This thesis presents Mantis, a system designed to evaluate possible driving routes and recommend the optimal route based on current and predicted travel conditions. The system uses the Bing Maps REST service to obtain a set of routes. Traffic data from the California Department of Transportation’s Performance Measurement System (PeMS) is then used to estimate travel times for these routes. In addition to simple travel time estimation based on instantaneous traffic conditions, Mantis can use historic data to predict traffic speeds at future times. This allows Mantis to more effectively account for regularly repeating traffic patterns such as rush hour, increasing the accuracy of its travel time estimates. Mantis is also capable of monitoring traffic incidents reported by the California Highway Patrol and identifying incidents that will be encountered along a route’s path.
","abstract_html":"<p>This thesis presents Mantis, a system designed to evaluate possible driving routes and recommend the optimal route based on current and predicted travel conditions. The system uses the Bing Maps REST service to obtain a set of routes. Traffic data from the California Department of Transportation’s Performance Measurement System (PeMS) is then used to estimate travel times for these routes. In addition to simple travel time estimation based on instantaneous traffic conditions, Mantis can use historic data to predict traffic speeds at future times. This allows Mantis to more effectively account for regularly repeating traffic patterns such as rush hour, increasing the accuracy of its travel time estimates. Mantis is also capable of monitoring traffic incidents reported by the California Highway Patrol and identifying incidents that will be encountered along a route’s path.</p>","abstract_has_math":false,"creators":["Hoover, Christopher"],"institution":null,"degree_name":"MS in Computer Science","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Franz Kurfess"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2013,"date_issued":"2013-06-01T07:00:00Z","date_published":"2013-06-01T07:00:00Z","updated_at":"2026-07-24T01:31:17Z","subjects":["Directions","Traffic","Prediction","Driving","Routes","Other Computer Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2013.112"],"render_values":[{"text":"10.15368/theses.2013.112","href":"https://doi.org/10.15368/theses.2013.112","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/943","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Franz Kurfess"]},{"key":"dc:creator","label":"Author","values":["Hoover, Christopher"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2013-06-14T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Computer Science"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Directions","Traffic","Prediction","Driving","Routes","Other Computer Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/943","10.15368/theses.2013.112"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis presents Mantis, a system designed to evaluate possible driving routes and recommend the optimal route based on current and predicted travel conditions. The system uses the Bing Maps REST service to obtain a set of routes. Traffic data from the California Department of Transportation’s Performance Measurement System (PeMS) is then used to estimate travel times for these routes. In addition to simple travel time estimation based on instantaneous traffic conditions, Mantis can use historic data to predict traffic speeds at future times. This allows Mantis to more effectively account for regularly repeating traffic patterns such as rush hour, increasing the accuracy of its travel time estimates. Mantis is also capable of monitoring traffic incidents reported by the California Highway Patrol and identifying incidents that will be encountered along a route’s path.
"]},{"key":"dc:title","label":"Title","values":["Mantis: A Predictive Driving Directions Recommendation System"]}]}],"canonical_facts":{"dc:contributor":["Franz Kurfess"],"dc:creator":["Hoover, Christopher"],"dc:date.available":["2013-06-14T07:00:00Z"],"dc:description.abstract":["This thesis presents Mantis, a system designed to evaluate possible driving routes and recommend the optimal route based on current and predicted travel conditions. The system uses the Bing Maps REST service to obtain a set of routes. Traffic data from the California Department of Transportation’s Performance Measurement System (PeMS) is then used to estimate travel times for these routes. In addition to simple travel time estimation based on instantaneous traffic conditions, Mantis can use historic data to predict traffic speeds at future times. This allows Mantis to more effectively account for regularly repeating traffic patterns such as rush hour, increasing the accuracy of its travel time estimates. Mantis is also capable of monitoring traffic incidents reported by the California Highway Patrol and identifying incidents that will be encountered along a route’s path.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/943","10.15368/theses.2013.112"],"dc:subject":["Directions","Traffic","Prediction","Driving","Routes","Other Computer Sciences"],"dc:title":["Mantis: A Predictive Driving Directions Recommendation System"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["MS in Computer Science"]},"updated_at":"2026-07-24T01:31:17Z"}