{"id":{"repo_id":"texas","oai_identifier":"oai:repositories.lib.utexas.edu:2152/22293"},"canonical_url":"https://search.dev.ndltd.org/etd/texas/oai:repositories.lib.utexas.edu:2152/22293","repository":{"repo_id":"texas","name":"University of Texas","base_url":"https://repositories.lib.utexas.edu/server/oai/request"},"display":{"title":"Adaptive routing behavior with real time information under multiple travel objectives","abstract":"Real time information about traffic conditions is becoming widely available through various media, and the focus on Advanced Traveler Information Systems (ATIS) is gaining importance rapidly. In such conditions, travelers have better knowledge about the system and adapt as the system evolves dynamically during their travel. Drivers may change routes along their travel in order to optimize their own objective of travel, which can be characterized by disutility functions. The focus of this research is to study the behavior of travelers with multiple trip objectives, when provided with real time information. A web based experiment is carried out to simulate a traffic network with information provision and different travel objectives. The decision strategies of participants are analyzed and compared to the optimal policy, along with few other possible decision rules and a general model is calibrated to describe the travelers' decision strategy. This research is a step towards calibrating equilibrium models for adaptive behavior with multiple user classes.","abstract_html":"Real time information about traffic conditions is becoming widely available through various media, and the focus on Advanced Traveler Information Systems (ATIS) is gaining importance rapidly. In such conditions, travelers have better knowledge about the system and adapt as the system evolves dynamically during their travel. Drivers may change routes along their travel in order to optimize their own objective of travel, which can be characterized by disutility functions. The focus of this research is to study the behavior of travelers with multiple trip objectives, when provided with real time information. A web based experiment is carried out to simulate a traffic network with information provision and different travel objectives. The decision strategies of participants are analyzed and compared to the optimal policy, along with few other possible decision rules and a general model is calibrated to describe the travelers' decision strategy. This research is a step towards calibrating equilibrium models for adaptive behavior with multiple user classes.","abstract_has_math":false,"creators":["Venkatraman, Ravi"],"institution":"The University of Texas at Austin","degree_name":"Master of Science in Engineering","degree_level":"Masters","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Boyles, Stephen David, 1982-"],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-08","date_published":"2013-08","updated_at":"2026-07-24T05:00:58Z","subjects":["Adaptive routing","Real time information","Online shortest paths","Routing games","Driving behavior"],"languages":["en_US"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2152/22293","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Boyles, Stephen David, 1982-"]},{"key":"dc:creator","label":"Author","values":["Venkatraman, Ravi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2013-11-20T19:14:54Z"]},{"key":"dc:date.issued","label":"Date","values":["2013-08"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science in Engineering"]},{"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":["Adaptive routing","Real time information","Online shortest paths","Routing games","Driving behavior"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/2152/22293"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["text"]},{"key":"dc:description.abstract","label":"Abstract","values":["Real time information about traffic conditions is becoming widely available through various media, and the focus on Advanced Traveler Information Systems (ATIS) is gaining importance rapidly. In such conditions, travelers have better knowledge about the system and adapt as the system evolves dynamically during their travel. Drivers may change routes along their travel in order to optimize their own objective of travel, which can be characterized by disutility functions. The focus of this research is to study the behavior of travelers with multiple trip objectives, when provided with real time information. A web based experiment is carried out to simulate a traffic network with information provision and different travel objectives. The decision strategies of participants are analyzed and compared to the optimal policy, along with few other possible decision rules and a general model is calibrated to describe the travelers' decision strategy. This research is a step towards calibrating equilibrium models for adaptive behavior with multiple user classes."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Adaptive routing behavior with real time information under multiple travel objectives"]}]}],"canonical_facts":{"dc:contributor.advisor":["Boyles, Stephen David, 1982-"],"dc:creator":["Venkatraman, Ravi"],"dc:date.accessioned":["2013-11-20T19:14:54Z"],"dc:date.issued":["2013-08"],"dc:description":["text"],"dc:description.abstract":["Real time information about traffic conditions is becoming widely available through various media, and the focus on Advanced Traveler Information Systems (ATIS) is gaining importance rapidly. In such conditions, travelers have better knowledge about the system and adapt as the system evolves dynamically during their travel. Drivers may change routes along their travel in order to optimize their own objective of travel, which can be characterized by disutility functions. The focus of this research is to study the behavior of travelers with multiple trip objectives, when provided with real time information. A web based experiment is carried out to simulate a traffic network with information provision and different travel objectives. The decision strategies of participants are analyzed and compared to the optimal policy, along with few other possible decision rules and a general model is calibrated to describe the travelers' decision strategy. This research is a step towards calibrating equilibrium models for adaptive behavior with multiple user classes."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["http://hdl.handle.net/2152/22293"],"dc:language.iso":["en_US"],"dc:subject":["Adaptive routing","Real time information","Online shortest paths","Routing games","Driving behavior"],"dc:title":["Adaptive routing behavior with real time information under multiple travel objectives"],"thesis:degree_discipline":["Civil Engineering"],"thesis:degree_level":["Masters"],"thesis:degree_name":["Master of Science in Engineering"],"thesis:institution_name":["The University of Texas at Austin"]},"updated_at":"2026-07-24T05:00:58Z"}