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1 Intermodal Travel Information with Distributed Routing mdv mentz datenverarbeitung VIKING Domain 4 seminar Copenhagen May 31, 2001 www.mentzdv.de.

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Presentation on theme: "1 Intermodal Travel Information with Distributed Routing mdv mentz datenverarbeitung VIKING Domain 4 seminar Copenhagen May 31, 2001 www.mentzdv.de."— Presentation transcript:

1 1 Intermodal Travel Information with Distributed Routing mdv mentz datenverarbeitung VIKING Domain 4 seminar Copenhagen May 31, 2001

2 2 mdv Topics mentz datenverarbeitung - Introduction - Broker - basics - place identification - supported passive servers - supported routing techniques -architecture - Examples

3 3 mdv Introduction mentz datenverarbeitung Distributed multiple planning systems co-operate under the control of a so-called Broker (search controller) Intermodal all means of transport are considered public transport : regional (bus, metro, commuter train, tram,...) long-distance (high-speed train, plane, ferry,...) individual transport (footpath, taxi, car, bicycle,...)

4 4 mdv Distributed vs. Integrated Trip Planning mentz datenverarbeitung today user wants to plan long-distance trips many regional limited trip planning systems few integrated national trip planning systems (with up to 70 data sources) integrated networks are huge requires large memory / disk space real-time information cannot be integrated large integration effort each regional change in timetable leads to new data integration therefore Distributed Trip Planning

5 5 mdv Work-split in Distributed Trip Planning mentz datenverarbeitung Work-split into passive servers -regional systems for regional transport (bus, metro, commuter train, tram,... + individual traffic) -national /international systems for long-distance transport (long-distance trains,... + flight traffic + ferry boats +...) active broker (search controller) -interface to the user -distributed routing Each system does what it can the best

6 6 mdv Distributed Routing mentz datenverarbeitung Idea of distributed routing 1)Divide total network into overlapping subnetworks with common points (transition points) in overlap 2)Divide user request into partial requests on subnetworks e.g. request from A to B is divided into 1) req. from A to transitions in network X 2) req. from transitions to transitions in network Y 3) req. from transitions to B in network Z Sometimes a division into more than 3 subnetworks is needed.

7 7 mdv Broker mentz datenverarbeitung Broker as search controller and integrator is interface to user knows the passive servers has meta knowledge implements distributed routing can access the passive servers

8 8 mdv Meta Knowledge mentz datenverarbeitung Broker has mappings place to subnetwork e.g. Hannover is in the subnetwork of the Hannover Region Transport Company GVH from subnetwork to subnetwork e.g. from subnet GVH via subnet DB (Deutsche Bahn) or via subnet Flight (START/Amadeus) to subnet Copenhagen Region subnetwork to passive server e.g. subnet GVH by server IOR_209328FD23DB88A6352C...

9 9 mdv Meta Knowledge mentz datenverarbeitung Broker gets from passive server transition points (common points of different networks) e.g. transition points for Hannover Town Hall are Hannover Main Station and Hannover Airport partial trips -from origin to transition points -from transition points to transition points -from transition points to destination e.g. from Hannover Town Hall to Hannover Airport, from Hannover Airport to Copenhagen Airport, from Copenhagen Airport to Copenhagen Rådhuspladsen

10 10 mdv Place Identification user-driven (e.g. EUSpirit) User input for origin / destination: 1)region 2) place(town, village) 3) point(stop, address, point of interest) By choosing the region from a list, the passive server for place and point identification is selected. + easy to implement - additional input step (region + town + point) - choose of region difficult when many regions mentz datenverarbeitung

11 11 mdv Place Identification knowledge-based (e.g. EFA/IMTP) User input for origin / destinition: 1) place(town, village) 2) point(stop, address, point of interest) One place server knows all places of the total network. He identifies the place. Point verification is done by the passive server for this place (this subnetwork) + user-friendly - effort to integrate place data mentz datenverarbeitung

12 12 mdv Passive Servers The following passive servers are supported PT EUSpirit server DELFI server EFA server START/Amadeus (flight server) IT EFA router PTV router (soon) mentz datenverarbeitung

13 13 mdv Distributed routing The following techniques for distributed routing are implemented EUSpirit DELFI Multimodal Alternative trips can be calculated by different network sequences techniques mentz datenverarbeitung

14 14 mdv Distributed Routing A) EUSpirit-Technique Extending the long-distance trips into the regional networks Used in EU project EUSpirit (Denmark, Scania, Austria, Vienna, Emilia Romangna, Berlin) Assumptions: origin and destination region are not close few transition points + fast if assumptions true + extension into regional networks parallel -needs estimation -slow if assumptions false mentz datenverarbeitung

15 15 Origin Destination Departure at 10:00 Regional Transport Long-distance Transport mdv mentz datenverarbeitung EUSpirit-Technique : Request

16 16 Origin Destination Estimated Departures Regional Transport Long-distance Transport 10:20 10:50 mdv mentz datenverarbeitung EUSpirit-Technique : Transition Points

17 17 OriginDestination Regional Transport Long-distance Transport 14:00 15:00 14:30 14:50 10:20 10:50 m:n search mdv mentz datenverarbeitung EUSpirit-Technique : Long-distance trip search

18 18 Origin Destination Regional Transport Long-distance Transport 14:00 14:30 10:20 10:50 9:50 10:10 15:20 14:50 1:m search (backward)n:1 search (forward) mdv mentz datenverarbeitung EUSpirit-Technique : Extension into regional networks

19 19 Origin Destination Regional Transport Long-distance Transport 14:00 10:50 10:10 14:50 Merging of partial trips mdv mentz datenverarbeitung EUSpirit-Technique : Result 10:25 14:20

20 20 mdv Distributed Routing B) DELFI-Technique Distributed Dijkstra-Algorithm Used in German project DELFI (in cooperation with HaCon, HBT, IVU, TLC, et al) +no estimation needed +works not only for 3 subnetworks -no parallelism mentz datenverarbeitung

21 21 Origin Destination Departure at 10:00 Regional Transport Long-distance Transport mdv mentz datenverarbeitung DELFI-Technique : Request

22 22 Origin Destination Regional Transport Long-distance Transport mdv mentz datenverarbeitung DELFI-Technique : Transition Points

23 23 Origin Destination Regional Transport Long-distance Transport 14:00 14:30 10:20 10:30 mdv mentz datenverarbeitung DELFI-Technique : Connection search forward 14:50 10:00

24 24 OriginDestination Regional Transport Long-distance Transport 14:20 14:40 mdv mentz datenverarbeitung DELFI-Technique : Connection search backward 14:50 10:50 10:35 10:10 14:00 10:20 14:50 10:00 14:30 10:30

25 25 Origin Destination Regional Transport Long-distance Transport 14:00 10:10 14:50 1:1 search (forward) mdv mentz datenverarbeitung DELFI-Technique : Trip search 1:1 search (forward) 10:50 14:20 10:30

26 26 Origin Destination Regional Transport Long-distance Transport 14:00 10:50 10:10 14:50 Merging of partial trips mdv mentz datenverarbeitung DELFI-Technique : Result 10:30 14:20

27 27 mdv Distributed Routing C) Multimodal Technique Extended DELFI-Technique for intermodal trip planning combines PT/IT for door-to-door trips needs passive servers for IT routing uses GIS data for IT routing produces graphical maps and textual descriptions restricted to EFA systems. mentz datenverarbeitung

28 28 mdv Multimodal Technique O = regional system of origin D = regional system of destination V = national system for long-distance transport 1.Search stops near origin/destination address 2.Search transition points in O and D 3.Search connections forward in O + V + D 4.Search connections backward in D + V + O 5.Search trips in O 6.Search trips in V 7.Search trips in D 8.Search IT path from origin address to origin stop 9.Search IT path from destination stop to destination address mentz datenverarbeitung DELFI

29 29 mdv Architecture mentz datenverarbeitung CORBA-Client Broker IMTP IDL DELFI IDL EUSpirit IDL DELFI Technique EUSpirit Technique IMTP Server DELFI Server EUSpirit Server Distributed Routing Standard Routing Passive Servers HTTP-ClientWAP-ClientSMS-Client Requests Results START AMADEUS IT Server IT IDL Multimodal Technique Flight IDL CORBA

30 30 Work-split Example mentz datenverarbeitung Stuttgart Town Hall Stockholm Gamla Stan Broker Town Hall VVS Gamla Stan TPG VVS Flight TPG VVS Train TPG VVS Flight TPG VVS Train TPG VVS TågPlus Guiden Travel Link START Amadeus VVS Ferry TPG Trip 1 Trip 2 Trip 3 Dep 06:34 Arr 12:09 VVS Ferry TPG Alternative 1 Alternative 2 Alternative 3 Meta Knowledge International Trains mdv VVS = Stuttgart Region Transport TPG = Stockholm Region Transport

31 31 mdv Example of an intermodal local trip Example Local trip from : Mannheim (Germany) Stresemannstrasse / Friedrichsplatz to :Ludwigshafen (Germany) Roonstrasse / Halbergstrasse using :one PT server with regional timetable data one IT server with regional GIS data mentz datenverarbeitung

32 32 mdv Output of an intermodal local trip Trip Overview mentz datenverarbeitung

33 33 mdv Output of an intermodal local trip Trip Detail mentz datenverarbeitung

34 34 mdv Output of an intermodal local trip mentz datenverarbeitung Origin Detail

35 35 mdv Output of an intermodal local trip mentz datenverarbeitung Destination Detail

36 36 mdv Output of an intermodal local trip mentz datenverarbeitung Overview Map

37 37 mdv Example of an intermodal regional trip Example Intermodal regional trip from : Ludwigshafen (Germany) Limesstrasse / Im Kappes to :Heidelberg (Germany) Marktplatz using :PT server with regional timetable data IT server with regional GIS data mentz datenverarbeitung

38 38 mdv Output of an intermodal regional trip Trip Overview mentz datenverarbeitung

39 39 mdv Output of an intermodal regional trip Trip Detail mentz datenverarbeitung

40 40 mdv Output of an intermodal regional trip mentz datenverarbeitung Origin Detail

41 41 mdv Output of an intermodal regional trip mentz datenverarbeitung Destination Detail

42 42 mdv Output of an intermodal regional trip mentz datenverarbeitung Overview Map

43 43 mdv Example of an intermodal long-distance trip Example Intermodal long-distance trip from : Mulhouse (France) Rue du Beau Regard to :Bremen (Germany) Im Leher Felde / Lilienthaler Heerstrasse using :PT server with Alsace timetable data PT server with German timetable data (trains) PT server with Bremen timetable data IT server with Alsace GIS data IT server with Bremen GIS data mentz datenverarbeitung

44 44 mdv Output of an intermodal long-distance trip Trip Overview mentz datenverarbeitung

45 45 mdv Output of an intermodal long-distance trip Trip Detail mentz datenverarbeitung

46 46 mdv Output of an intermodal long-distance trip mentz datenverarbeitung Origin Detail

47 47 mdv Output of an intermodal long-distance trip mentz datenverarbeitung Destination Detail

48 48 mdv Conclusion mentz datenverarbeitung With EFABroker you can build a distributed intermodal travel information system. Passive servers with EUSpirit, DELFI or EFA interfaces can be integrated. The EUSpirit and DELFI interface should be enlarged to allow real intermodal door-to-door trip planning. Open points: standardized access to Broker language problems (e.g. operational notices)


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