1 1(a) MPR 1(b) MPR MPR MPR MPR MPR 2 1 MPR MPR MPR A MPR B MPR 2 MPR MPR MPR MPR MPR GPS MPR MPR MPR 3. MPR MPR 2 MPR 2 (1) (4) Zai
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1 Popular MPR 1,a) 2,b) 2,c) GPS Most Popular Route( MPR) MPR MPR MPR MPR MPR MPR MPR Popular Popular MPR MPR Popular 1. GPS GPS GPS Google Maps *1 Zaiben [1] Most Popular Route( MPR) MPR MPR MPR a) [email protected] b) [email protected] c) [email protected] *1 Zaiben MPR [2] MPR MPR GPS MPR MPR MPR Popular Popular MPR MPR Popular 2. MPR MPR 1 1 (a) (b) c 2013 Information Processing Society of Japan 1
2 1 1(a) MPR 1(b) MPR MPR MPR MPR MPR 2 1 MPR MPR MPR A MPR B MPR 2 MPR MPR MPR MPR MPR GPS MPR MPR MPR 3. MPR MPR 2 MPR 2 (1) (4) Zaiben MPR 1 GPS 2 MPR Transfer ProbabilityTP 3 TP Route PopularityRP 4 RP MPR MPR (1) (2 TP (1)-2 (1)-3 1)-2 1)-3 MPR Popular 3.1 T 1 ( ) T t 1 t M M M K 2 ( ) T T.s T.e 1 T T.s T.e T.e T.s + 1 c 2013 Information Processing Society of Japan 2
3 T = m j=1 T j( T i T j = 0) T a T b 2 T a T b T a.e + 1 = T b.s T ab T T T a T b T Algorithm1 Algorithm 1 Time Segments Combination INITIALIZATION: T set { T 1,..., T m } 1: [ITERATION] 2: for T a, T b T set ( T a T b ) do 3: if ((CHECK T ab ( T a, T b )) is true) then 4: T ab =COMBINE( T a, T b ) 5: T set T set T ab - T a - T b 6: end if 7: end for 8: [STOP CONDITION] 9: There exists no T ab where CHECK T ab ( T a, T b ) is true for any combination of a pair, T a, T b T set ( T a T b ) 2 T.s + i (0 i < M m ) w i std( T m ) min( T m ) T m std( T m ) std( T m ) = M m 1 i=0 (w i avg( T m )) 2 (1) avg( T m ) = M m 1 i=0 w i M m M m (2) min( T m ) min( T m ) = w k ( w i W, w k w i ) (3) W = (w 0,..., w M ) m MPR MPR MPR 2 MPR [3] Most Most γ NumberMost 2 MPR 2 ( Most NumberMost MPR MPR Distribution Most NumberMost 2 MPR 2 MPR Distribution Distribution Most NumberMost Distribution 3 3 T 4 T 5 Most T ab ( β% ) std( T ab ) α 3 std Most T 45 NumberMost T ab ( β% ) std( T ab ) α γ 3 c 2013 Information Processing Society of Japan 3
4 3 1 T 4 T 5 N N N N N NumberMost T 45 Distribution ϵ ( N 0,..., N p ) K most K most =1 2 Distribution 3 24 T T 1 T L 1 L 2 L 3 ϵ T 4 T 5 1 T 4 T 5 N 3 2 Algorithm2 Algorithm Popular MPR Popular Popular TP TP popular TP TP Popular MPR Algorithm 2 CHECK T ab ( T a, T b ) INITIALIZATION: Time Segment T a, T b ; Parameter α, β, γ, ϵ; OUTPUT: combineflag; 1: boolean combineflag; 2: T ab = COMBINE( T a, T b ) 3: [CONDITION Most] 4: if (Most(more than β) of the std( T ab )) α then 5: combineflag = true; 6: end if 7: [CONDITION NumberMost] 8: if (Most(more than β) of the std( T ab )) α and (the minimum number of trajectories of all edges) γ then 9: combineflag = true; 10: end if 11: [CONDITION Distribution] 12: if (trajectory amounts takes up same persentage in a Time Segment then 13: combineflag = true; 14: end if 15: RETURN combineflag; n i TP P r t (n i d) 1 t d 4 t P r t (n i d) = p j n i,d (4) j=1 R Popular p(r) 5 p(r) = i P r t (n j d) (5) j= Truck [4] GPS 3 TP MPR MPR MPR Popular 2 1 T 24 t 1 t AM0 c 2013 Information Processing Society of Japan 4
5 4 3 MPR Popular Popular 24 MPR T 12 MPR T 345 MPR T 78 MPR T 1 MPR T 2 MPR T 6 MPR T 7 MPR T 8 MPR Most T 12 (0:00 5:59), T 345 (6:00 14:59), T 6, T 7, T 8 NumberMost T 1, T 2, T 345 (6:00 14:59), T 6, T 7, T 8 Distribution T 12 (0:00 5:59), T 345 (6:00 14:59), T 6, T 78 (18:00 23:59) AM2 59 T 1 AM3 AM5 59 T 2 AM6 AM8 59 T 3 AM9 AM11 59 T 4 PM12 PM14 59 T 5 PM15 00 PM17 59 T 6 PM18 PM20 59 T 7 PM21 00 PM23 59 T α β γ ϵ 0 T 1, T 2,..., T 8 1 α 10.0 β 60.0 γ 20 ϵ Transfer Network GPS GPS Most NumberMost T 3 T 4 T 5 AM6 PM14 59 Most T 1 T 2 NumberMost 20 Distribution T 1 T 2 T 3 T 4 T 5 T 7 T MPR MPR Popular MPR MPR Popular 5 Popular 3 5(a) (AM0 PM23 59 ) MPR MPR 6 Popular (b) T 12 (AM0 AM5 59 ) MPR MPR 10 (a) MPR Popular (a) MPR T 12 (AM0 AM5 59 ) MPR MPR 5(c) T 345 (AM6 PM14 59 ) MPR AM6 PM14 59 MPR Popular (a) MPR T 345 (AM6 PM14 59 ) 5(c) MPR 5(d) T 78 (PM18 PM23 59 ) MPR 5(a) MPR Popular T 78 (PM18 PM23 59 ) MPR 3 ( T 12 T 345 T 78 ) ( T 1 T 2 T 6 T 7 T 8 ) Popular MPR MPR Popular MPR T 345 c 2013 Information Processing Society of Japan 5
6 MPR T 78 MPR Popular MPR 3 T 345 T 78 Distribution 5. [5] [6] [7] Popular Route Zaiben Ling [8] 2 Uncertain Trajectory Popular Route GPS GPS Julia [9] GPS Kai-Ping [10] 2 MPR Hsun [11] MPR MPR Popular MPR MPR MPR Popular MPR MPR JSPS [1] Zaiben Chen, Heng Tao Shen, Xiaofang Zhou. Discovering popular routes from trajectories. In ICDE 11, pp , [2] 22 / (2010) [3],,. MPR. DEIM2013, [4] Trucks Data Set: [5] Chengxuan Liao, Jiaheng Lu, and Hong Chen. Synthesizing routes for low sampling trajectories with absorbing Markov chains. In WAIM 11, pp , [6] Yu Zheng, Lizhu Zhang, Xing Xie, Wei-Ying Ma. Mining interesting locations and travel sequences from gps trajectories. In WWW 09, pp , [7] Jing Yuan, Yu Zheng, Xing Xie, Guangzhong Sun. Driving with Knowledge from the Physical World. In KDD 11, pp ,2011. [8] Ling-Yin Wei, Yu Zheng, Wen-Chih Peng. Constructing Popular Routes from Uncertain Trajectories. In KDD 12, pp , [9] Julia Letchner, John Krumm, Eric Horvitz. Trip Router with Individualized Preferences (TRIP): Incorporating Personalization into Route Planning. In IAAI 06, pp , [10] Kai-Ping Chang, Ling-Yin Wei, Mi-Yeh Yeh, Wen-Chih Peng. Discovering personalized routes from trajectories. In LBSN 11, pp.33-40,2011. [11] Hsun-Ping Hsieh, Cheng-Te Li, Shou-De Lin. Exploiting Large-Scale Check-in Data to Recommend Time- Sensitive Routes. In UrbComp 12, pp.55-62,2012. c 2013 Information Processing Society of Japan 6
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The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). The material has been made available on the website
The copyright of this material is retained by the Information Processing Society of Japan (IPSJ). The material has been made available on the website by the author(s) under the agreement with the IPSJ.
