{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/43072"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/43072","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Traffic prediction and navigation using historical and current information","abstract":"We developed a traffic prediction and navigation system that deals with uncertainty of road traffic conditions by stochastic modeling of road networks. Our system consists of a data collecting system, a data management system, and a path planning system. First, the data collecting system gathers real-time travel time data using a mobile sensor network system, CarTel. GPS sensor units having wireless connectivity were deployed on taxis running around the Boston area, and report their position and time information to the networked database system. Second, the raw GPS data collected from this CarTel system is processed to generate a database storing the statistical information of road travel time. We organize a large amount of data in a form in which they can be accessed efficiently and can capture important aspects of road traffic conditions. Third, we developed efficient stochastic shortest path algorithms that find best paths depending on drivers' goals. We evaluate our algorithms using both simulations and real-world drives. Finally, we implemented a path planning system using historical and current information organized by our data management system. Our system provides a Web-based interface that is publicly usable. The interface provides traffic information, including optimal paths and visualized traffic conditions. Our system also offers analysis tools of users' own driving routes with user track-log uploading interface. We evaluate the system using taxi trajectories and human driving experiments.","abstract_html":"We developed a traffic prediction and navigation system that deals with uncertainty of road traffic conditions by stochastic modeling of road networks. Our system consists of a data collecting system, a data management system, and a path planning system. First, the data collecting system gathers real-time travel time data using a mobile sensor network system, CarTel. GPS sensor units having wireless connectivity were deployed on taxis running around the Boston area, and report their position and time information to the networked database system. Second, the raw GPS data collected from this CarTel system is processed to generate a database storing the statistical information of road travel time. We organize a large amount of data in a form in which they can be accessed efficiently and can capture important aspects of road traffic conditions. Third, we developed efficient stochastic shortest path algorithms that find best paths depending on drivers' goals. We evaluate our algorithms using both simulations and real-world drives. Finally, we implemented a path planning system using historical and current information organized by our data management system. Our system provides a Web-based interface that is publicly usable. The interface provides traffic information, including optimal paths and visualized traffic conditions. Our system also offers analysis tools of users' own driving routes with user track-log uploading interface. We evaluate the system using taxi trajectories and human driving experiments.","abstract_has_math":false,"creators":["Lim, Sejoon"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Daniela Rus."],"committee_chairs":[],"committee_members":[],"year":2008,"date_issued":"2008","date_published":"2008","updated_at":"2026-07-22T22:22:26Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/43072","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Daniela Rus."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."]},{"key":"dc:creator","label":"Author","values":["Lim, Sejoon"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2008-11-07T18:58:58Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2008-11-07T18:58:58Z"]},{"key":"dc:date.issued","label":"Date","values":["2008"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical Engineering and Computer Science."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/43072"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.","Includes bibliographical references (p. 101-104)."]},{"key":"dc:description.abstract","label":"Abstract","values":["We developed a traffic prediction and navigation system that deals with uncertainty of road traffic conditions by stochastic modeling of road networks. Our system consists of a data collecting system, a data management system, and a path planning system. First, the data collecting system gathers real-time travel time data using a mobile sensor network system, CarTel. GPS sensor units having wireless connectivity were deployed on taxis running around the Boston area, and report their position and time information to the networked database system. Second, the raw GPS data collected from this CarTel system is processed to generate a database storing the statistical information of road travel time. We organize a large amount of data in a form in which they can be accessed efficiently and can capture important aspects of road traffic conditions. Third, we developed efficient stochastic shortest path algorithms that find best paths depending on drivers' goals. We evaluate our algorithms using both simulations and real-world drives. Finally, we implemented a path planning system using historical and current information organized by our data management system. Our system provides a Web-based interface that is publicly usable. The interface provides traffic information, including optimal paths and visualized traffic conditions. Our system also offers analysis tools of users' own driving routes with user track-log uploading interface. We evaluate the system using taxi trajectories and human driving experiments."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Traffic prediction and navigation using historical and current information"]}]}],"canonical_facts":{"dc:contributor.advisor":["Daniela Rus."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science."],"dc:creator":["Lim, Sejoon"],"dc:date.accessioned":["2008-11-07T18:58:58Z"],"dc:date.available":["2008-11-07T18:58:58Z"],"dc:date.issued":["2008"],"dc:description":["Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.","Includes bibliographical references (p. 101-104)."],"dc:description.abstract":["We developed a traffic prediction and navigation system that deals with uncertainty of road traffic conditions by stochastic modeling of road networks. Our system consists of a data collecting system, a data management system, and a path planning system. First, the data collecting system gathers real-time travel time data using a mobile sensor network system, CarTel. GPS sensor units having wireless connectivity were deployed on taxis running around the Boston area, and report their position and time information to the networked database system. Second, the raw GPS data collected from this CarTel system is processed to generate a database storing the statistical information of road travel time. We organize a large amount of data in a form in which they can be accessed efficiently and can capture important aspects of road traffic conditions. Third, we developed efficient stochastic shortest path algorithms that find best paths depending on drivers' goals. We evaluate our algorithms using both simulations and real-world drives. Finally, we implemented a path planning system using historical and current information organized by our data management system. Our system provides a Web-based interface that is publicly usable. The interface provides traffic information, including optimal paths and visualized traffic conditions. Our system also offers analysis tools of users' own driving routes with user track-log uploading interface. We evaluate the system using taxi trajectories and human driving experiments."],"dc:description.degree":["S.M."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/43072"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Traffic prediction and navigation using historical and current information"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:22:26Z"}