Under JST RISTEX S3FIRE program, we are trying to implement Smart Access Vehicle (SAV) Service in Hakodate. The project adopts the method of service science loop - the repeated cycle of observation, design and implementation. In this paper we report the completion of its first cycle, and discuss how the cycle improved our initial design. We first conducted person trip research in Hakodate. We chose 20 candidates of various age and occupation, and recorded their everyday movements for four months. We then analyzed the result and made a person trip model. The model was then fed into our multi-agent simulator for Hakodate public transportation system. We conducted a small field test with five vehicles for one week. The most significant achievement is that we confirmed that our design of SAV system works. We succeeded in automatically dispatching five vehicles for eleven hours without any significant trouble or human supervision.

1. One Cycle of Smart Access Vehicle
Service Development
Hideyuki Nakashima1, Syoji Sano1, Keiji Hirata1,
Yoh Shiraishi1, Hitoshi Matsubara1,
Ryo Kanamori2, Hitoshi Koshiba3,1, and Itsuki Noda4
1 Future University Hakodate
2 Nagoya University
3 NISTEP
4 AIST

2. Smart Access Vehicle System
Goal: International Standard from Hakodate！
Funded by JST RISTEX S3FIRE
program

3. Smart Access Vehicle System
Current:
Bus and taxi are two
independent services
Bus: cheap but
less convenient
Which?
Taxi: convenient
but expensive
New（SAVS)：
All vehicles are controlled by
the computer system for
optimum dispatch
•Positioning by GPS
Mobility
Cloud
2014/10 © H. Nakashima 3

4. Essentials of Smart Access Vehicle System
• Provision of better public
transportation service
– Reduce necessity of private vehicles
– Real-time response to requests - No
reservation is required
• Flexibility
– Adaptation go climate change,
disaster
– Subsumes traditional bus and taxy
operations = Transportation cloud
• Efficiency
– No fixed route or timetable
– No empty operation
– Predictive allocation of vehicles
2014/10 © 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) only
2014/10 © H. Nakashima 5

6. Demand Responsive Transportation
• EU
– Low demand areas
• Demand Responsive Transport Services: Towards the
Flexible Mobility Agency
Italian National Agency for New Technologies, Energy and the
Environment
http://old.enea.it/com/ingl/New_ingl/publications/
editions/pdf/7_Demand_Transport_Services.pdf
2003
• 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 passengers
per bus
2014/10 h.nakashima@© fun.H. ac.Nakashima jp
8

9. Local optimization vs. Global optimization
• Search for best routs is local optimization
• SAV system is globally optimum
• But the intermediate state (mixture) is worse
Traditional fixed-route
bus SAVS
U-shaped transition
Best routing
2014/10 © H. Nakashima 9

10. Design-Service-Model Loop (aka FNS)
Service at
Hakodate
FOCUS
Modeling by MA
Simulation
Design with
MA Simulation
IMPLEMENT
OBSERVE
2014/10 © H. Nakashima 10

11. Transportation-based Service Unification
Independent services Unified services
SAVS
2014/10 © H. Nakashima 11

12. Observe-Modeling
Modeling by MA
Simulation
OBSERVE
Hakodate
2014/10 © H. Nakashima 12

13. Hakodate Person Trip Data
• GPS tracks (20 person 4
months)
Mobile phone OD data
2014/10 © H. Nakashima 13

14. Hakodate Trip Model
5000
4500
4000
3500
3000
2500
2000
1500
1000
500
0
trips per day
Mon Tue Wed Thr Fri Sat Sun
300
250
200
150
100
50
0
trips per hour
1 3 5 7 9 11 13 15 17 19 21 23
2014/10 © H. Nakashima 14

15. Design with MA Simulation
Modeling by MA
Simulation
FOCUS
Design with
MA Simulation
2014/10 © H. Nakashima 15

16. Simulation for 3000 cars
2014/10 © H. Nakashima 16

17. Design-Implement-Service
Design with
MA Simulation
IMPLEMENT
Service at
Hakodate
2014/10 © H. Nakashima 17

18. SAV System Overview
User
app
(4) Rendezvous info Onboard
(3) Estimated time (3) Rerouting order
Dispatch
system
(2) Request
terminal
Position
(continuous update)
DB
MA Simulation
2014/10 © H. Nakashima 18

19. Field-test in Oct. 2013
FUN
airport
train
station
test
area
2014/10 © H. Nakashima 19

20. Field test in Apr. 2014
• One day operation for
participants of annual
conf. of Serviceology
• 11:30-18:00
• 16 vehicles
• All central area of
Hakodate
2014/10 © H. Nakashima 20

21. User App（Apr. 2014）
2014/10 © H. Nakashima 21

22. I am on board
2014/10 © H. Nakashima 22

23. Dispatch Strategy
• Sequential optimal insertion (quasi optimal)
– MA auction (by all vehicles)
• Preserves predetermined pickup and delivery points
and the order
• Each car computes all possible insertions
23
bus1 O3 D3 O6 O7 D6 D7
bus2 O1 D5 O5 D1 O8 D8 O9 D9
bus3 O2 O4 D4 D2
Dn On
New Demand
2014/10 © H. Nakashima

24. Operation Statistics
(with artificial but random requests)
200
150
100
50
0
Processed more than 30 request/taxi/day
(current taxi average is 25 /taxi/day)
Debugging system
Taxi average
24(Thr) 25(Fri) 26(Sat) 27(Sun) 28(Mon) 29(Tue) 30(Wed)
# of SAV taxi average # of requests processed
2014/10 © H. Nakashima 24

25. Statistics
• 170 ops./day/5cars
– SAV：average 21 min
– On foot：average 31 min
MA simulation
• 250 ops./day/car
– SAV: 16 min
– On foot: 22 min
• 170 ops./day/car
– SAV: 11 min
– On foot: 22 min
Red=pick up, blue=destination
2014/10 © H. Nakashima 25

26. The Most Significant Achievement
Fully Automatic Operation
(World First Multi-vehicle Real-time Rerouting)
Took 1.5 times of average normal taxy operation per day
Found various requests from passengers too
2014/10 © 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 operation
Traditional operation
on SAV system
SAV operation
JUMP
2014/10 © H. Nakashima 28

28. Future Plans
• Deepening the concept of “Virtual
Transportation System”
• Disaster Response
• Service Unification on top of Transportation
Service
• User Interface Issues
2014/10 © H. Nakashima 29

29. Twin Loops of Practice and Theory
Design Blueprint Theoretical Model
Formalization of the
current situation
30
Implementation
Field
Modeling
Implementation
Service Design
Analysis
Theory Loop
Current
Situation
Fractal Structure
Observation and
analysis of practice
Practice Loop

30. Thank You
感謝靜聽
Terima Kasih
Merci Beaucoup
감사합니다
© H. Nakashima 31