{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/46638"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/46638","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Monitoring and control of extreme congestion events in road traffic networks","abstract":"This thesis addresses the problem of traffic management during extreme traffic congestion events. In particular, it describes the development of a new traffic sensing technology called TrafficTurk that is capable of being deployed through multiple temporary sensors quickly over an entire road network. The development of the technology is motivated by the enormous cost to society that is associated with extreme traffic congestion caused by pre-planned events such as sporting and cultural events, extreme weather and natural disasters. The eventual goal of the technology is to provide a comprehensive system for traffic management of extreme congestion events through real-time data collection and processing. Therefore, detailed explanations of data processing algorithms that are required to enable further development of the system are also provided. Specifically, a method to estimate traffic controller strategies at intersections in a road network using inverse optimal control and a method to normalize traffic data collected using TrafficTurk are explained in detail. Additionally, practical implementation knowledge gained thus far through extensive field testing of the system is also discussed.","abstract_html":"This thesis addresses the problem of traffic management during extreme traffic congestion events. In particular, it describes the development of a new traffic sensing technology called TrafficTurk that is capable of being deployed through multiple temporary sensors quickly over an entire road network. The development of the technology is motivated by the enormous cost to society that is associated with extreme traffic congestion caused by pre-planned events such as sporting and cultural events, extreme weather and natural disasters. The eventual goal of the technology is to provide a comprehensive system for traffic management of extreme congestion events through real-time data collection and processing. Therefore, detailed explanations of data processing algorithms that are required to enable further development of the system are also provided. Specifically, a method to estimate traffic controller strategies at intersections in a road network using inverse optimal control and a method to normalize traffic data collected using TrafficTurk are explained in detail. Additionally, practical implementation knowledge gained thus far through extensive field testing of the system is also discussed.","abstract_has_math":false,"creators":["Gowrishankar, Sudeep"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["Dullerud, Geir E."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-01-16T17:57:02Z","date_published":"2014-01-16T17:57:02Z","updated_at":"2026-07-22T22:25:36Z","subjects":["Inverse Optimal Control","TrafficTurk","Traffic","Monitoring","Control","Extreme","Congestion","Events"],"languages":["en"],"rights":["Copyright 2013 Sudeep Gowrishankar"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/46638","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dullerud, Geir E."]},{"key":"dc:creator","label":"Author","values":["Gowrishankar, Sudeep"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-01-16T17:57:02Z","2013-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"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":["Inverse Optimal Control","TrafficTurk","Traffic","Monitoring","Control","Extreme","Congestion","Events"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2013 Sudeep Gowrishankar"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/46638"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This thesis addresses the problem of traffic management during extreme traffic congestion events. In particular, it describes the development of a new traffic sensing technology called TrafficTurk that is capable of being deployed through multiple temporary sensors quickly over an entire road network. The development of the technology is motivated by the enormous cost to society that is associated with extreme traffic congestion caused by pre-planned events such as sporting and cultural events, extreme weather and natural disasters. The eventual goal of the technology is to provide a comprehensive system for traffic management of extreme congestion events through real-time data collection and processing. Therefore, detailed explanations of data processing algorithms that are required to enable further development of the system are also provided. Specifically, a method to estimate traffic controller strategies at intersections in a road network using inverse optimal control and a method to normalize traffic data collected using TrafficTurk are explained in detail. Additionally, practical implementation knowledge gained thus far through extensive field testing of the system is also discussed.","Item withdrawn by Laura Spradlin (lspradl2@illinois.edu) on 2013-12-11T16:54:36Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Gowrishankar_Sudeep.pdf: 6196979 bytes, checksum: b5dffb0f8e899746b4a27d81ec06b8fd (MD5)","Made available in DSpace on 2014-01-16T17:57:02Z (GMT). No. of bitstreams: 2 Sudeep_Gowrishankar.pdf: 6197262 bytes, checksum: 5282a34306a3acd618aebe541409055a (MD5) license.txt: 4069 bytes, checksum: 39f15960c2329eabf59e1668921d5002 (MD5)"]},{"key":"dc:title","label":"Title","values":["Monitoring and control of extreme congestion events in road traffic networks"]}]}],"canonical_facts":{"dc:contributor":["Dullerud, Geir E."],"dc:creator":["Gowrishankar, Sudeep"],"dc:date":["2014-01-16T17:57:02Z","2013-12"],"dc:description":["This thesis addresses the problem of traffic management during extreme traffic congestion events. In particular, it describes the development of a new traffic sensing technology called TrafficTurk that is capable of being deployed through multiple temporary sensors quickly over an entire road network. The development of the technology is motivated by the enormous cost to society that is associated with extreme traffic congestion caused by pre-planned events such as sporting and cultural events, extreme weather and natural disasters. The eventual goal of the technology is to provide a comprehensive system for traffic management of extreme congestion events through real-time data collection and processing. Therefore, detailed explanations of data processing algorithms that are required to enable further development of the system are also provided. Specifically, a method to estimate traffic controller strategies at intersections in a road network using inverse optimal control and a method to normalize traffic data collected using TrafficTurk are explained in detail. Additionally, practical implementation knowledge gained thus far through extensive field testing of the system is also discussed.","Item withdrawn by Laura Spradlin (lspradl2@illinois.edu) on 2013-12-11T16:54:36Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Gowrishankar_Sudeep.pdf: 6196979 bytes, checksum: b5dffb0f8e899746b4a27d81ec06b8fd (MD5)","Made available in DSpace on 2014-01-16T17:57:02Z (GMT). No. of bitstreams: 2 Sudeep_Gowrishankar.pdf: 6197262 bytes, checksum: 5282a34306a3acd618aebe541409055a (MD5) license.txt: 4069 bytes, checksum: 39f15960c2329eabf59e1668921d5002 (MD5)"],"dc:identifier":["http://hdl.handle.net/2142/46638"],"dc:language":["en"],"dc:rights":["Copyright 2013 Sudeep Gowrishankar"],"dc:subject":["Inverse Optimal Control","TrafficTurk","Traffic","Monitoring","Control","Extreme","Congestion","Events"],"dc:title":["Monitoring and control of extreme congestion events in road traffic networks"],"dc:type":["text"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:36Z"}