2026-09-22T21:22:57Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/120118 2023-09-04 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Mehr, Negar 2023-05 application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms The student, Xiaoyu Ma, accepted the attached license on 2023-04-26 at 17:45. The student, Xiaoyu Ma, submitted this Thesis for approval on 2023-04-26 at 17:54. This Thesis was approved for publication on 2023-05-03 at 16:39. DSpace SAF Submission Ingestion Package generated from Vireo submission #19201 on 2023-09-01 at 16:55:39 Learning to influence vehicles’ routing in mixed-autonomy networks Ma, Xiaoyu 2023-05-03 Intelligent Transportation Systems Automation Technologies For Smart Cities Road networks will soon be shared between human-driven and autonomous cars, i.e., they will operate under mixed vehicle autonomy. In this thesis, we consider control strategies that maximize throughput in mixed-autonomy traffic networks. In particular, we consider two control strategies that can influence vehicles' routing in mixed-autonomy networks such that the overall network performance is improved. First, we propose that unlike human-driven cars, which can be difficult to control, we can assume a level of control over autonomous cars, which provides us with an additional control input to affect traffic networks. We propose that in mixed-autonomy networks, the headway of autonomous cars can be assigned dynamically to influence vehicles' routing and reduce congestion. We argue that in mixed-autonomy networks, the headway of autonomous cars --- and consequently the capacity of link segments --- is not just a fixed design choice; but rather, it can be leveraged as an {infrastructure control} strategy to {dynamically} regulate capacities. Second, we consider a related but different control approach. We investigate the potential of using not constant but time-varying prices or tolls for vehicles to affect the route choices of autonomous or human-driven vehicles. We demonstrate how dynamically setting prices or tolls for traversing certain network links can increase the overall throughput of the network. To achieve these, we model the dynamics of mixed-autonomy traffic networks while accounting for the vehicles' route choice dynamics. We train an RL policy that learns to regulate either the headway of autonomous cars or the price assigned to each link, such that the total travel time in the network is minimized. We will show empirically that our trained policy can not only prevent the inefficiencies that result from selfish route choices but also decrease total travel time. Text eng https://hdl.handle.net/2142/120118 Copyright 2023 Xiaoyu Ma M.S. Thesis Electrical & Computer Engr University of Illinois at Urbana-Champaign Electrical & Computer Eng