Towards traffic Towards traffic-aware routing using o a ds t a o a ds t a c c a a e out aware routing using a a e out g us g us g g GPS vehicle trajectories GPS vehicle trajectories Carola Wenk Carola Wenk University of Texas at San Antonio University of Texas at San Antonio carola@cs utsa edu carola@cs utsa edu carola@cs.utsa.edu carola@cs.utsa.edu Collaboration with: • Dieter Pfoser, Computer Technology Institute, Athens, Greece • Peter Wagner, German Aerospace Center, Berlin, Germany
Outline Outline Problem Description Problem Description Problem Description Problem Description 1 1. 1. Enable in Enable in- -car navigation systems to find the best routes using car navigation systems to find the best routes using current traffic situation current traffic situation Travel Times and GPS curves Travel Times and GPS curves 2. 2. Map Map- -Matching Matching 3. 3. Incremental map- Incremental map I I t l t l -matching matching t hi t hi 1. 1. Global map Global map- -matching: Fr matching: Fréchet distance échet distance 2. 2. Global map Global map- -matching: Weak Fréchet distance matching: Weak Fréchet distance 3. 3. Routing System Setup and Future Work Routing System Setup and Future Work 4. 4. 2
In In- -Car Navigation Systems Car Navigation Systems � Navigation systems perform the Navigation systems perform the routing task routing task : Find a shortest route from A to B Find a shortest route from A to B Find a shortest route from A to B Find a shortest route from A to B � What does “shortest” mean? What does “shortest” mean? – Shortest length? No. Shortest length? No. – Shortest travel time ! Shortest travel time ! 3
Model for Routing Task Model for Routing Task � Model the street network as a graph: Model the street network as a graph: – Vertices: Intersections of roads Vertices: Intersections of roads Vertices: Intersections of roads Vertices: Intersections of roads – Edge: A road segment between two intersections Edge: A road segment between two intersections 4
Model for Routing Task Model for Routing Task � Routing Task: Find shortest path in the graph from Routing Task: Find shortest path in the graph from A to to B A B 5
How to Compute Shortest Paths? How to Compute Shortest Paths? � Dijkstra’s shortest path algorithm: Dijkstra’s shortest path algorithm: – Given A , B , and a graph with non-negative edge weights Given A B and a graph with non-negative edge weights – Among all paths from A to B in the graph, compute such a path whose total weight (= sum of edge weights) is minimized minimized � What are our edge weights? What are our edge weights? – Travel times – The travel time on a (directed) edge from c to d is the time it takes to travel from c to d 6
Outline Outline Problem Description Problem Description Problem Description Problem Description 1 1. 1. Enable in Enable in- -car navigation systems to find the best routes using car navigation systems to find the best routes using current traffic situation current traffic situation Travel Times and GPS curves Travel Times and GPS curves 2. 2. Map Map- -Matching Matching 3. 3. Incremental map- Incremental map I I t l t l -matching matching t hi t hi 1. 1. Global map Global map- -matching: Fr matching: Fréchet distance échet distance 2. 2. Global map Global map- -matching: Weak Fréchet distance matching: Weak Fréchet distance 3. 3. Routing System Setup and Future Work Routing System Setup and Future Work 4. 4. 7
Travel Time of an Edge Travel Time of an Edge � How do we know the travel time on an edge / road How do we know the travel time on an edge / road segment? segment? segment? segment? – Usually, navigation systems derive travel times from speed limits. – A very smart system might take a small number of congestion A very smart system might take a small number of congestion points into account – Usually variation of travel times during rush hour is not taken into account into account � New approach: Maintain a database of current travel times New approach: Maintain a database of current travel times – Use GPS trajectory data from vehicle fleets (delivery trucks, Use GPS trajectory data from vehicle fleets (delivery trucks, taxis, etc.) taxis, etc.) i i ) ) � Challenge: How do we build this database? Challenge: How do we build this database? 8
GPS Floating Car Data GPS Floating Car Data � Floating car data (FCD) Floating car data (FCD) A sequence (trajectory) of data points each consisting of: A sequence (trajectory) of data points each consisting of: A sequence (trajectory) of data points, each consisting of: A sequence (trajectory) of data points, each consisting of: – Basic vehicle telemetry, e.g., speed, direction, ABS use Basic vehicle telemetry, e.g., speed, direction, ABS use – The The position of the vehicle position of the vehicle ( � tracking data) obtained by GPS tracking data) obtained by GPS tracking tracking tracking tracking – A time stamp A time stamp � Traffic assessment Traffic assessment – Data from one vehicle as a sample to assess the overall Data from one vehicle as a sample to assess the overall traffic condition traffic condition – – cork swimming in the river cork swimming in the river Large amounts of tracking data (e.g., taxis, public transport, ( ( (e.g., taxis, public transport, g , g , , p , p p p , , – Large amounts of tracking data g g g g utility vehicles, private vehicles) utility vehicles, private vehicles) � Accurate picture of the traffic condition Accurate picture of the traffic condition – Tracking data needs to be related to the road network Tracking data needs to be related to the road network g � Map matching Map matching Time stamps from FCD yield from FCD yield travel times travel times for road segments for road segments – Time stamps 9
GPS Vehicle Tracking Data GPS Vehicle Tracking Data Problems: Problems: 1) Measurement error: 1) Measurement error: GPS points do not exactly lie on the roadmap roadmap 2) Sampling error: Map matching: GPS curve is a by- Find a curve in the graph that corresponds g p p product, and usually d t d ll to the GPS curve sampled every 30s ⇒ The GPS curve does not lie on the roadmap roadmap Roadmap of Athens, Corresponding path in GPS curve 10 Greece the roadmap
Outline Outline Problem Description Problem Description Problem Description Problem Description 1 1. 1. Enable in Enable in- -car navigation systems to find the best routes using car navigation systems to find the best routes using current traffic situation current traffic situation Travel Times and GPS curves Travel Times and GPS curves 2. 2. Map Map- -Matching Matching 3. 3. Incremental map- Incremental map I I t l t l -matching matching t hi t hi 1. 1. Global map Global map- -matching: Fr matching: Fréchet distance échet distance 2. 2. Global map Global map- -matching: Weak Fréchet distance matching: Weak Fréchet distance 3. 3. Routing System Setup and Future Work Routing System Setup and Future Work 4. 4. 11
Available Map Available Map- -Matching Algorithms Matching Algorithms � Incremental map Incremental map- -matching matching – Follow greedy strategy of incrementally extending solution Follo Follo Follow greedy strategy of incrementally extending solution greed strateg of incrementall e tending sol tion greed strateg of incrementall e tending sol tion from an already matched edge, e.g., [BPSW05] from an already matched edge, e.g., [BPSW05] – No quality guarantee No quality guarantee – Classical approach Classical approach � Global map Global map- -matching matching – Find among all possible trajectories in the road network the Find among all possible trajectories in the road network the g g p p j j one that is most similar to the vehicle trajectory one that is most similar to the vehicle trajectory Distance measure assesses similarity = quality guarantee = quality guarantee – Distance measure assesses similarity – Fr Fréchet distance (strong, weak) [BPSW05, WSP06] Fréchet distance (strong weak) [BPSW05 WSP06] Fr chet distance (strong weak) [BPSW05 WSP06] chet distance (strong, weak) [BPSW05, WSP06] BPSW05: ``On Map-Matching Vehicle Tracking Data'' (S. Brakatsoulas, D. Pfoser, R. Salas, and C. Wenk), Proc. 31st Conference on Very Large Data Bases (VLDB) : 853-864, 2005, Trondheim, Norway. WSP06: ``Addressing the Need for Map Matching Speed: Localizing Global Curve Matching Algorithms''(C Wenk Addressing the Need for Map-Matching Speed: Localizing Global Curve-Matching Algorithms (C. Wenk WSP06: and R. Salas and D. Pfoser), Proc. 18th International Conference on Scientific and Statistical Database Management (SSDBM) : 379-388, 2006, Vienna, Austria. 12
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