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

Deep learning methods in transportation domain: a review

Deep learning methods in transportation domain: a review

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 Title Publication 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.

Recent years have seen a significant amount of transportation data collected from multiple sources including road sensors, probe, GPS, CCTV and incident reports. Similar to many other industries, transportation has entered the generation of big data. With a rich volume of traffic data, it is challenging to build reliable prediction models based on traditional shallow machine learning methods. Deep learning is a new state-of-the-art machine learning approach which has been of great interest in both academic research and industrial applications. This study reviews recent studies of deep learning for popular topics in processing traffic data including transportation network representation, traffic flow forecasting, traffic signal control, automatic vehicle detection, traffic incident processing, travel demand prediction, autonomous driving and driver behaviours. In general, the use of deep learning systems in transportation is still limited and there are potential limitations for utilising this advanced approach to improve prediction models.

References

    1. 1)
      • 1. Soysal, M., Schmidt, E.G.: ‘Machine learning algorithms for accurate flow-based network traffic classification: evaluation and comparison’, Perform. Eval., 2010, 67, (6), pp. 451467.
    2. 2)
      • 2. Nguyen, H., Cai, C., Chen, F.: ‘Automatic classification of traffic incident's severity using machine learning approaches’, IET Intell. Transp. Syst., 2017, 11, (10), pp. 615623.
    3. 3)
      • 3. Fusco, G., Colombaroni, C., Comelli, L., et al: ‘Short-term traffic predictions on large urban traffic networks: applications of network-based machine learning models and dynamic traffic assignment models’. 2015 Int. Conf. Models and Technologies for Intelligent Transportation Systems (MT-ITS), Budapest, Hungary, 3 June 2015, pp. 93101.
    4. 4)
      • 4. Ellis, K., Godbole, S., Marshall, S., et al: ‘Identifying active travel behaviors in challenging environments using GPS, accelerometers, and machine learning algorithms’, Front. Public Health, 2014, 2, pp. 3946.
    5. 5)
      • 5. Jahangiri, A., Rakha, H., Dingus, T.A.: ‘Predicting red-light running violations at signalized intersections using machine learning techniques’. Transportation Research Board 94th Annual Meeting, 2015(No. 15-2910).
    6. 6)
      • 6. Lv, Y., Duan, Y., Kang, W., et al: ‘Traffic flow prediction with big data: a deep learning approach’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (2), pp. 865873.
    7. 7)
      • 7. LeCun, Y., Bengio, Y., Hinton, G.: ‘Deep learning’, Nature, 2015, 521, (7553), pp. 436444.
    8. 8)
      • 8. Farabet, C., Couprie, C., Najman, L., et al: ‘Learning hierarchical features for scene labeling’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (8), pp. 19151929.
    9. 9)
      • 9. Tompson, J.J., Jain, A., LeCun, Y., et al: ‘Joint training of a convolutional network and a graphical model for human pose estimation’. Advances in Neural Information Processing Systems, Montréal, Canada, 2014, pp. 17991807.
    10. 10)
      • 10. Collobert, R., Weston, J., Bottou, L., et al: ‘Natural language processing (almost) from scratch’, J. Mach. Learn. Res., 2011, 12, pp. 24932537.
    11. 11)
      • 11. Bordes, A., Chopra, S., Weston, J.: ‘Question answering with subgraph embeddings’. arXiv preprint arXiv:1406.3676, 14 June 2014.
    12. 12)
      • 12. Hinton, G., Deng, L., Yu, D., et al: ‘Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups’, IEEE Signal Process. Mag., 2012, 29, (6), pp. 8297.
    13. 13)
      • 13. Sainath, T.N., Mohamed, A.R., Kingsbury, B., et al: ‘Deep convolutional neural networks for LVCSR’. 2013 IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), Vancouver, Canada, 26 May 2013, pp. 86148618.
    14. 14)
      • 14. Leung, M.K., Xiong, H.Y., Lee, L.J., et al: ‘Deep learning of the tissue-regulated splicing code’, Bioinformatics, 2014, 30, (12), p. i1219.
    15. 15)
      • 15. Xiong, H.Y., Alipanahi, B., Lee, L.J., et al: ‘The human splicing code reveals new insights into the genetic determinants of disease’, Science, 2015, 347, (6218), p. 1254806.
    16. 16)
      • 16. Schmidhuber, J.: ‘Deep learning in neural networks: an overview’, Neural Netw., 2015, 61, pp. 85117.
    17. 17)
      • 17. Graves, A., Mohamed, A.R., Hinton, G.: ‘Speech recognition with deep recurrent neural networks’. 2013 IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), Vancouver, Canada, 26 May 2013, pp. 66456649.
    18. 18)
      • 18. Simonyan, K., Zisserman, A.: ‘Very deep convolutional networks for large-scale image recognition’. arXiv preprint arXiv:1409.1556, 4 September 2014.
    19. 19)
      • 19. Goodfellow, I., Mirza, M., Courville, A., et al: ‘Multi-prediction deep Boltzmann machines’. Advances in Neural Information Processing Systems, Lake Tahoe, CA, USA, 2013, pp. 548556.
    20. 20)
      • 20. Gehring, J., Miao, Y., Metze, F., et al: ‘Extracting deep bottleneck features using stacked auto-encoders’. 2013 IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), Vancouver, Canada, 26 May 2013, pp. 33773381.
    21. 21)
      • 21. Sarikaya, R., Hinton, G.E., Deoras, A.: ‘Application of deep belief networks for natural language understanding’, IEEE/ACM Trans. Audio Speech Lang. Process. (TASLP), 2014, 22, (4), pp. 778784.
    22. 22)
      • 22. Sainath, T.N., Vinyals, O., Senior, A.: ‘Convolutional, long short-term memory, fully connected deep neural networks’. 2015 IEEE Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), Brisbane, Queensland, Australia, 19 April 2015, pp. 45804584.
    23. 23)
      • 23. Ma, X., Yu, H., Wang, Y., et al: ‘Large-scale transportation network congestion evolution prediction using deep learning theory’, PLoS ONE, 2015, 10, (3), p. e0119044.
    24. 24)
      • 24. Fouladgar, M., Parchami, M., Elmasri, R., et al: ‘Scalable deep traffic flow neural networks for urban traffic congestion prediction’. arXiv preprint arXiv:1703.01006, 3 March 2017.
    25. 25)
      • 25. Lippi, M., Bertini, M., Frasconi, P.: ‘Short-term traffic flow forecasting: An experimental comparison of time-series analysis and supervised learning’, IEEE Trans. Intell. Transp. Syst., 2013, 14, (2), pp. 871882.
    26. 26)
      • 26. Huang, W., Song, G., Hong, H., et al: ‘Deep architecture for traffic flow prediction: deep belief networks with multitask learning’, IEEE Trans. Intell. Transp. Syst., 2014, 15, (5), pp. 21912201.
    27. 27)
      • 27. Hinton, G.E., Osindero, S., Teh, Y.W.: ‘A fast learning algorithm for deep belief nets’, Neural Comput., 2006, 18, (7), pp. 15271554.
    28. 28)
      • 28. Caruana, R.: ‘Multitask learning’. Machine learning, 1997, 28, (1), pp. 4175.
    29. 29)
      • 29. Jin, F., Sun, S.: ‘Neural network multitask learning for traffic flow forecasting’. IEEE Int. Joint Conf. Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence), Hong Kong, China, 1 June 2008, pp. 18971901.
    30. 30)
      • 30. Smith, B.L., Williams, B.M., Oswald, R.K.: ‘Comparison of parametric and nonparametric models for traffic flow forecasting’, Transp. Res. C Emerg. Technol., 2002, 10, (4), pp. 303321.
    31. 31)
      • 31. Polson, N.G., Sokolov, V.O.: ‘Deep learning for short-term traffic flow prediction’, Transp. Res. C Emerg. Technol., 2017, 79, pp. 17.
    32. 32)
      • 32. Zhao, Z., Chen, W., Wu, X., et al: ‘LSTM network: a deep learning approach for short-term traffic forecast’, IET Intell. Transp. Syst., 2017, 11, (2), pp. 6875.
    33. 33)
      • 33. Balaji, P.G., German, X., Srinivasan, D.: ‘Urban traffic signal control using reinforcement learning agents’, IET Intell. Transp. Syst., 2010, 4, (3), pp. 177188.
    34. 34)
      • 34. Prashanth, L.A., Bhatnagar, S.: ‘Reinforcement learning with function approximation for traffic signal control’, IEEE Trans. Intell. Transp. Syst., 2011, 12, (2), pp. 412421.
    35. 35)
      • 35. El-Tantawy, S., Abdulhai, B., Abdelgawad, H.: ‘Multiagent reinforcement learning for integrated network of adaptive traffic signal controllers (MARLIN-ATSC): methodology and large-scale application on downtown Toronto’, IEEE Trans. Intell. Transp. Syst., 2013, 14, (3), pp. 11401150.
    36. 36)
      • 36. Genders, W., Razavi, S.: ‘Using a deep reinforcement learning agent for traffic signal control’. arXiv preprint arXiv:1611.01142, 3 November 2016.
    37. 37)
      • 37. Mnih, V., Kavukcuoglu, K., Silver, D., et al: ‘Human-level control through deep reinforcement learning’, Nature, 2015, 518, (7540), pp. 529533.
    38. 38)
      • 38. van der Pol, E.: ‘Deep reinforcement learning for coordination in traffic light control’. Doctoral dissertation, Master's thesis, University of Amsterdam.
    39. 39)
      • 39. Li, L., Lv, Y., Wang, F.Y.: ‘Traffic signal timing via deep reinforcement learning’, IEEE/CAA J. Autom. Sin., 2016, 3, (3), pp. 247254.
    40. 40)
      • 40. Konoplich, G.V., Putin, E.O., Filchenkov, A.A.: ‘Application of deep learning to the problem of vehicle detection in UAV images’. XIX IEEE Int. Conf. Soft Computing and Measurements (SCM), St. Petersburg, Russia, 2016, pp. 46.
    41. 41)
      • 41. Zhou, Y., Cheung, N.-M.: ‘Vehicle classification using transferable deep neural network features’. arXiv, vol. 1601, 2016.
    42. 42)
      • 42. Moussa, G.S.: ‘Vehicle type classification with geometric and appearance attributes’, Int. J. Civil Archit. Sci. Eng., 2014, 8, (3), pp. 273278.
    43. 43)
      • 43. Wang, H., Cai, Y., Chen, L.: ‘A vehicle detection algorithm based on deep belief network’, Sci. World J., 2014, 2014, pp. 17.
    44. 44)
      • 44. Adu-Gyamfi, Y.O., Asare, S.K., Sharma, A., et al: ‘Automated vehicle recognition with deep convolutional neural networks’, 2017.
    45. 45)
      • 45. Cheng, Q., Liu, Y., Wei, W., et al: ‘Analysis and forecasting of the day-to-day travel demand variations for large-scale transportation networks: a deep learning approach’. Proc. Annual Meeting of the Transportation Research Board, Washington, DC, USA, January 2017.
    46. 46)
      • 46. Ke, J., Zheng, H., Yang, H., et al: ‘Short-term forecasting of passenger demand under on-demand ride services: a spatio-temporal deep learning approach’, Transp. Res. C Emerg. Technol., 2017, 85, pp. 591608.
    47. 47)
      • 47. Zhu, X., Li, J., Liu, Z., et al: ‘Location deployment of depots and resource relocation for connected car-sharing systems through mobile edge computing’, Int. J. Distrib. Sens. Netw., 2017, 13, (6), p. 1550147717711621.
    48. 48)
      • 48. Yao, H., Wu, F., Ke, J., et al: ‘Deep multi-view spatial-temporal network for taxi demand prediction’. Proc. Thirty-Second AAAI Conf. Artificial Intelligence (AAAI 2018), New Orleans, LA, February 2018.
    49. 49)
      • 49. Xu, J., Rahmatizadeh, R., Bölöni, L., et al: ‘Real-time prediction of taxi demand using recurrent neural networks’, IEEE Trans. Intell. Transp. Syst., 2017, pp. 110.
    50. 50)
      • 50. Zhu, L., Laptev, N.: ‘Deep and confident prediction for time series at uber’. 2017 IEEE Int. Conf. Data Mining Workshops (ICDMW), New Orleans, Louisiana, 18 November 2017, pp. 103110.
    51. 51)
      • 51. Liu, L., Chen, R.C.: ‘A MRT daily passenger flow prediction model with different combinations of influential factors’. 2017 31st Int. Conf. Advanced Information Networking and Applications Workshops (WAINA), Taipei, Taiwan, 27 March 2017, pp. 601605.
    52. 52)
      • 52. Baek, J., Sohn, K.: ‘Deep-learning architectures to forecast bus ridership at the stop and stop-to-stop levels for dense and crowded bus networks’, Appl. Artif. Intell., 2016, 30, (9), pp. 861885.
    53. 53)
      • 53. Bengio, Y.: ‘Learning deep architectures for AI’, Trends Mach. Learn., 2009, 2, (1), pp. 127.
    54. 54)
      • 54. Chen, Q., Song, X., Yamada, H., et al: ‘Learning deep representation from big and heterogeneous data for traffic accident inference’. AAAI, Toronto, Ontario, Canada, 12 February 2016, pp. 338344.
    55. 55)
      • 55. Chen, C., Xiang, H., Qiu, T., et al: ‘A rear-end collision prediction scheme based on deep learning in the Internet of vehicles’, J. Parallel Distrib. Comput., 2017, 117, pp. 192204.
    56. 56)
      • 56. Wan, J., Wang, D., Hoi, S.C., et al: ‘Deep learning for content-based image retrieval: A comprehensive study’. Proc. 22nd ACM Int. Conf. Multimedia, Orlando, FL, USA, 3 November 2014, pp. 157166.
    57. 57)
      • 57. Huval, B., Wang, T., Tandon, S., et al: ‘An empirical evaluation of deep learning on highway driving’. arXiv preprint arXiv:1504.01716, 7 April 2015.
    58. 58)
      • 58. Hadsell, R., Erkan, A., Sermanet, P., et al: ‘Deep belief net learning in a long-range vision system for autonomous off-road driving’. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, 2008. IROS 2008, Nice, France, 22 September 2008, pp. 628633.
    59. 59)
      • 59. Dong, W., Li, J., Yao, R., et al: ‘Characterizing driving styles with deep learning’. arXiv preprint arXiv:1607.03611, 13 July 2016.
    60. 60)
      • 60. Dwivedi, K., Biswaranjan, K., Sethi, A.: ‘Drowsy driver detection using representation learning’. 2014 IEEE Int. Advance Computing Conf. (IACC), Gurgaon, India, 21 February 2014, pp. 995999.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2018.0064
Loading

Related content

content/journals/10.1049/iet-its.2018.0064
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address