Intermodal trip composition: the MyWay meta-planning approach

Transcription

1 Intermodal trip composition: the MyWay meta-planning approach MyWay Final Workshop Barcelona Activa - 18 th February 2016 Michal Jakob Artificial Intelligence Center Czech Technical University in Prague 3/1/ Page

2 We ve built an intermodal trip planner that requires (almost) no data 3/1/ Page

3 Tram Bus Train Metro (Shared) Bike Electric Scooter Private car Car sharing Bike Taxi Ride sharing Ferry 3/1/ Page

4 Intermodal Plans Combining different (possibly both public and private) means of transport within one trip Car Train Bike sharing 3/1/ Page

5 Intermodal Planners: State of Practice Algorithms for intermodal trip planning have been recently introduced e.g.: Delling, Daniel, et al. "Computing multimodal journeys in practice." Experimental Algorithms. Springer Berlin Heidelberg, However, there are hardly any intermodal trip planners out there Why? 3/1/ Page

6 STANDARD APPROACH trip plan query Detailed information about all modes walk car PT bike bike sharing planning graph Route planning algorithm suggested plans 3/1/ Page

7 METAPLANNING APPROACH transport metagraph trip plan query Car route planner API PT trip planner API Metagraph construction Metaplanning Bike route planner API BS route planner API single-modal subplanners approximate but intermodal A novel way of planner integration metaplans Metaplan Refinement suggested plans 3/1/ Page

8 Metaplan Trip Metaplanning ~6 min ~25 min transfer: ~5 min ~8 min ~4 min Walk Train Shared bike Walk Viladecans Passeig de Gracia 3/1/ Page

9 Metaplan Refinement Example Refined detailed plan Metaplan ~6 min ~25 min transfer: ~5 min ~8 min ~4 min Walk Train Shared bike Walk Viladecans Passeig de Gracia metaplan refinement Public transport subplanner Bike sharing subplanner TripPlan(id=1, time=2520, legs=5, departure= t17:28:00+01:00) TripLeg(id=1, transportmode="walk", steps=2, duration=480, distance=289) TripStep(id=0; loc= , ) TripStep(id=1; name=viladecans (Estació de Tren) ; loc= , ; timefrompreviousstep=480) TripLeg(id=2, transportmode="train", steps=5, duration=1380, distance=14734) TripStep(id=0; name=viladecans (Estació de Tren) ; type=trainstation; loc= , )... TripStep(id=4; name=passeig de Gracia ; type=trainstation; loc= , ; timefrompreviousstep=360) TripLeg(id=3, transportmode="walk", steps=5, duration=136, distance=188) TripStep(id=0; loc= , ) TripStep(id=1; loc= , ; timefrompreviousstep=10)... TripLeg(id=4, transportmode="shared_bike", steps=31, duration=403, distance=1890) TripStep(id=0; loc= , ) TripStep(id=1; loc= , ; timefrompreviousstep=6)... TripStep(id=30; loc= , ; timefrompreviousstep=6) TripLeg(id=5, transportmode="walk", steps=5, duration=121, distance=168) 3/1/ Page

10 Metagraph Construction An algorithm that automatically builds an approximate intermodal model of the transport systems by querying single-modal trip planners. 3/1/ Page

11 Metagraph: Model based on Generalized time-dependent graph representation* Road junction Bike sharing station PT stop Metanode Cell diameter: km Metaedges Real routes Voronoi cell Metaedges weights: travel time, cost, emissions, physical effort * J. Hrncir and M. Jakob: Generalised Time-Dependent Graphs for Fully 3/1/2016 Multimodal Journey Planning. In IEEE Intelligent Transportation Systems 11 Page Conference (ITSC)

12 Metagraph: Construction smart subplanner querying Voronoi cells with adaptive density Local properties and mode location Nodes Subplanner Road Planner network APIs APIs Fixed mode stops Service availability zones Edges creation and time precomputation Metagraph Edges API calls hours of computation Nykl, J. - Hrnčíř, J. - Jakob, M. Achieving Full Plan Multimodality by Integrating Multiple Incomplete Journey Planners 3/1/2016 In: Proceedings of the 18th IEEE International Conference on Intelligent Transportation Systems. 12 Page 2015, p

13 Catalonia fragment Metagraph Example Metagraph sizes # metanodes # metaedges Catalonia Berlin Trikala ~1/100 nodes of a fully detailed graph 3/1/ Page

14 Metasearch and Refinement trip plan query Metaplanning Intermodal trip planning algorithm Runtime: hundreds miliseconds 20+ metaplans Metaplan Refinement Select most diverse metaplans to refine Invoke subplanners Runtime: seconds up to 8 detailed plans 3/1/ Page

15 Benefit 1: Fully intermodal plans faster than public transport-only plans 1 2 Not rule-based discovered by the metaplanning algorithm /1/ Page

16 Benefit 2: Fully intermodal plans where no public transport-only plans exist 3/1/ Page

17 Statistical Quality Evaluation Increased)Usage)of)Public)Transport Trikala Berlin Catalonia 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Intermodal+Journey+Plans+Group+Split+53+Criteria Only:Intermodal:PT Other Trikala Berlin Catalonia 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Dominating Pareto Dominated 3/1/ Page

18 Computational Statistics Metasearch (ms) Refinement (ms) Total (ms) Catalonia Trikala Berlin (9000) (10000) * for constrained single-criteria shortest path metasearch algorithm Metagraph size (#nodes + #edges) One trip planning requests results, on average, in the following number of subplanner requests (Cat): 1.45 (car), 2.81 (PT), 1.47 (bike) A number of speed-up techniques can be applied. 3/1/ Page

19 Pros and Cons Full intermodality Rapid deployment High customizability (facilitates mobility policy injection) Flexibility: new transport modes / services easy to add Service-oriented approach: reuse of existing planning capabilities (incl. their data maintenance processes) Disadvantage: slightly slower response times 3/1/ Page

20 Summary A novel approach to intermodal planning Substitutes access to data with access to planning APIs Employs AI instead of fixed rules for intermodal integration Successfully tested in three diverse living labs sites A good basis for mobility-as-a-service planning solutions Ready for deployment in new locations/areas 3/1/ Page

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