{"id":{"repo_id":"texas","oai_identifier":"oai:repositories.lib.utexas.edu:2152/62648"},"canonical_url":"https://search.dev.ndltd.org/etd/texas/oai:repositories.lib.utexas.edu:2152/62648","repository":{"repo_id":"texas","name":"University of Texas","base_url":"https://repositories.lib.utexas.edu/server/oai/request"},"display":{"title":"Potential impacts of connected-autonomous vehicles on congestion and safety : a look at Austin, Texas","abstract":"Data is a central component of Connected-Autonomous Vehicle (CAV) systems: the advantages and potential challenges of both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) CAV data underlie the question of wide scale CAV implementation. This report looks at the potential congestion and safety benefits of a vehicle system highly saturated with CAVs in Austin, Texas. Traffic factors such as capacity, intersection delay, and crash rate are examined with respect to their effect on an urban corridor in Austin. The case study relies almost entirely on collected field data to be used as a comparison against potential CAV advantages. In addition to a presentation of the quantitative benefits of CAVs, an infrastructure placement scheme that maximizes data transmission efficiency is also proposed. The results find that vehicle systems can see large improvements in capacity, intersection delay, and number of crashes, and at a relatively inexpensive cost.","abstract_html":"Data is a central component of Connected-Autonomous Vehicle (CAV) systems: the advantages and potential challenges of both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) CAV data underlie the question of wide scale CAV implementation. This report looks at the potential congestion and safety benefits of a vehicle system highly saturated with CAVs in Austin, Texas. Traffic factors such as capacity, intersection delay, and crash rate are examined with respect to their effect on an urban corridor in Austin. The case study relies almost entirely on collected field data to be used as a comparison against potential CAV advantages. In addition to a presentation of the quantitative benefits of CAVs, an infrastructure placement scheme that maximizes data transmission efficiency is also proposed. The results find that vehicle systems can see large improvements in capacity, intersection delay, and number of crashes, and at a relatively inexpensive cost.","abstract_has_math":false,"creators":["Archer, Jackson Longstreet"],"institution":"The University of Texas at Austin","degree_name":"Master of Science in Community and Regional Planning","degree_level":"Masters","degree_discipline":"Community and Regional Planning","degree_department":null,"school":null,"contributors":[],"advisors":["Zhang, Ming, 1963 April 22-"],"committee_chairs":[],"committee_members":["Jiao, Junfeng"],"year":2017,"date_issued":"2017-05-02","date_published":"2017-05-02","updated_at":"2026-07-24T05:01:00Z","subjects":["CAV","V2V","V2I","V2X","Autonomous","Connected-autonomous","Vehicle","Austin, Texas","Transportation","Congestion","Safety","Travel time"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["doi:10.15781/T2J09WM46"],"render_values":[{"text":"doi:10.15781/T2J09WM46","href":"https://doi.org/10.15781/T2J09WM46","code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2152/62648","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Zhang, Ming, 1963 April 22-"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Jiao, Junfeng"]},{"key":"dc:creator","label":"Author","values":["Archer, Jackson Longstreet"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2017-11-20T14:51:23Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2017-11-20T14:51:23Z"]},{"key":"dc:date.issued","label":"Date","values":["2017-05-02"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Community and Regional Planning"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Community and Regional Planning"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["The University of Texas at Austin"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["CAV","V2V","V2I","V2X","Autonomous","Connected-autonomous","Vehicle","Austin, Texas","Transportation","Congestion","Safety","Travel time"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["doi:10.15781/T2J09WM46"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/2152/62648"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Data is a central component of Connected-Autonomous Vehicle (CAV) systems: the advantages and potential challenges of both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) CAV data underlie the question of wide scale CAV implementation. This report looks at the potential congestion and safety benefits of a vehicle system highly saturated with CAVs in Austin, Texas. Traffic factors such as capacity, intersection delay, and crash rate are examined with respect to their effect on an urban corridor in Austin. The case study relies almost entirely on collected field data to be used as a comparison against potential CAV advantages. In addition to a presentation of the quantitative benefits of CAVs, an infrastructure placement scheme that maximizes data transmission efficiency is also proposed. The results find that vehicle systems can see large improvements in capacity, intersection delay, and number of crashes, and at a relatively inexpensive cost."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Potential impacts of connected-autonomous vehicles on congestion and safety : a look at Austin, Texas"]}]}],"canonical_facts":{"dc:contributor.advisor":["Zhang, Ming, 1963 April 22-"],"dc:contributor.committeemember":["Jiao, Junfeng"],"dc:creator":["Archer, Jackson Longstreet"],"dc:date.accessioned":["2017-11-20T14:51:23Z"],"dc:date.available":["2017-11-20T14:51:23Z"],"dc:date.issued":["2017-05-02"],"dc:description.abstract":["Data is a central component of Connected-Autonomous Vehicle (CAV) systems: the advantages and potential challenges of both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) CAV data underlie the question of wide scale CAV implementation. This report looks at the potential congestion and safety benefits of a vehicle system highly saturated with CAVs in Austin, Texas. Traffic factors such as capacity, intersection delay, and crash rate are examined with respect to their effect on an urban corridor in Austin. The case study relies almost entirely on collected field data to be used as a comparison against potential CAV advantages. In addition to a presentation of the quantitative benefits of CAVs, an infrastructure placement scheme that maximizes data transmission efficiency is also proposed. The results find that vehicle systems can see large improvements in capacity, intersection delay, and number of crashes, and at a relatively inexpensive cost."],"dc:format.mimetype":["application/pdf"],"dc:identifier":["doi:10.15781/T2J09WM46"],"dc:identifier.uri":["http://hdl.handle.net/2152/62648"],"dc:language.iso":["en"],"dc:subject":["CAV","V2V","V2I","V2X","Autonomous","Connected-autonomous","Vehicle","Austin, Texas","Transportation","Congestion","Safety","Travel time"],"dc:title":["Potential impacts of connected-autonomous vehicles on congestion and safety : a look at Austin, Texas"],"dc:type":["Thesis"],"thesis:degree_discipline":["Community and Regional Planning"],"thesis:degree_level":["Masters"],"thesis:degree_name":["Master of Science in Community and Regional Planning"],"thesis:institution_name":["The University of Texas at Austin"]},"updated_at":"2026-07-24T05:01:00Z"}