http://iet.metastore.ingenta.com
1887

Real-time estimation of freeway travel time with recurrent congestion based on sparse detector data

Real-time estimation of freeway travel time with recurrent congestion based on sparse detector data

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Intelligent Transport Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Loop detectors distributed on freeways are very vulnerable and could be damaged or malfunctioned due to improper sealing, pavement deterioration. This may lead to poor travel time estimation as most of existing methodologies require detailed data collected from numerous detectors along a specified freeway route. To address this problem, this study proposes an effective and reliable methodology for real-time freeway travel time estimation with data from sparse detectors. In contrast to the existing methods, the proposed methodology requires significantly less number of detectors but maintains fairy good performance on travel time estimation. The proposed methodology utilises a self-organised mapping algorithm to cluster the detectors with similar traffic patterns. The data collected from the representative detectors within each cluster is then employed to estimate the travel time based on a support vector regression model. The case studies conducted for three selected freeway routes in Northern California over 3 weeks demonstrate that the proposed methodology accurately captures the fluctuation of travel time induced by the variations of traffic states. The estimated results are exceptionally accurate with smaller mean errors and root-mean-squared errors compared with the benchmark values obtained from the well-known performance measurement system in California.

References

    1. 1)
      • F. Dion , H. Rakha .
        1. Dion, F., Rakha, H.: ‘Estimating spatial travel times using automatic vehicle identification data’. TRB Annual Meeting, January 2003, pp. 1216.
        . TRB Annual Meeting , 12 - 16
    2. 2)
      • F. Dion , H. Rakha .
        2. Dion, F., Rakha, H.: ‘Estimating dynamic roadway travel times using automatic vehicle identification data for low sampling rates’, Transp. Res. B Methodol., 2006, 40, (9), pp. 745766.
        . Transp. Res. B Methodol. , 9 , 745 - 766
    3. 3)
      • L.D. Vanajakshi , B.M. Williams , L.R. Rilett .
        3. Vanajakshi, L.D., Williams, B.M., Rilett, L.R.: ‘Improved flow-based travel time estimation method from point detector data for freeways’, J. Transp. Eng., 2009, 135, (1), pp. 2636.
        . J. Transp. Eng. , 1 , 26 - 36
    4. 4)
      • P. Li , R.R. Souleyrette .
        4. Li, P., Souleyrette, R.R.: ‘A generic approach to estimate freeway traffic time using vehicle ID-matching technologies’, Comput.-Aided Civ. Infrastruct. Eng., 2016, 31, (5), pp. 351365.
        . Comput.-Aided Civ. Infrastruct. Eng. , 5 , 351 - 365
    5. 5)
      • B.-S. Yoo , C.-H. Park , S.-P. Kang .
        5. Yoo, B.-S., Park, C.-H., Kang, S.-P.: ‘Travel time estimation using mobile data’. Proc. Eastern Asia Society for Transportation Studies, 2005, vol. 5, pp. 15331547.
        . Proc. Eastern Asia Society for Transportation Studies , 1533 - 1547
    6. 6)
      • A. Steiner .
        6. Steiner, A.: ‘A new method for travel time estimation on long freeway sections’, Eur. J. Transp. Infras. Res., 2008, 8, (4), pp. 333354.
        . Eur. J. Transp. Infras. Res. , 4 , 333 - 354
    7. 7)
      • A. Alessandri , R. Bolla , M. Repetto .
        7. Alessandri, A., Bolla, R., Repetto, M.: ‘Estimation of freeway traffic variables using information from mobile phones’. Proc. 2003 American Control Conf., June 2003, vol. 5, pp. 40894094.
        . Proc. 2003 American Control Conf. , 4089 - 4094
    8. 8)
      • C. Sun , S.G. Ritchie , K. Tsai .
        8. Sun, C., Ritchie, S.G., Tsai, K., et al: ‘Use of vehicle signature analysis and lexicographic optimization for vehicle reidentification on freeways’, Transp. Res. C, Emerg. Technol., 1999, 7, (4), pp. 167185.
        . Transp. Res. C, Emerg. Technol. , 4 , 167 - 185
    9. 9)
      • S.S. Moghaddam , B. Hellinga .
        9. Moghaddam, S.S., Hellinga, B.: ‘Real-time prediction of arterial roadway travel times using data collected by bluetooth detectors’. 2014 TRB Annual Meeting, January 2014, pp. 40894094.
        . 2014 TRB Annual Meeting , 4089 - 4094
    10. 10)
      • A. Haghani , M. Hamedi , K.F. Sadabadi .
        10. Haghani, A., Hamedi, M., Sadabadi, K.F., et al: ‘Data collection of freeway travel time ground truth with Bluetooth sensors’, Transp. Res. Rec., 2010, 2160, pp. 6068.
        . Transp. Res. Rec. , 60 - 68
    11. 11)
      • J.J. Vinagre Diaz , A.B. Rodriguez Gonzalez , M. Richard Wilby .
        11. Vinagre Diaz, J.J., Rodriguez Gonzalez, A.B., Richard Wilby, M.: ‘Bluetooth traffic monitoring systems for travel time estimation on freeways’, IEEE Trans. Intell. Transp. Syst., 2016, 17, (1), pp. 123132.
        . IEEE Trans. Intell. Transp. Syst. , 1 , 123 - 132
    12. 12)
      • I. Sanaullah , M. Quddus , M. Enoch .
        12. Sanaullah, I., Quddus, M., Enoch, M.: ‘Developing travel time estimation methods using sparse GPS data’, J. Intell. Transp. Syst., 2016, 20, (6), pp. 532544.
        . J. Intell. Transp. Syst. , 6 , 532 - 544
    13. 13)
      • A. Bhaskar , M. Qu , E. Chung .
        13. Bhaskar, A., Qu, M., Chung, E.: ‘Bluetooth vehicle trajectory by fusing bluetooth and loops: motorway travel time statistics’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (1), pp. 113122.
        . IEEE Trans. Intell. Transp. Syst. , 1 , 113 - 122
    14. 14)
      • K.F. Petty , P. Bickel , M. Ostland .
        14. Petty, K.F., Bickel, P., Ostland, M., et al: ‘Accurate estimation of travel times from single-loop detectors’, Transp. Res. A Policy Pract., 1998, 32, (1), pp. 117.
        . Transp. Res. A Policy Pract. , 1 , 1 - 17
    15. 15)
      • Z.J.Z. Jia , C.C.C. Chen , B. Coifman .
        15. Jia, Z.J.Z., Chen, C.C.C., Coifman, B., et al: ‘The PeMS algorithms for accurate, real-time estimates of g-factors and speeds from single-loop detectors’. 2001 IEEE Intelligent Transport Systems Proc. ITSC 2001, August 2001, pp. 536541.
        . 2001 IEEE Intelligent Transport Systems Proc. ITSC 2001 , 536 - 541
    16. 16)
      • J.W.C. Van Lint .
        16. Lint, J.W.C. Van: ‘An improved travel-time estimation algorithm using dual loop detectors’, Transp. Res. Rec., 2003, 1855, pp. 4148.
        . Transp. Res. Rec. , 41 - 48
    17. 17)
      • B. Coifman .
        17. Coifman, B.: ‘Estimating travel times and vehicle trajectories on freeways using dual loop detectors’, Transp. Res. A Policy Pract., 2002, 36, pp. 351364.
        . Transp. Res. A Policy Pract. , 351 - 364
    18. 18)
      • C. Chen , J. Kwon , J. Rice .
        18. Chen, C., Kwon, J., Rice, J., et al: ‘Detecting errors and imputing missing data for single-loop surveillance systems’, Transp. Res. Rec., 2003, 1855, pp. 160167.
        . Transp. Res. Rec. , 160 - 167
    19. 19)
      • J. Tang , Y. Zou , J. Ash .
        19. Tang, J., Zou, Y., Ash, J., et al: ‘Travel time estimation using freeway point detector data based on evolving fuzzy neural inference system’, PLoS One, 2016, 11, (2), p. 0147263.
        . PLoS One , 2 , 0147263
    20. 20)
      • U. Mori , A. Mendiburu , M. Álvarez .
        20. Mori, U., Mendiburu, A., Álvarez, M., et al: ‘A review of travel time estimation and forecasting for advanced traveller information systems’, Transp. A Transp. Sci., 2015, 11, (2), pp. 119157.
        . Transp. A Transp. Sci. , 2 , 119 - 157
    21. 21)
      • D.H. Nam , D.R. Drew .
        21. Nam, D.H., Drew, D.R.: ‘Traffic dynamics: method for estimating freeway travel times in real time from flow measurements’, J. Transp. Eng., 1996, 122, (3), pp. 185191.
        . J. Transp. Eng. , 3 , 185 - 191
    22. 22)
      • D.H. Nam , D.R. Drew .
        22. Nam, D.H., Drew, D.R.: ‘Analyzing freeway traffic under congestion: traffic dynamics approach’, J. Transp. Eng., 1998, 124, (3), pp. 208212.
        . J. Transp. Eng. , 3 , 208 - 212
    23. 23)
      • H.B. Celikoglu .
        23. Celikoglu, H.B.: ‘Flow-based freeway travel-time estimation: a comparative evaluation within dynamic path loading’, IEEE Trans. Intell. Transp. Syst., 2013, 14, (2), pp. 772781.
        . IEEE Trans. Intell. Transp. Syst. , 2 , 772 - 781
    24. 24)
      • H.B. Celikoglu .
        24. Celikoglu, H.B.: ‘Reconstructing freeway travel times with a simplified network flow model alternating the adopted fundamental diagram’, Eur. J. Oper. Res., 2013, 228, (2), pp. 457466.
        . Eur. J. Oper. Res. , 2 , 457 - 466
    25. 25)
      • H.B. Celikoglu .
        25. Celikoglu, H.B.: ‘Travel time measure specification by functional approximation: application of radial basis function neural networks’, Proc Soc. Behav. Sci., 2011, 20, pp. 613620.
        . Proc Soc. Behav. Sci. , 613 - 620
    26. 26)
      • J. Kwon , K. Petty , P. Varaiya .
        26. Kwon, J., Petty, K., Varaiya, P.: ‘Probe vehicle runs or loop detectors?: effect of detector spacing and sample size on accuracy of freeway congestion monitoring’, Transp. Res. Rec., 2008, 2012, pp. 5763.
        . Transp. Res. Rec. , 57 - 63
    27. 27)
      • X.J. Ban , E. Engineering .
        27. Ban, X.J., Engineering, E.: ‘Optimal sensor placement for freeway travel time estimation’, Transp. Traffic Theory, 2009, 7, pp. 697721.
        . Transp. Traffic Theory , 697 - 721
    28. 28)
      • X. Ban , L. Chu , R. Herring .
        28. Ban, X., Chu, L., Herring, R., et al: ‘Sequential modeling framework for optimal sensor placement for multiple intelligent transportation system applications’, J. Transp. Eng., 2011, 137, (2), pp. 112120.
        . J. Transp. Eng. , 2 , 112 - 120
    29. 29)
      • R.L. Bertini , D.J. Lovell .
        29. Bertini, R.L., Lovell, D.J.: ‘Impacts of sensor spacing on accurate freeway travel time estimation for traveler information’, J. Intell. Transp. Syst., 2009, 13, (2), pp. 97110.
        . J. Intell. Transp. Syst. , 2 , 97 - 110
    30. 30)
      • B. Bartin , K. Ozbay , C. Lyigun .
        30. Bartin, B., Ozbay, K., Lyigun, C.: ‘Clustering-based methodology for determining optimal roadway configuration of detectors for travel time estimation’, Transp. Res. Rec., 2007, 2000, pp. 98105.
        . Transp. Res. Rec. , 98 - 105
    31. 31)
      • J. Kianfar , P. Edara .
        31. Kianfar, J., Edara, P.: ‘Optimizing freeway traffic sensor locations by clustering global-positioning-system-derived speed patterns’, IEEE Trans. Intell. Transp. Syst., 2010, 11, (3), pp. 738747.
        . IEEE Trans. Intell. Transp. Syst. , 3 , 738 - 747
    32. 32)
      • A. Bhaskar , M. Qu , E. Chung .
        32. Bhaskar, A., Qu, M., Chung, E.: ‘A hybrid model for motorway travel time estimation: considering increased detector spacing’, Transp. Res. Rec., 2014, 2442, pp. 7184.
        . Transp. Res. Rec. , 71 - 84
    33. 33)
      • M. Treiber , D. Helbing .
        33. Treiber, M., Helbing, D.: ‘Reconstructing the spatio-temporal traffic dynamics from stationary detector data’, Cooper Transp. Dyn., 2002, 1, (3), pp. 124.
        . Cooper Transp. Dyn. , 3 , 1 - 24
    34. 34)
      • H.B. Celikoglu , M.A. Silgu .
        34. Celikoglu, H.B., Silgu, M.A.: ‘Extension of traffic flow pattern dynamic classification by a macroscopic model using multivariate clustering’, Transp. Sci.., 2016, 50, (3), pp. 966981.
        . Transp. Sci.. , 3 , 966 - 981
    35. 35)
      • H. Yin , S.C.C. Wong , J. Xu .
        35. Yin, H., Wong, S.C.C., Xu, J., et al: ‘Urban traffic flow prediction using a fuzzy-neural approach’, Transp. Res. C Emerg. Technol., 2002, 10, (2), pp. 8598.
        . Transp. Res. C Emerg. Technol. , 2 , 85 - 98
    36. 36)
      • M.A. Silgu , H.B. Celikoglu .
        36. Silgu, M.A., Celikoglu, H.B.: ‘Clustering traffic flow patterns by fuzzy C-means method: some preliminary findings’. Computer Aided Systems Theory – EUROCAST, 2015, vol. 9520, pp. 756764.
        . Computer Aided Systems Theory – EUROCAST , 756 - 764
    37. 37)
      • P.V. Palacharla , P.C. Nelson .
        37. Palacharla, P.V., Nelson, P.C.: ‘Application of fuzzy logic and neural networks for dynamic travel time estimation’, Int. Trans. Oper. Res., 1999, 6, (1), pp. 145160.
        . Int. Trans. Oper. Res. , 1 , 145 - 160
    38. 38)
      • M.T. Asif , J. Dauwels , C.Y. Goh .
        38. Asif, M.T., Dauwels, J., Goh, C.Y., et al: ‘Spatiotemporal patterns in large-scale traffic speed prediction’, IEEE Trans. Intell. Transp. Syst., 2014, 15, (2), pp. 794804.
        . IEEE Trans. Intell. Transp. Syst. , 2 , 794 - 804
    39. 39)
      • H.B. Celikoglu .
        39. Celikoglu, H.B.: ‘An approach to dynamic classification of traffic flow patterns’, Comput. Aided Civ. Infrastruct. Eng., 2013, 28, (4), pp. 273288.
        . Comput. Aided Civ. Infrastruct. Eng. , 4 , 273 - 288
    40. 40)
      • H.B. Celikoglu .
        40. Celikoglu, H.B.: ‘Dynamic classification of traffic flow patterns simulated by a switching multimode discrete cell transmission model’, IEEE Trans. Intell. Transp. Syst., 2014, 15, (6), pp. 25392550.
        . IEEE Trans. Intell. Transp. Syst. , 6 , 2539 - 2550
    41. 41)
      • C. Chen .
        41. Chen, C.: ‘Freeway performance measurement system (PeMS)’, Calif. Partners Adv. Transit Highw., 2003, pp. 1517.
        . Calif. Partners Adv. Transit Highw. , 15 - 17
    42. 42)
      • T. Kohonen .
        42. Kohonen, T.: ‘The self-organizing map’, Neurocomputing, 1998, 21, (1), pp. 16.
        . Neurocomputing , 1 , 1 - 6
    43. 43)
      • J. Vesanto , E. Alhoniemi .
        43. Vesanto, J., Alhoniemi, E.: ‘Clustering of the self-organizing map’, IEEE Trans. Neural Netw., 2000, 11, (3), pp. 586600.
        . IEEE Trans. Neural Netw. , 3 , 586 - 600
    44. 44)
      • C.-C. Chang , C.-J. Lin .
        44. Chang, C.-C., Lin, C.-J.: ‘LIBSVM: a library for support vector machines’, ACM Trans. Intell. Syst. Technol., 2011, 2, (3), pp. 127.
        . ACM Trans. Intell. Syst. Technol. , 3 , 1 - 27
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2016.0356
Loading

Related content

content/journals/10.1049/iet-its.2016.0356
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address