{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/120118"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/120118","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Learning to influence vehicles’ routing in mixed-autonomy networks","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-09-01 without embargo terms","abstract_has_math":false,"creators":["Ma, Xiaoyu"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Mehr, Negar"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-05","date_published":"2023-05","updated_at":"2026-07-22T22:24:56Z","subjects":["Intelligent Transportation Systems","Automation Technologies For Smart Cities"],"languages":["en","eng"],"rights":["Copyright 2023 Xiaoyu Ma"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/120118","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Mehr, Negar"]},{"key":"dc:creator","label":"Author","values":["Ma, Xiaoyu"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-05","2023-05-03"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Intelligent Transportation Systems","Automation Technologies For Smart Cities"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2023 Xiaoyu Ma"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/120118"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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","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."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Learning to influence vehicles’ routing in mixed-autonomy networks"]}]}],"canonical_facts":{"dc:contributor":["Mehr, Negar"],"dc:creator":["Ma, Xiaoyu"],"dc:date":["2023-05","2023-05-03"],"dc:description":["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","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."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/120118"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Xiaoyu Ma"],"dc:subject":["Intelligent Transportation Systems","Automation Technologies For Smart Cities"],"dc:title":["Learning to influence vehicles’ routing in mixed-autonomy networks"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}