-1DWQ6FL)RXQGDWLRQ6UL/DQND DOI: http://dx.doi.org/10.4038/jnsfsr.v45i3.8188
RESEARCH ARTICLE
8VHRIDUWL¿FLDOQHXUDOQHWZRUNVDQGVXSSRUWYHFWRUPDFKLQHVWR SUHGLFWODFNLQJWUDI¿FVXUYH\GDWD Mohammed Saiful Alam Siddiquee1 and Kalum Priyanath Udagepola2* 1 2
Department of Civil Engineering, Faculty of Engineering, King Abdul Aziz University, Jeddah, Saudi Arabia. 'HSDUWPHQWRI,QIRUPDWLRQDQG&RPSXWLQJ6FLHQFHV6FLHQWL¿F5HVHDUFK'HYHORSPHQW,QVWLWXWHRI7HFKQRORJ\$XVWUDOLD%ULVEDQH$XVWUDOLD
Revised: 21 December 2016; Accepted: 19 January 2017
Abstract: 7KH DLP RI WKLV SDSHU ZDV WR SUHGLFW ODFNLQJ GDWD IURPDWUDI¿FVXUYH\DORQJDSULQFLSDOKLJKZD\LQ%DQJODGHVK XVLQJ DUWL¿FLDO QHXUDO QHWZRUN $11 FRPELQHG ZLWK WKH VXSSRUWYHFWRUPDFKLQH 690 7UDI¿FGDWDZHUHREWDLQHGDWDQ hourly rate using a methodical inquiry over a four-year period DW WKH -DPXQD WROO FROOHFWLRQ SRLQW ZKLFK LV ORFDWHG DORQJ WKH 1RUWK %HQJDO FRUULGRU RI %DQJODGHVK 7ZR HYROXWLRQDU\ FRPSXWDWLRQDO VWDWLVWLFDO SURFHGXUHV ZHUH XVHG DORQJ ZLWK LWV FRUUHVSRQGLQJ QXPHULFDO PRGHO 7KH QHXUDO QHWZRUN DQG 690ZHUHIHGZLWKGDWDIURPUHFXUULQJZHHNVRYHUDIRXU \HDUSHULRG7KHPLVVLQJGDWDZHUHSUHGLFWHGZLWKVLJQL¿FDQW DFFXUDF\ XVLQJ ERWK PHWKRGV$FFXUDF\ RI WKH PHWKRGV ZDV FRPSDUHGZKLFKVKRZHGWKDWWKH690PHWKRGLVPXFKPRUH accurate than the ANN technique. Combination of both the $11DQG690PRGHOVFDQEHXVHGWRREWDLQWUHQGVLQWUDI¿F data more accurately. Keywords: $UWL¿FLDO QHXUDO QHWZRUN HVWLPDWLRQ VXSSRUW YHFWRUPDFKLQHWUDI¿FGDWDSUHGLFWLRQ
INTRODUCTION ,QWKHDEVHQFHRIORQJGXUDWLRQWUDI¿FFRXQWGDWDWKHUH DUHWZRDSSURDFKHVIRUWKHHVWLPDWLRQRIDQQXDODYHUDJH GDLO\WUDI¿F $$'7 IURPVKRUWFRXQWVHLWKHUE\XVLQJD regression model or by applying previously established H[SDQVLRQ IDFWRUV RI VLPLODU IDFLOLWLHV *DUEHU +RHO 6HYHUDO VWXGLHV .DXE 6KDUPD FDUULHG RXW LQ GHYHORSHG FRXQWULHV KDYH VKRZQ WKDW LI WKH FRQWLQXRXV ÀRZ GDWD LV DYDLODEOH IRU D SDUWLFXODU URDG VHJPHQW RI D KLJKZD\ D PRGHO FRXOG EH EXLOW FRQVLGHULQJWKHUHFXUVLYHSDWWHUQRIWUDI¿FÀRZLQRUGHU to predict the AADT from short count (i.e. less than 24 KRXUV GDWD ,Q ,QGLD .XPDUD et al., 2013) an attempt *
Corresponding author (
[email protected])
KDV EHHQ PDGH WR HVWDEOLVK D WUHQG LQ WUDI¿F YROXPHV SHUWDLQLQJ WKH GDLO\ ZHHNO\ PRQWKO\ DQG \HDUO\ ÀRZ patterns, and to use these particulars for forecasting WUDI¿FYROXPHV6KRUWWHUPWUDI¿FÀRZLVDPDMRUIHDWXUH LQDUHDVVXFKDVWUDI¿FPDQDJHPHQWDQGFRQWURO $EGL Moshiri, 2015). There are chaotic and frantic properties GLVSOD\HGLQVKRUWWHUPWUDI¿F'HVSLWHVRPHVWXGLHVWKDW SULRULWLVH ORQJWHUP WUDI¿F ÀRZ IRUHFDVWLQJ ZH KDYH considered short-term forecasting (Sun et al., 2008). In ORQJWHUPWUDI¿FÀRZIRUHFDVWLQJHYHQWVVXFKDVVFKRRO timings, holidays and other periodic events are utilised IRUMXGJLQJWKHIXWXUHWUDI¿FÀRZV &HWLQHUet al., 2010). In this study, by applying the short-term forecaster, the PLQLPXPUHVROXWLRQRIIRUHFDVWLQJLVRQHPLQXWHZKLFK is obtained from a 2-lane eastbound stretch of the I-494 IUHHZD\ LQ 0LQQHVRWD 86$ %XW LQ WKH FDVH RI ORQJ WHUP WUDI¿F ÀRZ IRUHFDVWLQJ ¿YH PLQXWHV RU RQHKRXU WUDI¿FGDWDVXFKDVKLVWRULFDOGDWDDWHDFKPDMRUMXQFWLRQ in the city are considered (Wang et al., 2006; Cetiner et al., 2010; Chiou et al., 2010). Although these studies ZHUHFDUULHGRXWLQGHYHORSHGDQGGHYHORSLQJFRXQWULHV such as India many years ago, so far no such effort in this subject has been made in Bangladesh. One of the main reasons as stated earlier is the lack of long duration URXQGWKHFORFN ÀRZ GDWD 0RUHRYHU WR PDNH D WLPH VHULHVPRGHOLQWKHFRQWH[WRI%DQJODGHVKLVVRPHZKDW complex and intricate in nature as compared to that of GHYHORSHGFRXQWULHVIRUWKHIROORZLQJUHDVRQV • The shifting nature of the month of Ramadan and (LGIHVWLYDOJUHDWO\DIIHFWVWKHWUDI¿FÀRZSDWWHUQDQG PDNHVLWGLI¿FXOWWRSUHGLFWPRQWKO\H[SDQVLRQIDFWRUV ,Q WKH ZHVWHUQ ZRUOG PDLQ IHVWLYDOV OLNH &KULVWPDV 1HZ Tcrit, the null hypothesis ZDVUHMHFWHGDQGLWZDVFRQFOXGHGZLWK FRQ¿GHQFH WKDWWKHVLJQL¿FDQWGLIIHUHQFHLQVLPXODWLRQE\$11DQG actual data exists. On the otherhand, in the second case, WKHQXOOK\SRWKHVLVZDVWKDWWKHUHLVQRGLIIHUHQFHEHWZHHQ SUHGLFWHGGDWDE\690DQGPHDVXUHGGDWD,WZDVIRXQG that Tobs = - 1.0 and Tcrit = - 1.71387. Since Tobs 7crit, the 240
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Traffic Volume 7UDI¿FYROXPH
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Figure 3: 3ORWRIKRXUO\WUDI¿FYROXPHvs hours of a day for the date 13th February, 1999
8
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Figure 4: 3ORWRIKRXUO\WUDI¿FYROXPHvs hours of a day for the date 30th May, 2002
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Figure 5: 3ORWRIGDLO\WUDI¿FYROXPHvs hours of a day for the date 13th February, 1999
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Figure 6: 3ORWRIGDLO\WUDI¿FYROXPHvs hours of a day for the date 30th May, 2002
Journal of the National Science Foundation of Sri Lanka 45(3)
September 2017 1
244
Mohammed Saiful Alam Siddiquee & Kalum Priyanath Udagepola
3800 A ctual data (24/05/2002 to 30/05/2002) S im ulated by A N N S im ulated by S V M
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Actual data (12/02/1999 to 18/02/1999) Simulated by ANN Simulated by SVM
'DLO\WRWDOWUDI¿FYROXPH Daily Total Traffic Volume
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Figure 8: 3ORWRIGDLO\WRWDOWUDI¿FYROXPHvsGD\RIDZHHN WR of May, 2002)
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SVM generated data Hour 1 Hour 2 Hour 3 Hour 4 Hour 5 Hour 6 Hour 7 Hour 8 Hour 9 Hour 10 Hour 11 Hour 12 Actual Data Hour 6-12
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Figure 7: 3ORWRIGDLO\WRWDOWUDI¿FYROXPHvsGD\RIDZHHN WR of February, 1999)
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Figure 9: $11 JHQHUDWHG SORW RI KRXUO\ WUDI¿F YROXPH vs years (1999 to 2003)
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QXOOK\SRWKHVLVZDVUHMHFWHGDQGLWZDVFRQFOXGHGZLWK FRQ¿GHQFH WKDW WKHUH LV QR VLJQL¿FDQW GLIIHUHQFH in simulation by SVM and actual data. That means the SVM predicted the observed data exactly.
WUDI¿FYROXPHDQGGDLO\WUDI¿FYROXPH7KHUHIRUH690 WHFKQLTXH RI WUDI¿F YROXPH IRUHFDVWLQJ DSSHDUV WR EH more accurate.
All the predictions of data as presented in Figures 3 through 8 by SVM is almost 100 % accurate. In these ¿JXUHV DFWXDO GDWD SRLQWV DUH RYHUODSSHG E\ 690 simulated points. While predictions by ANN models are smooth, it could not capture the sudden jumps in actual WUDI¿F GDWD EXW WKH 690 FRXOG FDSWXUH DOO WKH GHWDLOV RIWKHWUDI¿FÀRZGDWD,WFRXOGFDSWXUHERWKWKHKRXUO\
September 2017
The potential of using ANN and SVM models to FDSWXUHDOOWKHSRVVLEOHWUHQGVLQVLGHWUDI¿FGDWDLVVKRZQ )LJXUHV DQGVKRZWKHYDULDWLRQRIKRXUO\WUDI¿FGDWD ZLWKLWV\HDUVRIRULJLQ,QWKLVZD\WKHLQGLYLGXDOJURZWK IDFWRURIHDFKKRXUO\WUDI¿FGDWDFDQEHVHHQ,WFDQEH REVHUYHGWKDWWKHKRXUO\YDULDWLRQDOVRFKDQJHVZLWKWKH \HDU)RUWKHFODULW\RIWKH¿JXUHRQO\XSWRKRXUVRI data are plotted.
Journal of the National Science Foundation of Sri Lanka 45(3)
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CONCLUSION 7KHIROORZLQJFRQFOXVLRQVDUHGHGXFHGIURPWKLVVWXG\ XVLQJ$11DQG690WRDVVLPLODWHVSRUDGLFWUDI¿FGDWD and predict missing data from the models: 1) The generated ANN and SVM models can capture WKHKRXUO\WUDI¿FGDWDSDWWHUQVDQGSUHGLFWWKHSDWWHUQ quite reasonably. 2) The trained ANN and SVM models can also capture WKHKRXUO\YDULDWLRQRIGDLO\WUDI¿FYROXPH 3) The ANN and SVM models predict the variation of GDLO\ WRWDO WUDI¿F YROXPH RYHU WKH GD\V RI D ZHHN quite successfully. 4) Above all, both methods can predict the hourly JURZWKUDWHRIWKHWUDI¿FYROXPH 5) SVM results are more accurate than ANN results in all respects. REFERENCES 1. $EGL - 0RVKLUL % $SSOLFDWLRQ RI WHPSRUDO GLIIHUHQFH OHDUQLQJ UXOHV LQ VKRUWWHUP WUDI¿F ÀRZ prediction. Expert Systems 32(1): 49 – 64. DOI: https://doi.org/10.1111/exsy.12055 2. %DUHZRRG-( 7UDI¿F(QJLQHHULQJ+DQGERRN, 3rd HGLWLRQ ,QVWLWXWH RI 7UDI¿F (QJLQHHUV :DVKLQJWRQ '& USA. 3. Basu D. (2006). Modeling stream speed in heterogeneous WUDI¿F HQYLURQPHQW XVLQJ $11OHVVRQV OHDUQW Transport 21: 269 – 273. 4. &HWLQHU%*6DUL0 %RUDW2 $QHXUDOQHWZRUN EDVHG WUDI¿FÀRZ SUHGLFWLRQ PRGHO Mathematical and Computational Applications 15: 269 – 278. DOI: https://doi.org/10.3390/mca15020269 5. &KLRX
