Despite numerous studies on general human–robot interactions, in the context of transportation, automated vehicle (AV)–human driver interaction is not a well-studied subject. These vehicles have fundamentally different decision-making logic compared with human drivers and the driving interactions between AVs and humans can potentially change traffic flow dynamics. Accordingly, through an experimental study, this paper investigates whether there is a difference between human–human and human–AV interactions on the road. This study focuses on car-following behavior and conducted several car-following experiments utilizing Texas A&M University’s automated Chevy Bolt. Utilizing NGSIM US-101 dataset, two scenarios for a platoon of three vehicles were considered. For both scenarios, the leader of the platoon follows a series of speed profiles extracted from the NGSIM dataset. The second vehicle in the platoon can be either another human-driven vehicle (scenario A) or an AV (scenario B). Data is collected from the third vehicle in the platoon to characterize the changes in driving behavior when following an AV. A data-driven and a model-based approach were used to identify possible changes in driving behavior from scenario A to scenario B. The findings suggested there is a statistically significant difference between human drivers’ behavior in these two scenarios and human drivers felt more comfortable following the AV. Simulation results also revealed the importance of capturing these changes in human behavior in microscopic simulation models of mixed driving environments.
Traffic prediction is a major component of any traffic management system. With the increase in data sources and advancement in connectivity, data analysis and machine learning approaches for traffic prediction have gained a lot of attention. Most of the existing data analysis approaches in traffic prediction rely on aggregated inputs such as flow and density, with limited studies using the individual vehicle-level data. The time-space diagram of the vehicles can be constructed from the connected vehicles’ data. This plot is comprehensive and contains all the information about traffic flow dynamics at both microscopic and macroscopic levels. Accordingly, this study introduces a deep learning-based methodology to directly predict the traffic state based on the time-space diagram with the use of convolutional neural networks (CNN). The time-space diagram is directly used as the input to the traffic prediction model using a CNN. The prediction capability of the proposed model is compared with multilayer perceptron, support vector regression, and autoregressive integrated moving average, and the results indicate a superior capability of CNN in predicting flow and density across all possible values of these parameters.
I would like to give special thanks to my advisor, Dr. Stefan A. Romanoschi. I appreciate all his contributions of time, ideas and support to make this experience perfect. His enthusiasm and motivation was the driving forces throughout my graduate study. Without his guidance and determinations, I could never have explored the depths in my research. I am also very grateful to members of my thesis committee: Dr. Sahadat Hossain and Dr. Xinbao Yu, for reviewing this thesis and for their valuable inputs. I acknowledge former and present members of the Materials and Pavement Lab, Tito Nyamuhokya, Ali Abdullah, Reza Saeedzadeh, and Nickey Akbariyeh. It has been a pleasure working with them and I appreciate their technical support, generous help, and constructive discussions. I would also like to extend my sincere thanks to my best friend, Aida Homayoun, whose support and belief in me was a treasure. Great appreciation goes to my parents, Simin and Hossein. Both have inspired me with many commendable qualities and given me a good foundation with which to meet life. They have taught me to be hard-working, independent and persistent. Without their love and support, it would have been impossible to complete my research.
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