{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/83188"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/83188","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Dynamic Signal Coordination Models for a Network With Oversaturated Intersections","abstract":"This research presents the development of a dynamic signal coordination model (DSCM) for a network (one-way and two-way streets) with oversaturated intersections. The model is formulated as a discrete optimization problem with the objective to maximize the number of vehicles released by the network. The disutility function is introduced to account for queue accumulation along coordinated arterials. None of the existing signal coordination algorithms can effectively deal with a network of oversaturated intersections. The proposed model contributes to this gap. A variety of queue management strategies (QMS), such as gridlock avoidance, local and system congestion releases, and a congestion release with maximum priority on certain movements, are developed and applied into a network with oversaturated intersections. Different network topologies are considered, i.e., open and closed loop coordinated arterials. The problem is solved by Genetic Algorithms (GA). Two types of GA (SGA and BOA) were used, which differ in terms of their procedures to generate a new set of candidate solutions. SGA uses standard genetic operators, such as selection and crossover, to generate a new set of solutions, while BOA generates new candidate solutions using an estimate of the joint distribution of current promising solutions. To reduce computation time for solving DSCM, GA is executed in parallel machines. A master-slave GA technique is used in which one allows a single processor (master) to take care all genetic operation and assigns a number of processors (slaves) to evaluate GA's fitness functions. The microscopic simulation model, CORSIM, is used to validate the proposed signal coordination model. The results confirm that signal control and strategies developed within the framework of DSCM can be implemented, and the traffic volume and relationship of variables developed for DSCM are realistic to describe operational performance of a real signal system. DSCM is implemented for a different set of hypothetical network topologies. The implementation is mainly theoretical, but experimental evidence is included to illustrate the accuracy and appropriateness of DSCM. The results of this investigation enable traffic engineers to design signal coordination with a variety of coordinated arterial configurations and different queue management strategies for networks with oversaturated intersections.","abstract_html":"This research presents the development of a dynamic signal coordination model (DSCM) for a network (one-way and two-way streets) with oversaturated intersections. The model is formulated as a discrete optimization problem with the objective to maximize the number of vehicles released by the network. The disutility function is introduced to account for queue accumulation along coordinated arterials. None of the existing signal coordination algorithms can effectively deal with a network of oversaturated intersections. The proposed model contributes to this gap. A variety of queue management strategies (QMS), such as gridlock avoidance, local and system congestion releases, and a congestion release with maximum priority on certain movements, are developed and applied into a network with oversaturated intersections. Different network topologies are considered, i.e., open and closed loop coordinated arterials. The problem is solved by Genetic Algorithms (GA). Two types of GA (SGA and BOA) were used, which differ in terms of their procedures to generate a new set of candidate solutions. SGA uses standard genetic operators, such as selection and crossover, to generate a new set of solutions, while BOA generates new candidate solutions using an estimate of the joint distribution of current promising solutions. To reduce computation time for solving DSCM, GA is executed in parallel machines. A master-slave GA technique is used in which one allows a single processor (master) to take care all genetic operation and assigns a number of processors (slaves) to evaluate GA's fitness functions. The microscopic simulation model, CORSIM, is used to validate the proposed signal coordination model. The results confirm that signal control and strategies developed within the framework of DSCM can be implemented, and the traffic volume and relationship of variables developed for DSCM are realistic to describe operational performance of a real signal system. DSCM is implemented for a different set of hypothetical network topologies. The implementation is mainly theoretical, but experimental evidence is included to illustrate the accuracy and appropriateness of DSCM. The results of this investigation enable traffic engineers to design signal coordination with a variety of coordinated arterial configurations and different queue management strategies for networks with oversaturated intersections.","abstract_has_math":false,"creators":["Girianna, Montty"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Civil and Environmental Engineering","degree_department":null,"school":null,"contributors":["Benekohal, Rahim F."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-25T21:03:26Z","date_published":"2015-09-25T21:03:26Z","updated_at":"2026-07-22T22:26:20Z","subjects":["Engineering, Civil"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3069997"],"render_values":[{"text":"(MiAaPQ)AAI3069997","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/83188","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Benekohal, Rahim F."]},{"key":"dc:creator","label":"Author","values":["Girianna, Montty"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-25T21:03:26Z","10000-01-01","2002"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil and Environmental Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"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":["Engineering, Civil"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/83188","(MiAaPQ)AAI3069997"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This research presents the development of a dynamic signal coordination model (DSCM) for a network (one-way and two-way streets) with oversaturated intersections. The model is formulated as a discrete optimization problem with the objective to maximize the number of vehicles released by the network. The disutility function is introduced to account for queue accumulation along coordinated arterials. None of the existing signal coordination algorithms can effectively deal with a network of oversaturated intersections. The proposed model contributes to this gap. A variety of queue management strategies (QMS), such as gridlock avoidance, local and system congestion releases, and a congestion release with maximum priority on certain movements, are developed and applied into a network with oversaturated intersections. Different network topologies are considered, i.e., open and closed loop coordinated arterials. The problem is solved by Genetic Algorithms (GA). Two types of GA (SGA and BOA) were used, which differ in terms of their procedures to generate a new set of candidate solutions. SGA uses standard genetic operators, such as selection and crossover, to generate a new set of solutions, while BOA generates new candidate solutions using an estimate of the joint distribution of current promising solutions. To reduce computation time for solving DSCM, GA is executed in parallel machines. A master-slave GA technique is used in which one allows a single processor (master) to take care all genetic operation and assigns a number of processors (slaves) to evaluate GA's fitness functions. The microscopic simulation model, CORSIM, is used to validate the proposed signal coordination model. The results confirm that signal control and strategies developed within the framework of DSCM can be implemented, and the traffic volume and relationship of variables developed for DSCM are realistic to describe operational performance of a real signal system. DSCM is implemented for a different set of hypothetical network topologies. The implementation is mainly theoretical, but experimental evidence is included to illustrate the accuracy and appropriateness of DSCM. The results of this investigation enable traffic engineers to design signal coordination with a variety of coordinated arterial configurations and different queue management strategies for networks with oversaturated intersections.","Made available in DSpace on 2015-09-25T21:03:26Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3069997.pdf: 11224406 bytes, checksum: fc3f6e968b78c956975f6132fc0de80d (MD5) Previous issue date: 2002","Embargo set by: Seth Robbins for item 84469 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","256 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2002."]},{"key":"dc:title","label":"Title","values":["Dynamic Signal Coordination Models for a Network With Oversaturated Intersections"]}]}],"canonical_facts":{"dc:contributor":["Benekohal, Rahim F."],"dc:creator":["Girianna, Montty"],"dc:date":["2015-09-25T21:03:26Z","10000-01-01","2002"],"dc:description":["This research presents the development of a dynamic signal coordination model (DSCM) for a network (one-way and two-way streets) with oversaturated intersections. The model is formulated as a discrete optimization problem with the objective to maximize the number of vehicles released by the network. The disutility function is introduced to account for queue accumulation along coordinated arterials. None of the existing signal coordination algorithms can effectively deal with a network of oversaturated intersections. The proposed model contributes to this gap. A variety of queue management strategies (QMS), such as gridlock avoidance, local and system congestion releases, and a congestion release with maximum priority on certain movements, are developed and applied into a network with oversaturated intersections. Different network topologies are considered, i.e., open and closed loop coordinated arterials. The problem is solved by Genetic Algorithms (GA). Two types of GA (SGA and BOA) were used, which differ in terms of their procedures to generate a new set of candidate solutions. SGA uses standard genetic operators, such as selection and crossover, to generate a new set of solutions, while BOA generates new candidate solutions using an estimate of the joint distribution of current promising solutions. To reduce computation time for solving DSCM, GA is executed in parallel machines. A master-slave GA technique is used in which one allows a single processor (master) to take care all genetic operation and assigns a number of processors (slaves) to evaluate GA's fitness functions. The microscopic simulation model, CORSIM, is used to validate the proposed signal coordination model. The results confirm that signal control and strategies developed within the framework of DSCM can be implemented, and the traffic volume and relationship of variables developed for DSCM are realistic to describe operational performance of a real signal system. DSCM is implemented for a different set of hypothetical network topologies. The implementation is mainly theoretical, but experimental evidence is included to illustrate the accuracy and appropriateness of DSCM. The results of this investigation enable traffic engineers to design signal coordination with a variety of coordinated arterial configurations and different queue management strategies for networks with oversaturated intersections.","Made available in DSpace on 2015-09-25T21:03:26Z (GMT). No. of bitstreams: 2 license.txt: 4848 bytes, checksum: 96035ab3f5e1c23cc7138a224ce498bd (MD5) 3069997.pdf: 11224406 bytes, checksum: fc3f6e968b78c956975f6132fc0de80d (MD5) Previous issue date: 2002","Embargo set by: Seth Robbins for item 84469 Lift date: Forever Reason: Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","Restricted to the U of I community idenfinitely during batch ingest of legacy ETDs","U of I Only","256 p.","Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2002."],"dc:identifier":["http://hdl.handle.net/2142/83188","(MiAaPQ)AAI3069997"],"dc:language":["eng"],"dc:subject":["Engineering, Civil"],"dc:title":["Dynamic Signal Coordination Models for a Network With Oversaturated Intersections"],"dc:type":["text"],"thesis:degree_discipline":["Civil and Environmental Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:26:20Z"}