1. One Cycle of Smart Access VehicleService DevelopmentHideyuki Nakashima1, Syoji Sano1, Keiji Hirata1,Yoh Shiraishi1, Hitoshi Matsubara1,Ryo Kanamori2, Hitoshi Koshiba3,1, and Itsuki Noda41 Future University Hakodate2 Nagoya University3 NISTEP4 AIST

2. Smart Access Vehicle SystemGoal: International Standard from HakodateFunded by JST RISTEX S3FIREprogram 3. Smart Access Vehicle SystemCurrent:Bus and taxi are twoindependent servicesBus: cheap butless convenientWhich?Taxi: convenientbut expensiveNewSAVS)All vehicles are controlled bythe computer system foroptimum dispatchPositioning by GPSMobilityCloud2014/10 H. Nakashima 3 4. Essentials of Smart Access Vehicle System Provision of better publictransportation service Reduce necessity of private vehicles Real-time response to requests - Noreservation is required Flexibility Adaptation go climate change,disaster Subsumes traditional bus and taxyoperations = Transportation cloud Efficiency No fixed route or timetable No empty operation Predictive allocation of vehicles2014/10 H. Nakashima 4 5. Demand Responsive Transport (DRT) Detour/free stop Fixed route{detour/stop} on demand Pre-scheduling Flex-routing Fixed stops with on-demand routing Pre-scheduling EU projects Full-demand Low demand areas (mainly pre-scheduling Full-demand buses Share taxi Urban areas (Real-time scheduling) Our system (SAVS) only2014/10 H. Nakashima 5 6. Demand Responsive Transportation EU Low demand areas Demand Responsive Transport Services: Towards theFlexible Mobility AgencyItalian National Agency for New Technologies, Energy and theEnvironmenthttp://old.enea.it/com/ingl/New_ingl/publications/editions/pdf/7_Demand_Transport_Services.pdf2003 SAV Urban areas (total employment of all vehicles)2014/10 H. Nakashima 6 7. Fixed Number of Buses(average time of travel / no. of demand)2014/10 h.nakashima@fu nH..a Nc.ajpkashima 7 8. More Buses with as Passengers Increase(average time of travel / no. of demand)number of passengersper bus2014/10 h.nakashima@ fun.H. ac.Nakashima jp8 9. Local optimization vs. Global optimization Search for best routs is local optimization SAV system is globally optimum But the intermediate state (mixture) is worseTraditional fixed-routebus SAVSU-shaped transitionBest routing2014/10 H. Nakashima 9 10. Design-Service-Model Loop (aka FNS)Service atHakodateFOCUSModeling by MASimulationDesign withMA SimulationIMPLEMENTOBSERVE2014/10 H. Nakashima 10 11. Transportation-based Service UnificationIndependent services Unified servicesSAVS2014/10 H. Nakashima 11 12. Observe-ModelingModeling by MASimulationOBSERVEHakodate2014/10 H. Nakashima 12 13. Hakodate Person Trip Data GPS tracks (20 person 4months)Mobile phone OD data2014/10 H. Nakashima 13 14. Hakodate Trip Model5000450040003500300025002000150010005000trips per dayMon Tue Wed Thr Fri Sat Sun300250200150100500trips per hour1 3 5 7 9 11 13 15 17 19 21 232014/10 H. Nakashima 14 15. Design with MA SimulationModeling by MASimulationFOCUSDesign withMA Simulation2014/10 H. Nakashima 15 16. Simulation for 3000 cars2014/10 H. Nakashima 16 17. Design-Implement-ServiceDesign withMA SimulationIMPLEMENTService atHakodate2014/10 H. Nakashima 17 18. SAV System OverviewUserapp(4) Rendezvous info Onboard(3) Estimated time (3) Rerouting orderDispatchsystem(2) RequestterminalPosition(continuous update)DBMA Simulation2014/10 H. Nakashima 18 19. Field-test in Oct. 2013FUNairporttrainstationtestarea2014/10 H. Nakashima 19 20. Field test in Apr. 2014 One day operation forparticipants of annualconf. of Serviceology 11:30-18:00 16 vehicles All central area ofHakodate2014/10 H. Nakashima 20 21. User AppApr. 20142014/10 H. Nakashima 21 22. I am on board2014/10 H. Nakashima 22 23. Dispatch Strategy Sequential optimal insertion (quasi optimal) MA auction (by all vehicles) Preserves predetermined pickup and delivery pointsand the order Each car computes all possible insertions23bus1 O3 D3 O6 O7 D6 D7bus2 O1 D5 O5 D1 O8 D8 O9 D9bus3 O2 O4 D4 D2Dn OnNew Demand2014/10 H. Nakashima 24. Operation Statistics(with artificial but random requests)200150100500Processed more than 30 request/taxi/day(current taxi average is 25 /taxi/day)Debugging systemTaxi average24(Thr) 25(Fri) 26(Sat) 27(Sun) 28(Mon) 29(Tue) 30(Wed)# of SAV taxi average # of requests processed2014/10 H. Nakashima 24 25. Statistics 170 ops./day/5cars SAVaverage 21 min On footaverage 31 minMA simulation 250 ops./day/car SAV: 16 min On foot: 22 min 170 ops./day/car SAV: 11 min On foot: 22 minRed=pick up, blue=destination2014/10 H. Nakashima 25 26. The Most Significant AchievementFully Automatic Operation(World First Multi-vehicle Real-time Rerouting)Took 1.5 times of average normal taxy operation per dayFound various requests from passengers too2014/10 H. Nakashima 27 27. Jumping over the U-curve (power of IT) Transition procedure must be within service loop SAV subsumes traditional bus/taxi systems as special cases Neutral evolution method Traditional fixed-route operation on SAV Optimum routing Occasional SAV operation test When ready, switch to the new operationTraditional operationon SAV systemSAV operationJUMP2014/10 H. Nakashima 28 28. Future Plans Deepening the concept of VirtualTransportation System Disaster Response Service Unification on top of TransportationService User Interface Issues2014/10 H. Nakashima 29 29. Twin Loops of Practice and TheoryDesign Blueprint Theoretical ModelFormalization of thecurrent situation30ImplementationFieldModelingImplementationService DesignAnalysisTheory LoopCurrentSituationFractal StructureObservation andanalysis of practicePractice Loop 30. Thank YouTerima KasihMerci Beaucoup H. Nakashima 31