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Volumn 23, Issue 6, 2013, Pages 1611-1629

Identifying important variables for predicting travel time of freeway with non-recurrent congestion with neural networks

Author keywords

Electronic toll collection; Freeway; Neural networks; Non recurrent congestion; Travel time prediction

Indexed keywords

ELECTRONIC TOLL COLLECTION; ELECTRONIC TOLL COLLECTION SYSTEMS; INTELLIGENT TRANSPORTATION SYSTEMS; MULTI LAYER PERCEPTRON; NON-RECURRENT CONGESTION; PREDICTION CAPABILITY; TRAVEL TIME INFORMATION; TRAVEL TIME PREDICTION;

EID: 84885930791     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-012-1114-z     Document Type: Article
Times cited : (33)

References (49)
  • 1
    • 27744431971 scopus 로고    scopus 로고
    • Short-term travel time forecasts for transport information system in Hong Kong
    • Lam WHK, Chan KS, Tam ML, Shi JWZ (2005) Short-term travel time forecasts for transport information system in Hong Kong. J Adv Transp 39(3): 289-306.
    • (2005) J Adv Transp , vol.39 , Issue.3 , pp. 289-306
    • Lam, W.H.K.1    Chan, K.S.2    Tam, M.L.3    Shi, J.W.Z.4
  • 3
    • 2142708706 scopus 로고    scopus 로고
    • Measuring recurrent and nonrecurrent traffic congestion
    • Skabardonis A, Varaiya P, Petty KF (2003) Measuring recurrent and nonrecurrent traffic congestion. Transp Res Record 1856: 118-124.
    • (2003) Transp Res Record , vol.1856 , pp. 118-124
    • Skabardonis, A.1    Varaiya, P.2    Petty, K.F.3
  • 4
    • 27544497495 scopus 로고    scopus 로고
    • Analysis of freeway accident frequencies: negative binomial regression versus artificial neural network
    • Chang LY (2005) Analysis of freeway accident frequencies: negative binomial regression versus artificial neural network. Saf Sci 43(8): 541-557.
    • (2005) Saf Sci , vol.43 , Issue.8 , pp. 541-557
    • Chang, L.Y.1
  • 5
    • 57649214105 scopus 로고    scopus 로고
    • Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks
    • Santosh TV, Srivastava A, Sanyasi Rao VVS, Ghosh AK, Kushwaha HS (2009) Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks. Reliab Eng Syst Saf 94(3): 759-762.
    • (2009) Reliab Eng Syst Saf , vol.94 , Issue.3 , pp. 759-762
    • Santosh, T.V.1    Srivastava, A.2    Sanyasi Rao, V.V.S.3    Ghosh, A.K.4    Kushwaha, H.S.5
  • 6
    • 0037442845 scopus 로고    scopus 로고
    • Review and comparison of methods to study the contribution of variables in Artificial Neural Network models
    • Gevrey M, Dimopoulos I, Lek S (2003) Review and comparison of methods to study the contribution of variables in Artificial Neural Network models. Ecol Model 160(3): 249-264.
    • (2003) Ecol Model , vol.160 , Issue.3 , pp. 249-264
    • Gevrey, M.1    Dimopoulos, I.2    Lek, S.3
  • 7
    • 71949115970 scopus 로고    scopus 로고
    • Development of an accident duration prediction model on the Korean Freeway Systems
    • Chung Y (2010) Development of an accident duration prediction model on the Korean Freeway Systems. Accid Anal Prev 42(1): 282-289.
    • (2010) Accid Anal Prev , vol.42 , Issue.1 , pp. 282-289
    • Chung, Y.1
  • 8
    • 34548504071 scopus 로고    scopus 로고
    • Sequential forecast of incident duration using Artificial Neural Network models
    • Wei CH, Lee Y (2007) Sequential forecast of incident duration using Artificial Neural Network models. Accid Anal Prev 39(4): 944-954.
    • (2007) Accid Anal Prev , vol.39 , Issue.4 , pp. 944-954
    • Wei, C.H.1    Lee, Y.2
  • 9
    • 0036132002 scopus 로고    scopus 로고
    • Incident dispatching, clearance and delay
    • Hall RW (2002) Incident dispatching, clearance and delay. Transp Res Part A Policy Pract 36(1): 1-16.
    • (2002) Transp Res Part A Policy Pract , vol.36 , Issue.1 , pp. 1-16
    • Hall, R.W.1
  • 10
    • 0345375534 scopus 로고    scopus 로고
    • Dynamic travel time prediction with real-time and historic data
    • Chien SIJ, Kuchipudi CM (2003) Dynamic travel time prediction with real-time and historic data. J Transp Eng 129(6): 608-616.
    • (2003) J Transp Eng , vol.129 , Issue.6 , pp. 608-616
    • Chien, S.I.J.1    Kuchipudi, C.M.2
  • 11
    • 0037954189 scopus 로고    scopus 로고
    • A multivariate state space approach for urban traffic flow modeling and prediction
    • Stathopoulos A, Karlaftis MG (2003) A multivariate state space approach for urban traffic flow modeling and prediction. Transp Res Part C Emerg Technol 11(2): 121-135.
    • (2003) Transp Res Part C Emerg Technol , vol.11 , Issue.2 , pp. 121-135
    • Stathopoulos, A.1    Karlaftis, M.G.2
  • 12
    • 21244465823 scopus 로고    scopus 로고
    • Refining genetically designed models for improved traffic prediction on rural roads
    • Zhong M, Sharma S, Lingras P (2005) Refining genetically designed models for improved traffic prediction on rural roads. Transp Plan Technol 28(3): 213-236.
    • (2005) Transp Plan Technol , vol.28 , Issue.3 , pp. 213-236
    • Zhong, M.1    Sharma, S.2    Lingras, P.3
  • 13
    • 0018729076 scopus 로고
    • Analysis of freeway traffic time-series data by using Box-Jenkins techniques
    • Hamed MS, Cook AR (1979) Analysis of freeway traffic time-series data by using Box-Jenkins techniques. Transp Res Record 722: 1-9.
    • (1979) Transp Res Record , vol.722 , pp. 1-9
    • Hamed, M.S.1    Cook, A.R.2
  • 14
    • 0029308065 scopus 로고
    • Short-term prediction of traffic volume in urban arterials
    • Hamed MM, Al-Masaeid HR, Said ZM (1995) Short-term prediction of traffic volume in urban arterials. J Transp Eng 121(3): 249-254.
    • (1995) J Transp Eng , vol.121 , Issue.3 , pp. 249-254
    • Hamed, M.M.1    Al-Masaeid, H.R.2    Said, Z.M.3
  • 15
    • 0030298951 scopus 로고    scopus 로고
    • Combining Kohonen maps with ARIMA time series models to forecast traffic flow
    • Der Voort MV, Dougherty M, Watson S (1996) Combining Kohonen maps with ARIMA time series models to forecast traffic flow. Transp Res Part C Emerg Technol 4(5): 307-318.
    • (1996) Transp Res Part C Emerg Technol , vol.4 , Issue.5 , pp. 307-318
    • Der Voort, M.V.1    Dougherty, M.2    Watson, S.3
  • 16
    • 0003023581 scopus 로고    scopus 로고
    • Short-term traffic volume forecasting using radial basis function neural network
    • Park B, Messer CJ, Urbanik TII (1998) Short-term traffic volume forecasting using radial basis function neural network. Transp Res Record 1651: 39-47.
    • (1998) Transp Res Record , vol.1651 , pp. 39-47
    • Park, B.1    Messer, C.J.2    Urbanik, T.I.I.3
  • 17
    • 0033226152 scopus 로고    scopus 로고
    • Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting
    • Lee S, Fambro D (1999) Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting. Transp Res Record 1678: 179-188.
    • (1999) Transp Res Record , vol.1678 , pp. 179-188
    • Lee, S.1    Fambro, D.2
  • 18
    • 58349104545 scopus 로고    scopus 로고
    • Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions
    • Castro-Neto M, Jeong Y-S, Jeong M-K, Han LD (2009) Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions. Expert Syst Appl 36(3, Part 2): 6164-6173.
    • (2009) Expert Syst Appl , vol.36 , Issue.3 PART 2 , pp. 6164-6173
    • Castro-Neto, M.1    Jeong, Y.-S.2    Jeong, M.-K.3    Han, L.D.4
  • 20
    • 0019025588 scopus 로고
    • Use of the box and Jenkins time series technique in traffic forecasting
    • Nihan NL, Holmesland KO (1980) Use of the box and Jenkins time series technique in traffic forecasting. Transportation 9(2): 125-143.
    • (1980) Transportation , vol.9 , Issue.2 , pp. 125-143
    • Nihan, N.L.1    Holmesland, K.O.2
  • 21
    • 39849084754 scopus 로고    scopus 로고
    • Travel time estimation on a freeway using Discrete Time Markov Chains
    • Yeon J, Elefteriadou L, Lawphongpanich S (2008) Travel time estimation on a freeway using Discrete Time Markov Chains. Transp Res Part B Methodol 42(4): 325-338.
    • (2008) Transp Res Part B Methodol , vol.42 , Issue.4 , pp. 325-338
    • Yeon, J.1    Elefteriadou, L.2    Lawphongpanich, S.3
  • 22
    • 0032779078 scopus 로고    scopus 로고
    • Spectral basis neural networks for realtime travel time forecasting
    • Park D, Rilett L, Han G (1999) Spectral basis neural networks for realtime travel time forecasting. J Transp Eng 125(6): 515-523.
    • (1999) J Transp Eng , vol.125 , Issue.6 , pp. 515-523
    • Park, D.1    Rilett, L.2    Han, G.3
  • 23
    • 33646762818 scopus 로고    scopus 로고
    • Accurate freeway travel time prediction with state-space neural networks under missing data
    • van Lint JWC, Hoogendoorn SP, van Zuylen HJ (2005) Accurate freeway travel time prediction with state-space neural networks under missing data. Transp Res Part C Emerg Technol 13(5-6): 347-369.
    • (2005) Transp Res Part C Emerg Technol , vol.13 , Issue.5-6 , pp. 347-369
    • van Lint, J.W.C.1    Hoogendoorn, S.P.2    van Zuylen, H.J.3
  • 24
    • 25144524732 scopus 로고    scopus 로고
    • Short-term prediction of travel time using neural networks on an interurban freeway
    • Innamaa S (2005) Short-term prediction of travel time using neural networks on an interurban freeway. Transportation 32(6): 649-669.
    • (2005) Transportation , vol.32 , Issue.6 , pp. 649-669
    • Innamaa, S.1
  • 26
    • 40349102104 scopus 로고    scopus 로고
    • Online learning solutions for freeway travel time prediction
    • van Lint JWC (2008) Online learning solutions for freeway travel time prediction. Trans Intell Transp Syst 9(1): 38-47.
    • (2008) Trans Intell Transp Syst , vol.9 , Issue.1 , pp. 38-47
    • van Lint, J.W.C.1
  • 27
    • 80052718938 scopus 로고    scopus 로고
    • A Bayesian dynamic linear model approach for real-time short term freeway travel time prediction
    • Fei X, Lu CC, Liu K (2011) A Bayesian dynamic linear model approach for real-time short term freeway travel time prediction. Transp Res Part C Emerg Technol 19(6): 1306-1318.
    • (2011) Transp Res Part C Emerg Technol , vol.19 , Issue.6 , pp. 1306-1318
    • Fei, X.1    Lu, C.C.2    Liu, K.3
  • 28
    • 1642336343 scopus 로고    scopus 로고
    • Optimization of dynamic neural network performance for short-term traffic prediction
    • Ishak S, Kotha P, Alecsandru C (2003) Optimization of dynamic neural network performance for short-term traffic prediction. Transp Res Record 1836: 45-56.
    • (2003) Transp Res Record , vol.1836 , pp. 45-56
    • Ishak, S.1    Kotha, P.2    Alecsandru, C.3
  • 29
    • 3242676259 scopus 로고    scopus 로고
    • Optimizing traffic prediction performance of neural networks under various topological input, and traffic condition settings
    • Ishak S, Alecsandru C (2004) Optimizing traffic prediction performance of neural networks under various topological input, and traffic condition settings. J Transp Eng 130(4): 452-465.
    • (2004) J Transp Eng , vol.130 , Issue.4 , pp. 452-465
    • Ishak, S.1    Alecsandru, C.2
  • 30
    • 14744295830 scopus 로고    scopus 로고
    • Special factor adjustment model using fuzzy-neural network in traffic prediction
    • Xiao H, Sun H, Ran B, Oh Y (2004) Special factor adjustment model using fuzzy-neural network in traffic prediction. Transp Res Record 1879: 17-23.
    • (2004) Transp Res Record , vol.1879 , pp. 17-23
    • Xiao, H.1    Sun, H.2    Ran, B.3    Oh, Y.4
  • 31
    • 4544262864 scopus 로고    scopus 로고
    • A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed
    • Vanajakshi L, Rilett LR (2004) A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed. 2004 IEEE Intell Veh Symp 194-199.
    • (2004) 2004 IEEE Intell Veh Symp , pp. 194-199
    • Vanajakshi, L.1    Rilett, L.R.2
  • 32
    • 0035372068 scopus 로고    scopus 로고
    • An object-oriented neural network approach to short-term traffic forecasting
    • Dia H (2001) An object-oriented neural network approach to short-term traffic forecasting. Eur J Oper Res 131(2): 253-261.
    • (2001) Eur J Oper Res , vol.131 , Issue.2 , pp. 253-261
    • Dia, H.1
  • 33
    • 84878011320 scopus 로고    scopus 로고
    • Application of artificial neural networks for water quality prediction
    • doi:10.1007/s00521-012-0940-3
    • Najah A, El-Shafie A, Karim OA, El-Shafie AH (2012) Application of artificial neural networks for water quality prediction. Neural Comput Appl. doi: 10. 1007/s00521-012-0940-3.
    • (2012) Neural Comput Appl
    • Najah, A.1    El-Shafie, A.2    Karim, O.A.3    El-Shafie, A.H.4
  • 34
    • 84856000234 scopus 로고    scopus 로고
    • Predictability and forecasting automotive price based on a hybrid train algorithm of MLP neural network
    • Reza Peyghami M, Khanduzi R (2012) Predictability and forecasting automotive price based on a hybrid train algorithm of MLP neural network. Neural Comput Appl 21(1): 125-132.
    • (2012) Neural Comput Appl , vol.21 , Issue.1 , pp. 125-132
    • Reza Peyghami, M.1    Khanduzi, R.2
  • 35
    • 0042664078 scopus 로고    scopus 로고
    • Incident detection using support vector machines
    • Yuan F, Cheu RL (2003) Incident detection using support vector machines. Transp Res Part C Emerg Technol 11(3-4): 309-328.
    • (2003) Transp Res Part C Emerg Technol , vol.11 , Issue.3-4 , pp. 309-328
    • Yuan, F.1    Cheu, R.L.2
  • 36
    • 27544446036 scopus 로고    scopus 로고
    • Data mining of tree-based models to analyze freeway accident frequency
    • Chang LY, Chen WC (2005) Data mining of tree-based models to analyze freeway accident frequency. J Saf Res 36(4): 365-375.
    • (2005) J Saf Res , vol.36 , Issue.4 , pp. 365-375
    • Chang, L.Y.1    Chen, W.C.2
  • 37
    • 33745153428 scopus 로고    scopus 로고
    • Estimating dynamic roadway travel times using automatic vehicle identification data for low sampling rates
    • Dion F, Rakha H (2006) Estimating dynamic roadway travel times using automatic vehicle identification data for low sampling rates. Transp Res Part B Methodol 40(9): 745-766.
    • (2006) Transp Res Part B Methodol , vol.40 , Issue.9 , pp. 745-766
    • Dion, F.1    Rakha, H.2
  • 38
    • 77956953022 scopus 로고    scopus 로고
    • Application of automatic vehicle identification technology for real-time journey time estimation
    • Tam ML, Lam William HK (2010) Application of automatic vehicle identification technology for real-time journey time estimation. Information Fusion 12(1): 11-19.
    • (2010) Information Fusion , vol.12 , Issue.1 , pp. 11-19
    • Tam, M.L.1    Lam William, H.K.2
  • 39
    • 58349116224 scopus 로고    scopus 로고
    • A virtual vehicle probe model for time-dependent travel time estimation on signalized arterials
    • Liu Henry X, Ma W (2009) A virtual vehicle probe model for time-dependent travel time estimation on signalized arterials. Transp Res Part C Emerg Technol 17(1): 11-26.
    • (2009) Transp Res Part C Emerg Technol , vol.17 , Issue.1 , pp. 11-26
    • Liu Henry, X.1    Ma, W.2
  • 41
  • 43
    • 2042515742 scopus 로고    scopus 로고
    • Neural networks in business a survey of applications (1992-1998)
    • Vellido A, Lisboa PJG, Vaughan J (1999) Neural networks in business a survey of applications (1992-1998). Expert Syst Appl 17(1): 51-70.
    • (1999) Expert Syst Appl , vol.17 , Issue.1 , pp. 51-70
    • Vellido, A.1    Lisboa, P.J.G.2    Vaughan, J.3
  • 45
    • 77950312724 scopus 로고    scopus 로고
    • Driver's visual attention as a function of driving experience and visibility. Using a driving simulator to explore drivers' eye movements in day, night and rain driving
    • Konstantopoulos P, Chapman P, Crundall D (2010) Driver's visual attention as a function of driving experience and visibility. Using a driving simulator to explore drivers' eye movements in day, night and rain driving. Accid Anal Prev 42(3): 827-834.
    • (2010) Accid Anal Prev , vol.42 , Issue.3 , pp. 827-834
    • Konstantopoulos, P.1    Chapman, P.2    Crundall, D.3
  • 46
    • 79951949165 scopus 로고    scopus 로고
    • Prediction intervals to account for uncertainties in neural network predictions: methodology and application in bus travel time prediction
    • Mazloumi E, Rose G, Currie G, Moridpour S (2011) Prediction intervals to account for uncertainties in neural network predictions: methodology and application in bus travel time prediction. Eng Appl Artif Intell 24(3): 534-542.
    • (2011) Eng Appl Artif Intell , vol.24 , Issue.3 , pp. 534-542
    • Mazloumi, E.1    Rose, G.2    Currie, G.3    Moridpour, S.4
  • 48
    • 0037266090 scopus 로고    scopus 로고
    • Neural networks as statistical tools for business researchers
    • DeTienne KB, Detienne DH, Joshi SA (2003) Neural networks as statistical tools for business researchers. Organ Res Methods 6(2): 236-265.
    • (2003) Organ Res Methods , vol.6 , Issue.2 , pp. 236-265
    • DeTienne, K.B.1    Detienne, D.H.2    Joshi, S.A.3
  • 49
    • 79951775181 scopus 로고    scopus 로고
    • Statistical methods versus neural networks in transportation research: differences, similarities and some insights
    • Karlaftis MG, Vlahogianni EI (2011) Statistical methods versus neural networks in transportation research: differences, similarities and some insights. Transp Res Part C Emerg Technol 19(3): 387-399.
    • (2011) Transp Res Part C Emerg Technol , vol.19 , Issue.3 , pp. 387-399
    • Karlaftis, M.G.1    Vlahogianni, E.I.2


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