Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA SEA - PowerPoint PPT Presentation

Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA SEA · June 17, 2020 Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf I NSTITUTE OF T HEORETICAL I NFORMATICS · A LGORITHMICS G ROUP www.kit.edu KIT – The Research University in the Helmholtz Association

Multi-Modal Route Planning Goals: Journey planning for public transit Find optimal journeys Consider modes of transportation: All timetable-based modes (trains, trams, buses, ...) 1 Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA Institute of Theoretical Informatics Algorithmics Group Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf

Multi-Modal Route Planning Goals: Journey planning for public transit Find optimal journeys Consider modes of transportation: All timetable-based modes (trains, trams, buses, ...) Walking (from, to, and between stops) 1 Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA Institute of Theoretical Informatics Algorithmics Group Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf

Multi-Modal Route Planning Goals: Journey planning for public transit Find optimal journeys Consider modes of transportation: All timetable-based modes (trains, trams, buses, ...) Walking (from, to, and between stops) Bike sharing (or other rental based services) 1 Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA Institute of Theoretical Informatics Algorithmics Group Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf

Multi-Modal Route Planning Goals: Journey planning for public transit Find optimal journeys Consider modes of transportation: All timetable-based modes (trains, trams, buses, ...) Walking (from, to, and between stops) Bike sharing (or other rental based services) No limits on any of the transportation modes 1 Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA Institute of Theoretical Informatics Algorithmics Group Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf

Problem Statement Given: Public transit network (timetable) 2 Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA Institute of Theoretical Informatics Algorithmics Group Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf

Problem Statement Given: Public transit network (timetable) Stops (bus stops, stations) 2 Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA Institute of Theoretical Informatics Algorithmics Group Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf

Problem Statement Given: Public transit network (timetable) Stops (bus stops, stations) Routes (bus lines, train lines) 2 Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA Institute of Theoretical Informatics Algorithmics Group Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf

Problem Statement Given: Public transit network (timetable) Stops (bus stops, stations) Routes (bus lines, train lines) Trips (schedule of a vehicle) 11:10 11:07 11:08 11:05 2 Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA Institute of Theoretical Informatics Algorithmics Group Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf

Problem Statement Given: Public transit network (timetable) Stops (bus stops, stations) Routes (bus lines, train lines) Trips (schedule of a vehicle) Transfer graph (non-schedule based) 2 Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA Institute of Theoretical Informatics Algorithmics Group Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf

Problem Statement Given: Public transit network (timetable) Stops (bus stops, stations) Routes (bus lines, train lines) Trips (schedule of a vehicle) Transfer graph (non-schedule based) Vertices (crossings, places) 2 Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA Institute of Theoretical Informatics Algorithmics Group Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf

Problem Statement Given: Public transit network (timetable) Stops (bus stops, stations) Routes (bus lines, train lines) Trips (schedule of a vehicle) Transfer graph (non-schedule based) Vertices (crossings, places) Edges (roads, paths) 2 Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA Institute of Theoretical Informatics Algorithmics Group Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf

Problem Statement Given: Public transit network (timetable) Stops (bus stops, stations) Routes (bus lines, train lines) Trips (schedule of a vehicle) Transfer graph (non-schedule based) Vertices (crossings, places) Edges (roads, paths) Bike sharing stations (per operator) 2 Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA Institute of Theoretical Informatics Algorithmics Group Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf

Problem Statement Given: Public transit network (timetable) Stops (bus stops, stations) Routes (bus lines, train lines) Trips (schedule of a vehicle) Transfer graph (non-schedule based) Vertices (crossings, places) Edges (roads, paths) Bike sharing stations (per operator) 2 Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA Institute of Theoretical Informatics Algorithmics Group Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf

Problem Statement Given: Public transit network (timetable) Stops (bus stops, stations) Routes (bus lines, train lines) Trips (schedule of a vehicle) Transfer graph (non-schedule based) Vertices (crossings, places) Edges (roads, paths) Bike sharing stations (per operator) 2 Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA Institute of Theoretical Informatics Algorithmics Group Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf

Problem Statement s , 11:32 Given: Public transit network (timetable) Stops (bus stops, stations) Routes (bus lines, train lines) Trips (schedule of a vehicle) Transfer graph (non-schedule based) Vertices (crossings, places) t Edges (roads, paths) Bike sharing stations (per operator) Source s , target t , and a departure time 2 Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA Institute of Theoretical Informatics Algorithmics Group Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf

Problem Statement s , 11:32 Given: Public transit network (timetable) Stops (bus stops, stations) Routes (bus lines, train lines) Trips (schedule of a vehicle) Transfer graph (non-schedule based) Vertices (crossings, places) t Edges (roads, paths) Bike sharing stations (per operator) Source s , target t , and a departure time Objective: Find all Pareto-optimal journeys w.r.t. arrival time and number of trips 2 Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA Institute of Theoretical Informatics Algorithmics Group Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf

Problem Statement s , 11:32 Given: Public transit network (timetable) Stops (bus stops, stations) Routes (bus lines, train lines) Trips (schedule of a vehicle) Transfer graph (non-schedule based) Vertices (crossings, places) 11:43 t Edges (roads, paths) Bike sharing stations (per operator) Source s , target t , and a departure time Objective: Find all Pareto-optimal journeys w.r.t. arrival time and number of trips 2 Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA Institute of Theoretical Informatics Algorithmics Group Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf

Problem Statement s , 11:32 Given: Public transit network (timetable) Stops (bus stops, stations) Routes (bus lines, train lines) Trips (schedule of a vehicle) Transfer graph (non-schedule based) Vertices (crossings, places) 11:43 t Edges (roads, paths) 11:45 Bike sharing stations (per operator) Source s , target t , and a departure time Objective: Find all Pareto-optimal journeys w.r.t. arrival time and number of trips 2 Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA Institute of Theoretical Informatics Algorithmics Group Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf

Problem Statement s , 11:32 Given: Public transit network (timetable) Stops (bus stops, stations) Routes (bus lines, train lines) Trips (schedule of a vehicle) Transfer graph (non-schedule based) Vertices (crossings, places) 11:43 t Edges (roads, paths) 11:47 11:45 Bike sharing stations (per operator) Source s , target t , and a departure time Objective: Find all Pareto-optimal journeys w.r.t. arrival time and number of trips 2 Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA Institute of Theoretical Informatics Algorithmics Group Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf

Approaches Greatest Challenge: Distinguish and handle multiple bike sharing operators Labels with different rental bikes cannot be compared 3 Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA Institute of Theoretical Informatics Algorithmics Group Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf

Approaches Greatest Challenge: Distinguish and handle multiple bike sharing operators Labels with different rental bikes cannot be compared 3 Faster Multi-Modal Route Planning with Bike Sharing Using ULTRA Institute of Theoretical Informatics Algorithmics Group Jonas Sauer, Dorothea Wagner, and Tobias Z¨ undorf