1 3 GPS GPS Holguin-Veras 1) Denver Browne et al. 11) 197 Christian et al. 12) GPS b) GPS Quddus et al. 13) Greenfield 14) Miyashita et al. 15), 16) G

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1 C1 tyokota@kiban.kuciv.kyoto-u.ac.jp C1 d-tamagawa@kiban.kuciv.kyoto-u.ac.jp 3 Key Words : probe cars, freight vehicle, map-matching, logistics, GPS 1. (1) GPS 1) 4) 1 m (2) a) Yokota & Tamagawa 5) 1 2 Filiozzi 6), 7) Greaves 8) Melbourne 3 7 GPS OD GPS Fitzgrald et al. 9) GPS

2 1 3 GPS GPS Holguin-Veras 1) Denver Browne et al. 11) 197 Christian et al. 12) GPS b) GPS Quddus et al. 13) Greenfield 14) Miyashita et al. 15), 16) GPS Miwa et al. 17), 18) 3 m Dijkstra 1) 2) 3) 3 Miwa et al. 17) Makimura et al. 2) GPS 1 DRM

3 1 Makimura et al. 2) Miwa et al. 17) 21) GPS GPS PHS DRM DRM GPS 21) PHS 2 1 km 1 GPS Maikimura 2) Makimura 2) DRM 2.(4) c) 19), 22), 23) 23)

4 (3) GPS (1) P i j E(i, j) = α f ( p p (t i ), p m, j (t i ) ) (1 α) g(θ p,i, θ l,m ), (1) f ( p p (t i ), p m, j (t i ) ) = exp ( p p(t i ) p m (t i ) 2 ), (2) 2σ 2 p p (t i ) = ( x p (t i ), y p (t i ) ), (3) p m, j (t i ) = (x m (t i ), y m (t i )), i =, 1, 2,..., P 1, j =, 1, 2,..., Q 1. (4) (1) 2 p p (t i ) t i p m, j (t i ) t i j P Q Q 16 (1) α a) (2) f ( p p (t i ), p m, j (t i ) ) t i p p (t i ) j p m, j (t i ) σ GPS 2 m 5) 2

5 (2) 3 d d 1 m b) (1) 2 g(θ p,i, θ l,m ) θ p,i θ m GPS 3 p p (t i ) p p (t i 1 ) l m (x 1, y 1 ) (x 2, y 2 ) θ l,m = arg{(x 2 x 1 ) i(y 2 y 1 )} η (5) η = 1, η = 1 (6) θ l,m π θ l,m < π (7) i i 2 = 1 (5) arg C math.h atan2 θ p,i θ p,i = arg{(x p,i x p,i 1 ) i(y p,i y p,i 1 )} (8) π θ p,i < π (9) θ l,m θ p,i θ l,m θ p,i 2π < θ l,m θ p,i < 2π (1) 1 θ l,m θ p,i < 2π (11) 1 { g(θ p,i, θ l,m ) = max, 1 θ } l,m θ p,i, π/2 θ l,m θ p,i < π (12) { g(θ p,i, θ l,m ) = max, 3 θ } l,m θ p,i, π/2 g( p,i, l,m) 1 1 /2 3 /2 2 p,il,m 5 g g( p,i, l,m) /2 3 /2 2 p,il,m 6 g π θ l,m θ p,i < 2π (13) 5 g(θ p,i, θ l,m ) = 1 θ l,m θ p,i, π/2 θ l,m θ p,i < π/2 (14) g(θ p,i, θ l,m ) = 1 θ l,m θ p,i, π/2 π/2 θ l,m θ p,i < π (15) g(θ p,i, θ l,m ) = 3 θ l,m θ p,i, π/2 π θ l,m θ p,i < 3π/2 (16) g(θ p,i, θ l,m ) = 3 θ l,m θ p,i, π/2 3π/2? θ l,m θ p,i < 2π (17) g 6 GPS 2

6 7 α 1 km/h Q c) α (1) α α km 1 α DRM α 7 α % α α.5 (2) (1) 8 34/47 = 72%

7 /47 73% GPS 5 m a) b) 1 1 (a) 1 (b) 1 F (18) P 1 F(s(i)) = E(i, j) a j, j δ{ j, s(i)} (18) j ϕ i 1 i= j ϕ i a j, j = 1 stage i link j stage i 1 a j, j link j = stage i link j stage i 1 link j (19) stage ϕ i stage i 16 (18) i (18)(19) j j stage i 1 stage i δ{ } Kronecker j s(i) 1 s(i) stage i (5) s(i), i =,..., P 1. F (19) (18) F (3) (18) F (2) w(i, j) i = i j 11

8 リンク候補の軸 (j=,1,..,q-1) Stage 局所評価値局所評価値局所評価値局所評価値 E(,) E(,1) E(,j) E(,Q-1) w(,) w(,1) w(,j) w(,q-1) 到達可能性行列 : aj,=1, 到達可能な場合 aj,=, 到達不可能な場合時間軸 (i=,1,..,p-1) max{w(,)a,} max{w(,)a1,} max{w(,)aj,} 局所評価値局所評価値局所評価値局所評価値 E(1,) E(1,1) E(1,j) E(1,Q-1) max{w(,)aq-1,} Stage 1 w(1,) w(1,1) w(1,j) w(1,q-1) 到達可能性行列 : aj,=1, 到達可能な場合 aj,=, 到達不可能な場合 max{w(1,) a,} max{w(1,) a1,} max{w(1,) aj,} max{w(1,) aq-1,} 後段の stage へ 11 w(i, j) = max{w(i 1, j ) a j, j E(i, j)}, (2) j j =, 1, 2,..., Q 1, j =, 1, 2,..., Q 1, i = 1, 1, 2,..., P 1 11 P 1 i = i = 1 w(, j) = E(, j) j =, 1, 2,..., Q 1 (21) w(1, j) = max{w(, j ) a j j, j E(i, j)} = max{e(, j ) a j j, j E(1, j)} j =, 1, 2,..., Q 1 (22) i = 2.. w(2, j) = max{w(1, j ) a j j, j E(2, j)} i = P 1 j =, 1, 2,..., Q 1 (23) w(p 1, j) = max{w(p 2, j ) a j j, j E(P 1, j)}, j =, 1, 2,..., Q 1 (24) P 1 {w(p 1, j)}, j =, 1, 2,...., Q 1 stage P Q E PQ 1 m

9 1 4 Intel c Core TM 2 Duo E84 3. GHz 12 47/47 = 1% 4 GB (4) %

10 % 3. (1) GPS 2 Yokota & Tamagawa 3) Tamagawa & Yokota 4) 3.1% OD 24) ) (2)

11 % 9% 8% 7% 6% 5% 4% 3% 2% 1% % 7, 6, 5, 4, 3, 2, 1, ) 8 1 km km 8 Yokota & Tamagawa 5) GPS 2 2 km 95% km 4 km 18 2 km 8% 17 1% 95% 9% 85% 8% 75% 7% 65% 6% 55% 5% 45% 4% 35% 3% 25% 2% 15% 1% 5% % 18 (3) 19 1 km 2 km 1 km 2% 2 km 3 km NEXCO 2% 4 km 5 km 2 21

12 4, 1% 9% 8% 7% 6% 5% 4% 3% 2% 1% NEXCO 45, 4, 35, 3, 25, 2, 15, 1, 5, 4 3,5 3, 2,5 2, 1,5 1, 5 21 y =.923x R² =.575 % (km) = km/h 7% (4) 2 km 7% (25) 6 km , %8%

13 y =.1559x.63 R² = % 8% 95% 95% (km) y =.7429x R² = % 25 3 km 25 6 R km 7 km km 7%

14 1) ) ) Yokota, T. and Tamagawa, D.: An Analysis of Urban Freight Transport Behavior Using A Probe Car System - A Case Study of Keihanshin Area in Japan?, WCTR Lisbon, Lisbon, 21. 4) Tamagawa, D. and Yokota, T.: An Evaluation of Urban Road Network from a Viewpoint of Logistics by Using Probe Car Systems - A Case Study of the Keihanshin Area in Japan, The 3 rd International Conference on Transportation and Logistics (T-LOG 21), 21. 5) Yokota, T. and Tamagawa, D.: Constructing Two-layered Freight Traffic Network Model from Truck Probe Data, International Journal of Intelligent Transportation Systems Research, Vol. 9, No. 1, pp. 1-11, ) Figliozzi, M.: Analysis of the Efficiency of Urban Commercial Vehicle Tours Data Collection Methodology, and Policy Implications, Transportation Research Part B: Methodological, Vol. 41, No. 9, pp , 27. 7) Figliozzi, M.: The impacts of congestion on commercial vehicle tour characteristics and costs, Transportation Research Part E: Logistics and Transportation Review, Vol. 46, Issue 4, pp , July 21. 8) Greaves, S. P.: Collecting Commercial Vehicle Tour Data with Passive Global Positioning System Technology, Transportation Research Record, No. 249, pp , 28. 9) Fitzgerald, C., Zhang, J. and Stopher, P.: Processing GPS Data From Travel Surveys, International Global Navigation Sattellite Systems Society (IGNSS) Symposium, Gold Coast, Australia, 26. 1) Holguin-Veras, J.: Observed Trip Chain Behavior of Commercial Vehicles, Transportation Research Record, Vol. 196, pp. 74-8, ) Browne, M., Allen, J., Steele, S., Cherrett, T. and McLeod, F.: Analysing the results of UK urban freight studies, Proceedings of the 6 th International Conference on City Logistics, Puerto Vallarta, pp , ) Christian, A., Daniele, P. and Louis, R. J.: Urban freight establishment and tour-based surveys for policy-oriented modelling, Proceedings of the 6 th International Conference on City Logistics, Puerto Vallarta, pp , ) Quddus, M. A., Ochieng, W. Y. and Noland, R. B.: Current map-matching algorithms for transport applications: Stateof-the art and future research directions, Transportation Research Part C, Vol.15, pp , ) Greenfield, J.: Matching GPS Observations to Locations on a Digital Map, In Annual Meeting of the Transportation Research Board, ) Miyashita, K., Terada, T. and Nishio, S.: A Map Matching Algorithm for Car Navigation Systems that predict User Destination, 22 nd International Conference on Advanced Information Networking and Applications-Workshop, pp , ) Vol. 5, No. 1, pp , ) Miwa, T., Sakai, T. and Morikawa, T.: Route Identification and Travel Time Prediction Using Probe-Car Data, International Journal of ITS Research, Vol. 2, No. 1, pp , October ) Vol.44, No.3, pp.1-11, ) Miwa, T. and Morikawa, T.: Analysis on Route Choice Behavior Based on Probe-Car Data, 1th World Congress on Intelligent Transport Systems, Madrid, 23. 2) Makimura, K., Kikuchi, H., Tada, S., Nakajima, Y., Ishida H. and Hyodo, T.: Performance Indicator Measurement Using Car Navigation Systems, the 81th Transportation Research Board, CD-ROM, ) PHS No.663/IV- 48, pp.95-14, ) Kitamura, S., Yamamoto, T., Yoshii, T. and Miwa, T.: Descriptive Analysis of Detouring Traffic through Neighbourhood Streets Using Probe Car Data, 1 th World Congress on Intelligent Transport Systems, Madrid, ) 64 IV-5 CD-ROM29. 24) ( ) STUDY ON THE ROAD USAGE BEHAVIOUR OF FREIGHT VEHICLES IN KEIHANSHIN AREA BY USING PROBE DATA Takayoshi YOKOTA and Dai TAMAGAWA In order to be able to analyze the road usage behavior of freight vehicles from the GPS probe data, the authors developed an expressway usage detection algorithm by introducing a new dynamic programming based map-matching and route estimation algorithm. By applying the algorithm, the road use behaviour of 3 freight vehicles of Keihanshin area, Japan is made clear. One of the typical results shows that more than 8% of all VHT (vehicle hours travelled) was consumed with low speed driving at less than 2 km/h velocity. The length of access to expressways and the egress from them is crucial in choosing the expressway in freight vehicles trips. It is shown that the thresholds of the length of access and egress is almost linear to the trip length they take. In order to increase the usage of expressway by freight vehicles, decreasing the length of the access and egress physically or by toll policy is desired.

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橡 PDF * * ** * * Transport Information Service Realized with the Fleet Management ASP Kiyotaka KISHINO*Yasushi ISHIDA*Takumi FUSHIKI** Naohiko GONMORI* Hiroyuki NAKAGAWA* Abstract: Fleet management system utilizing