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1 * * ** * * Transport Information Service Realized with the Fleet Management ASP Kiyotaka KISHINO*Yasushi ISHIDA*Takumi FUSHIKI** Naohiko GONMORI* Hiroyuki NAKAGAWA* Abstract: Fleet management system utilizing GPS and packet cellular phone networks are spreading recently in Japan. We started service of the truck fleet management system as an application service provider in April Truck location data collected with the system are not only used for fleet management of vehicles, but are also very effective in monitoring traffic conditions by regarding trucks as floating cars in traffic information gathering. In this paper, we constructed a traffic information system which providers traffic conditions and travel times off road links when using truck location data as the floating car data. We examined the effectiveness of the truck location data as the floating car data using actual data, and confirmed that nearly 90% of the location data was applicable to traffic information estimation. We also developed an arrival time prediction system based on the location data, and attempted service application to the fleet management system. 1. ASP 15 1) GPS(Global Positioning System) 2) 995 IT(Information Technology ASP(Application Service Provider)e-trasuse-Transport Support System 3) VICS(Vehicle Information and Communication System) (1) 4) (2) 300 5) ) (3) 7) 8) 9)10)11) * () 4-6 ** * Hitachi, Ltd. ** Hitachi Research Rab., Hitachi, Ltd. 5 (Received March 17, 2003) 49

2 Center SCM(Supply Chain Management) Freight Report Safety Drive Location Traffic Inf. IT Cellular 99 Origin Destination OBU OBU : On Board Unit Fig.1 Fleet Management System Fig.1 ASP GPS 3. ASPe-trasus 3.1 ASPe-trasus ASP e-trasus e-trasus ASP ASP 50 11:10 Fig.2 Truck Location Management 50

3 3.2 ASPe-trasus 1 e-trasus Fig.2 e-trasus 1 yy ,000 Fig.3 ( ( ) ) 1 2 Fig.3 Sample of daily Report 51

4 ASP ( ) ( ) (1 2 )180 (3 )300 (5 ) 1 Fig.4 Table Truck Map-Matching Arrival Time Table1 Result of Simulation (sec) Ratio of Fig.4 Method of Truck Data Usage for Traffic Information Speed Over (%) Fig.5 a 3 12 /( 4,800 /) ( km Fig.5 a 4.1 1VICS ITS(Intelligent Transportation System) Fig.5(b) FM a VICS GPS ( ) 1030 VICS 12) 1 VICS Fig.6 A-B-C 3 Data Input & Route Estimation Prediction 52

5 計測自動制御学会産業論文集 (a) Speed on Road Map (Truck Location Data) (b) Speed on Road Map (Route Estimation) Fig.5 Speed on Road Map 53

6 C1 A1 B1 C x A2 B2 A x B x B4 C2 A3 B3 x : Gathering Point(GPS) : Candidate Point : Matching Point Fig.6 Map-Matching and Route Estimation A2 23 (B) (B1B4) (C) 2 C1 C2 8 (Table 2 ) A-B-C ( Dijkstra 13) ) Table2 Route Pattern GPS Pattern No.1 Point No.2 Point No.3 Point 1 A2 B1 C1 2 A2 B1 C A2 B2 C A2 B2 C A2 B3 C1 6 A2 B3 C A2 B4 C1 8 A2 B4 C (DRM(Digital Road Map VICS 24 8 A1 A (A2-B2-C2) 50m ) (B) (B2) 13 A1 (A2-B2) 25 (A) 14 A1 (C) A1A2A3 (C) (C2) (B2-C2) 4 b 15 Fig.5(b) Fig.5(a) Fig.5 50km 1 14 Fig.6 A-B-C (84) 133 (16%) 20 a 21 (A) 4 22 Table 3 10:00 1(Link 1) 54

7 4(Link4) 25 ( ) 30 ( ) Destination 14) Route Table 3 Travel Time Problem Link 1 Link 2 Link 3 Link 4 10:00 5min 5min 5min 10 min 10:05 5min 5min 10 min 10 min 10:10 5min 10 min 10 min 10 min 10:20 10 min 10 min 10 min 10 min : Prediction : 25 min : Result Time : 30 min Origin Fig.7 Travel Time Prediction Origin Link 1 Link 2 Link 3 Destination 7:15 Time 20(km/h) 48(km/h) 7:10 14(km/h) Table 4 7:05 ( ) / ( ) 45(km/h) 29(km/h) 7:00 Route Fig.8 Example of Arrival Time Prediction Table4 Speed of Each Link and Time Span 45km/h 1 2 Time 7:00-7:05-7:10-7:15-2 Link No. 7:04 7:09 7:14 7:19 29km/h 3 Link Link 2 (29) :05:00 Link (48) 44 29km/h 14km/h (*)Estimation Data Average Speed(km/h) Table 4 * 5 Table 4 Fig.9 Fig.9 a Fig.7 b c Fig.9(a) VICS 70km 90km 13 AM8:00 AM10:00 Table 4 Fig.8 10km 40km

8 Higashi-Kantou Expressway around Funabashi Speed[km/h] (19Data) (a) Time Metropolitan Expressway around Hamazakibashi Speed[km/h] (19Data) Speed[km/h] (b) Time Harumi-Dori around Hibiya (15Data) X Y R Xn Yn L XXn YYn 1L R 2L R 3L R L R 15 1 Time (c) Fig.9 Sample of Link Speed Fig.9(b) km Fig.9(c) 1 GPS 84 56

9 Shop 2 Shop 1 Radius 100 m Fig.10 Radius of Shop 1 and Shop 2 Shop Shop 2 R 300m100m 30m 3 Shop 3 T Table 5 Table % Fig.11 Delivery Order and Route 30 m 95 GPS Fig.10 R GPS 2.8%(12/423 GPS Table 5 Automatic Arrival Recognition Radius 300m 100m 30m Fig Automatic Arrival Recognition 100% (30/30) 100% (15/15) 95% (58/61) 3 3 Rate 2.9% 2/70 Average of (T) 4min 44sec 4min 12sec 4min 40sec ASP e-trasus 57

10 11) : 60,, 43-12, 3801/3808 (2002) ) () : ) : , 113/136 (1998) 14) : (2001) km () SCM EDI ( ) / () ( ) ),,, : (),, 82-, 39/42 (2000) 2) M. Yoshii : Trial for Commercial Vehicle Operations Management Using Information Technology, Proc. of 8 th, ITS World Congress, Sydney (2001) 3) : e-trasus, 4) () (VICS ): () 5) :,, 25, 12/13 (2001) 6) Internet ITS PROJECT 7) P. Larima : VERDI-from Field Trial to Deployment, Proc. of 4 th ITS World Congress, Berlin (1997) 8) K. Choi : An Algorithm for Calculating Dynamic Link Travel Times Using GPS and a Digital Road Map, Proc. of 5 th ITS Congress, Seoul (1998) ) K. Aoki : Research and Development and the Proof Test the Probe Car, Proc. of 7 th ITS W0rld Congress, Turin (2000) 10) :IPCar,, (), 36-3, 48/50 (2001) 58

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