's transparency, such as interaction modes and information levels, and explored their impact on drivers considering different system reliability. In Experiment 1, a 2 x 2 mixed factorial design was used in this study, with transparency (Push: proactive vs. Pull: on-demand) as a within-subjects variable and reliability (high vs. low) as a between-subjects variable. In a driving simulator, twenty-seven young drivers drove with two types of in-vehicle agents during Level 3 automated driving. Results suggested that participants generally preferred the Push-type agent, as it conveyed a sense of intelligence and competence. The high-reliability agent was associated with higher situation awareness and less workload, compared to the low-reliability agent. Although Experiment 1 explored the effects of transparency by changing the interaction mode and the accuracy of the information, a theoretical framework was not well outlined regarding how much information should be conveyed and how unreliable information influenced drivers. Thus, Experiment 2 further studied the transparency regrading information level, and the impact of reliability on its effect. A 3 x 2 mixed factorial design was used in this study, with transparency (T1, T2, T3) as a between-subject variable and reliability (high vs. low) as a within-subjects variable. Fifty-three participants were recruited. Results suggested that transparency influenced drivers' takeover time, lane keeping, and jerk. The high-reliability agent was associated with the higher perception of system accuracy and response speed, and longer takeover time than the low-reliability agent. Participants in T2 transparency showed higher cognitive trust, lower workload, and higher situation awareness only when system reliability was high. The results of this study may have significant effects on the ongoing creation and advancement of intelligent agent design in automated vehicles. / Master of Science / This thesis explores the effects of system's transparency and reliability of the in-vehicle intelligent agents (IVIAs) on drivers' performance and perception in the context of automated vehicles. Transparency is defined as the amount of information and the way to be shared with the operator about the function of the system. Reliability refers to the accuracy of the agent's statements. The experiments focused on different aspects of IVIA's transparency, such as interaction modes (proactive vs. on-demand) and information composition (small vs. medium vs. large), and how they impact drivers considering different system reliability. In the experiment, participants were required to drive in the driving simulator and follow the voice command from the IVIAs. A theoretical model called Situation Awareness-based Agent Transparency Model was adopted to build the agent's interactive scripts. In Experiment 1, 27 young drivers drove with two types of in-vehicle agents during Level 3 automated driving. Results suggested that participants generally preferred the agent that provided information proactively, and it conveyed a sense of intelligence and competence. Also, when the system's reliability is high, participants were found to have higher situation awareness of the environment and spent less effort on the driving tasks, compared to when the system's reliability is low. Our result also showed that these two factors can jointly influence participants' driving performance when they need to take over control from the automated system. Experiment 2 further studied the transparency regarding the information composition of the agent's voice prompt and the impact of reliability on its effect. A total of 53 participants were recruited, and the results suggested that transparency influenced drivers' takeover time, lane keeping, and jerk. The high-reliability agent was associated with a higher perception of system accuracy and response speed and a longer time to take over when requested than the low-reliability agent. Participants in the medium transparency condition showed higher cognitive trust toward the system, perceived lower workload when driving, and higher situation awareness only when system reliability was high. Overall, this research highlights the importance of transparency in IVIAs for improving drivers' performance, perception, and situation awareness. The results may have significant implications for the design and advancement of intelligent agents in automated vehicles.

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Autonomous Vehicles (AVs) are computer equipped vehicles that can operate without human driver’s active control using information provided by their sensors about the surrounding environment. Self-driving vehicles may have seemed to be a distant dream several years ago, but manufactures’ prototypes showed that AVs are becoming real now. Several car manufactures (i.e. Benz, Audi, etc.) and information technology firms (i.e. Google) have either showcased their fully AVs or announced their robot cars to be released in a few years. AVs hold the promise to transform the ways we live and travel. Although several studies have been conducted on the impacts of AVs, much remains to be explored regarding the various ways in which AVs could reshape our lifestyle. This dissertation addresses the knowledge gap in understanding the potential implications of AV technologies on travel behavior and system modeling. A comprehensive review of literature regarding AV adoption, potential impacts and system modeling was provided. Bass diffusion models were developed to investigate the market penetration process of AVs based on experience learned from past technologies. A stated preference survey was conducted to gather information from university population on the perceptions and attitudes toward AV technologies. The data collected from the Florida International University (FIU) was used to develop econometric models exploring the willingness to pay and relocation choices of travelers in light of the new technologies. In addition, the latest version of the Southeast Planning Regional Model (SERPM) 7.0, an Activity-Based Model (ABM), was employed to examine the potential impacts of AVs on the transportation network. Three scenarios were developed for short-term (2035), mid-term (2045) and long-term (2055) conditions. This dissertation provides a systematic approach to understand the potential implications of AV technologies on travel behavior and system modeling. The results of the survey data analysis and the scenario analysis also provide important inputs to guide planning and policy analysis on the impacts of AV technologies.

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Sharing automated vehicles (AVs) is a possible future, where shared automated vehicles (SAVs) and pooled automated vehicles (PooledAVs) are prospective on-demand AV configurations. While SAVs and PooledAVs can contribute to the sustainability of transport systems, the success of on-demand AVs depends on whether and how the public adopts them as regular travel modes. As such, this dissertation investigates five objectives: (1) to scrutinize the essential steps of designing a future mobility survey , while the primary focus of the survey is on respondents’ intentions to adopt various AV configurations (2) to propose and validate a theoretical model for on-demand AV adoption by extending the Theory of Planned Behavior (TPB), (3) to identify the prospective use cases of SAVs as the potential precursor of on-demand AVs, (4) to identify individual characteristics that may trigger different behavioral intentions among the on-demand AV service types, and finally (5) to investigate Canadians’ intentions to adopt on-demand AVs. A nationwide Canadian survey was designed and administered in fall 2021 (n = 5002) among adults (18 to 75 years old) residing in six major Canadian metropolitan areas: Toronto, Vancouver, Ottawa-Gatineau, Montréal, Calgary, and Hamilton. The findings of this dissertation paint a complex picture of on-demand AV adoption in the Canadian context with respect to the application of constructs from common technology adoption models and will help researchers investigating the characteristics of prospective consumers of on-demand AVs to identify the importance of affective motivations regarding adopting such emerging travel modes. The results reveal that many Canadians are yet either uncertain or reluctant to adopt AV technology in shared mobility services. In this light, policymakers and planners should adjust and moderate their expectations regarding the future market for on-demand AVs and be prepared for potential changes in travel behavior by examining incremental changes in existing on-demand ride-hailing services. / Dissertation / Doctor of Philosophy (PhD) / This dissertation assesses the conditions under which Canadians are willing to use fully automated vehicles (AVs) and investigates public perceptions and intentions to use “automated ride-hailing services,” which function as a taxi or Uber/Lyft service without a driver, and “pooled automated ride-hailing services,” which are a form of ride-hailing services, where passengers share a ride with someone they do not know to save on the cost of travel. To this end, an online survey (n = 5002) was designed and administered in October and November 2021 across six major Canadian metropolitan areas: Toronto, Vancouver, Ottawa-Gatineau, Montreal, Calgary, and Hamilton. Overall, results suggest that expectations towards AVs suddenly transforming the entire transportation sector, should be moderated and “automated ride-hailing services” and “pooled automated ride-hailing services” (when they are available in the entire Canadian market) are likely to be adopted as a supplementary mobility tool rather than a substitution for current travel modes.

Many cities suffer from problems with high traffic flows in the city centers which leads to a desire to get more people to choose public transport over cars. For many car drivers, the main reason to take the car is the convenience and time efficiency; the price is often of less importance. The public transport providers should, therefore, strive to improve their Level of Service (LOS). A general process that can be used by public transport providers or other stakeholders to evaluate the LOS in a public transport system based on Automated Vehicle Location (AVL) data is developed and presented in this thesis.The process values the quality and suitability of the AVL data, propose which KPIs to use and how to use the results to find possible improvements. Four different types of erroneous data were discovered: outliers in position, outliers in speed, outliers in travel time and general errors. KPIs are developed in three main areas: on-time performance, travel time distribution and speed, where each KPI is divided into several sub-areas.

This study investigates the impact of peripheral visual information in alleviating motion sickness when engaging in non-driving tasks in fully automated driving. A peripheral visual feedforward system (PVFS) was designed providing information about the upcoming actions of the automated car in the periphery of the occupant’s attention. It was hypothesized that after getting the information from the PVFS, the users’ situation awareness is improved while motion sickness is prevented from developing. The PVFS was also assumed not to increase mental workload nor interrupt the performance of the non-driving tasks. The study was accomplished on an actual road using a Wizard of Oz technique deploying an instrumented car that behaved like a real fully automated car. The test rides using the current setup and methodology indicated high consistency in simulating the automated driving. Results showed that with PVFS, situation awareness was enhanced and motion sickness was lessened while mental workload was unchanged. Participants also indicated high hedonistic user experience with the PVFS. While providing peripheral information showed positive results, further study such as delivering richer information and active head movement are possibly needed.