Skip to main content
SpringerLink
Log in

Real-time signal queue length prediction using long short-term memory neural network

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Optimal traffic control and signal planning can significantly reduce traffic congestion and potential delays at intersections. However, a major challenge to optimize traffic signal timing is to accurately predict traffic before commencing the next cycle. An optimal strategy cannot be achieved with a poor prediction of future traffic. In this study, using a deep learning approach, we develop a data-driven real-time queue length prediction technique. We consider a connected corridor where information from vehicle detectors (located at the intersection) will be shared to consecutive intersections. We assume that the queue length of an intersection in the next cycle will depend on the queue length of the target and two upstream intersections in the current cycle. We use InSync adaptive traffic control system data to train a long short-term memory neural network model capturing time-dependent patterns of a queue of a signal. To avoid overfitting and select the best combination of hyperparameters, we use sequential model-based optimization technique. Our experiment results show that the proposed model performs very well to predict the queue length. Although we run our experiments predicting the queue length for a single movement, the proposed method can be applied for other movements as well.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Schrank D, Eisele B, Lomax T, Bak J (2015) Urban mobility scorecard. Texas A&M Transportation Institute, vol 39, August, 2015. https://doi.org/DTRT06-G-0044

  2. Smith SF, Barlow GJ, Xie X-F, Rubinstein ZB (2013) Smart urban signal networks: initial application of the SURTRAC adaptive traffic signal control system. In: Icaps, pp 434–442

  3. Chandra R, Gregory C (2012) InSync adaptive traffic signal technology: real-time artificial intelligence delivering real-world results. In: InSync White Paper, No. March 2012

  4. Liu HX, Wu X, Ma W, Hu H (2009) Real-time queue length estimation for congested signalized intersections. Transp Res Part C: Emerg Technol 17(4):412–427. https://doi.org/10.1016/j.trc.2009.02.003

    Article  Google Scholar 

  5. Feng Y, Head KL, Khoshmagham S, Zamanipour M (2015) A real-time adaptive signal control in a connected vehicle environment. Transp Res Part C: Emerg Technol 55:460–473. https://doi.org/10.1016/j.trc.2015.01.007

    Article  Google Scholar 

  6. Jing P, Huang H, Chen L (2017) An adaptive traffic signal control in a connected vehicle environment: a systematic review. Information (Switzerland). https://doi.org/10.3390/info8030101

    Article  Google Scholar 

  7. Mirchandani P, Head L (2001) A real-time traffic signal control system: architecture, algorithms, and analysis. Transportation Research Part C: Emerging Technologies 9(6):415–432. https://doi.org/10.1016/S0968-090X(00)00047-4

    Article  Google Scholar 

  8. Miyato T, Dai AM, Goodfellow I (2019) Adversarial training methods for semi-supervised text classification. In: 5th international conference on learning representations, ICLR 2017—conference track proceedings, pp 1–11

  9. Rosen-Zvi M, Griffiths T, Steyvers M, Smyth P (2012) The author-topic model for authors and documents. arXiv preprint arXiv:1207.4169

  10. Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv. https://doi.org/10.1145/1177352.1177355

    Article  Google Scholar 

  11. Finn C, Goodfellow I, Levine S (2016). Unsupervised learning for physical interaction through video prediction. In: Advances in neural information processing systems, No Nips, pp 64–72

  12. Bi J, Yuan H, Zhou M (2019) Temporal Prediction of Multiapplication Consolidated Workloads in Distributed Clouds. IEEE Trans Autom Sci Eng 16(4):1763–1773. https://doi.org/10.1109/TASE.2019.2895801

    Article  Google Scholar 

  13. Drucker H, Surges CJC, Kaufman L, Smola A, Vapnik V (1997) Support vector regression machines. Adv Neural Inf Process Syst 1:155–161

    Google Scholar 

  14. Cortes C, Vapnik V (1995) Support-vector networks 1: introduction. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  15. Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46(3):175–185

    MathSciNet  Google Scholar 

  16. Werbos PJ (1990) Backpropagation through time: what it does and how to do it. Proc IEEE 78(10):1550–1560. https://doi.org/10.1109/5.58337

    Article  Google Scholar 

  17. Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci USA 79(April):2254–2558. https://doi.org/10.1201/9780429500459

    Article  MathSciNet  MATH  Google Scholar 

  18. Geurts M, Box GEP, Jenkins GM (1977) Time series analysis: forecasting and control. Wiley, New York, p 1977

    Google Scholar 

  19. Yu B, Song X, Guan F, Yang Z, Yao B (2016) K-nearest neighbor model for multiple-time-step prediction of short-term traffic condition. J Transp Eng 142(6):04016018. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000816

    Article  Google Scholar 

  20. Deshpande M, Bajaj PR (2016) Performance analysis of support vector machine for traffic flow prediction. In: 2016 international conference on global trends in signal processing, information computing and communication (ICGTSPICC), 2016, pp 126–129. https://doi.org/10.1109/ICGTSPICC.2016.7955283

  21. Wu C, Wei C, Su D, Chang M, Ho J (2004). Travel time prediction with support vector regression. In: Proceedings of the 2003 IEEE international conference on intelligent transportation systems, vol 2, pp 1438–1442. https://doi.org/10.1109/ITSC.2003.1252721

  22. Billings D, Jiann-Shiou Y (2006) Application of the ARIMA models to urban roadway travel time prediction—a case study. In: IEEE international conference on systems, man and cybernetics (SMC’06) 2006, pp 2529–2534

  23. Lee YLY (2009) Freeway travel time forecast using artifical neural networks with cluster method. In: 2009 12th international conference on information fusion, pp 1331–1338

  24. Yasdi R (1999) Prediction of road traffic using a neural network approach. Neural Comput Appl 8(2):135–142. https://doi.org/10.1007/s005210050015

    Article  Google Scholar 

  25. Hong WC (2012) Application of seasonal SVR with chaotic immune algorithm in traffic flow forecasting. Neural Comput Appl 21(3):583–593. https://doi.org/10.1007/s00521-010-0456-7

    Article  Google Scholar 

  26. Hodge VJ, Krishnan R, Austin J, Polak J, Jackson T (2014) Short-term prediction of traffic flow using a binary neural network. Neural Comput Appl 25(7–8):1639–1655. https://doi.org/10.1007/s00521-014-1646-5

    Article  Google Scholar 

  27. Polson NG, Sokolov VO (2017) Deep learning for short-term traffic flow prediction. Transp Res Part C: Emerg Technol 79:1–17. https://doi.org/10.1016/j.trc.2017.02.024

    Article  Google Scholar 

  28. Ai Y, Li Z, Gan M, Zhang Y, Yu D, Chen W, Ju Y (2019) A deep learning approach on short-term spatiotemporal distribution forecasting of dockless bike-sharing system. Neural Comput Appl 31(5):1665–1677. https://doi.org/10.1007/s00521-018-3470-9

    Article  Google Scholar 

  29. Shukla S, Balachandran K, Sumitha VS (2017) A Framework for smart transportation using big data. In: Proceedings of 2016 international conference on ICT in business, industry, and government, ICTBIG 2016, pp 1–3. https://doi.org/10.1109/ICTBIG.2016.7892720

  30. Ma X, Tao Z, Wang Y, Yu H, Wang Y (2015) Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transp Res Part C: Emerg Technol 54:187–197. https://doi.org/10.1016/j.trc.2015.03.014

    Article  Google Scholar 

  31. Yu H, Wu Z, Wang S, Wang Y, Ma X (2017) Spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks. Sensors (Switzerland) 17(7):1–16. https://doi.org/10.3390/s17071501

    Article  Google Scholar 

  32. Rahman R, Hasan S (2018). Short-term traffic speed prediction for freeways during hurricane evacuation: a deep learning approach. pp 1291–1296. https://doi.org/10.1109/ITSC.2018.8569443

  33. Newell GF (1965) Approximation methods for queues with application to the fixed-cycle traffic light. Soc Ind Appl Math 7(2):223–240

    MathSciNet  MATH  Google Scholar 

  34. Chang TH, Lin JT (2000) Optimal signal timing for an oversaturated intersection. Transp Res Part B: Methodol 34(6):471–491. https://doi.org/10.1016/S0191-2615(99)00034-X

    Article  Google Scholar 

  35. Mirchandani PB, Zou N (2007) Queuing models for analysis of traffic adaptive signal control. IEEE Trans Intell Transp Syst 8(1):50–59. https://doi.org/10.1109/TITS.2006.888619

    Article  Google Scholar 

  36. Balke KN, Charara H, Parker R (2005). Development of a traffic signal performance measurement system (TSPMS). vol 7, No May 2005, p 83

  37. Webster FV (1957) Traffic signal settings. Road research technical paper 39

  38. Robertson D (1969) TRANSYT: a traffic network study tool ministry of transport. RRL Report LR253, Crowthorne, Berkshire, United Kingdom

  39. May AD (1975) Traffic flow theory- the traffic engineers challenge. Proc Inst Traf Eng 1965:290–303

    Google Scholar 

  40. Sharma A, Bullock DM, Bonneson JA, Sharma A, Bullock DM, Bonneson JA (2007) Input–output and hybrid techniques for real-time prediction of delay and maximum queue length at signalized intersections delay and maximum queue length at signalized intersections. Transp Res Record: J Transp Res Board 2035:69–80. https://doi.org/10.3141/2035-08

    Article  Google Scholar 

  41. Vigos G, Papageorgiou M, Wang Y (2008) Real-time estimation of vehicle-count within signalized links. Transp Res Part C: Emerg Technol 16(1):18–35. https://doi.org/10.1016/j.trc.2007.06.002

    Article  Google Scholar 

  42. Stephanopoulos G, Michalopoulos PG, Stephanopoulos G (1979) Modelling and analysis of traffic queue dynamics at signalized intersections. Transp Res Part A: Gen 13(5):295–307. https://doi.org/10.1016/0191-2607(79)90028-1

    Article  MATH  Google Scholar 

  43. Lighthill MJ, Whitham GB (1955) On kinematic waves II a theory of traffic flow on long crowded roads. Proc R Soc Lond Ser A Math Phys Sci 229(1178):317–345. https://doi.org/10.1098/rspa.1955.0089

    Article  MathSciNet  MATH  Google Scholar 

  44. Richards PI (1956) Shock waves on the highway. Oper Res 4(1):42–51. https://doi.org/10.1287/opre.4.1.42

    Article  MathSciNet  MATH  Google Scholar 

  45. Smaglik E, Sharma A, Bullock D, Sturdevant J, Duncan G (2007) Event-based data collection for generating actuated controller performance measures. Transp Res Record: J Transp Res Board 2035(2035):97–106. https://doi.org/10.3141/2035-11

    Article  Google Scholar 

  46. An C, Wu YJ, Xia J, Huang W (2017) Real-time queue length estimation using event-based advance detector data. J Intell Transp Syst Technol Plan Oper. https://doi.org/10.1080/15472450.2017.1299011

    Article  Google Scholar 

  47. Jeff Ban X, Hao P, Sun Z (2011) Real time queue length estimation for signalized intersections using travel times from mobile sensors. Transp Res Part C: Emerg Technol 19(6):1133–1156. https://doi.org/10.1016/j.trc.2011.01.002

    Article  Google Scholar 

  48. Hao P, Ban X (2015) Long queue estimation for signalized intersections using mobile data. Transp Res Part B: Methodol 82:54–73. https://doi.org/10.1016/j.trb.2015.10.002

    Article  Google Scholar 

  49. Comert G (2013) Simple analytical models for estimating the queue lengths from probe vehicles at traffic signals. Transp Res Part B: Methodol 55:59–74. https://doi.org/10.1016/j.trb.2013.05.001

    Article  Google Scholar 

  50. Tiaprasert K, Zhang Y, Wang XB, Zeng X (2015) Queue length estimation using connected vehicle technology for adaptive signal control. IEEE Trans Intell Transp Syst 16(4):2129–2140. https://doi.org/10.1109/TITS.2015.2401007

    Article  Google Scholar 

  51. Emami A, Sarvi M, Asadi Bagloee S (2019) A neural network algorithm for queue length estimation based on the concept of K-leader connected vehicles. J Modern Transp 27(4):341–354. https://doi.org/10.1007/s40534-019-00200-y

    Article  Google Scholar 

  52. Gao K, Han F, Dong P, Xiong N, Du R (2019) Connected vehicle as a mobile sensor for real time queue length at signalized intersections. Sensors (Switzerland) 19(9):1–22. https://doi.org/10.3390/s19092059

    Article  Google Scholar 

  53. Chang GL, Su CC (1995) Predicting intersection queue with neural network models. Transp Res Part C 3(3):175–191. https://doi.org/10.1016/0968-090X(95)00005-4

    Article  Google Scholar 

  54. Lee S, Xie K, Ngoduy D, Keyvan-Ekbatani M (2019) An advanced deep learning approach to real-time estimation of lane-based queue lengths at a signalized junction. Transp Res Part C: Emerg Technol 109:117–136. https://doi.org/10.1016/j.trc.2019.10.011

    Article  Google Scholar 

  55. Gan S, Liang S, Li K, Deng J, Cheng T (2018) Trajectory length prediction for intelligent traffic signaling: a data-driven approach. IEEE Trans Intell Transp Syst 19(2):426–435. https://doi.org/10.1109/TITS.2017.2700209

    Article  Google Scholar 

  56. Wang J, Kumbasar T (2019) Parameter optimization of interval Type-2 fuzzy neural networks based on PSO and BBBC methods. IEEE/CAA J Autom Sin 6(1):247–257. https://doi.org/10.1109/JAS.2019.1911348

    Article  Google Scholar 

  57. Gao S, Zhou M, Wang Y, Cheng J, Yachi H, Wang J (2019) Dendritic neuron model with effective learning algorithms for classification, approximation, and prediction. IEEE Trans Neural Netw Learn Syst 30(2):601–614. https://doi.org/10.1109/TNNLS.2018.2846646

    Article  Google Scholar 

  58. Zeng J, Yu S, Qian Y, Feng X (2018) Expressway traffic flow model study based on different traffic rules. IEEE/CAA J Autom Sin 5(6):1099–1103. https://doi.org/10.1109/JAS.2017.7510469

    Article  Google Scholar 

  59. Lipton ZC, Berkowitz J, Elkan C (2015) A critical review of recurrent neural networks for sequence learning 17 Oct 2015. pp 1–38. arXiv:1506.00019v4 [Cs.LG]

  60. Hochreiter S, Urgen Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  61. Géron A (2017) Hands-on machine learning with Scikit-learn and TensorFlow. O’Reilly Media, Newton

    Google Scholar 

  62. Wang G, Qiao J, Bi J, Li W, Zhou M (2019) TL-GDBN: growing deep belief network with transfer learning. IEEE Trans Autom Sci Eng 16(2):874–885. https://doi.org/10.1109/TASE.2018.2865663

    Article  Google Scholar 

  63. Bergstra J, Bardenet R, Bengio Y, Kégl B (2011) Algorithms for hyper-parameter optimization. In: Advances in neural information processing systems (NIPS), pp 2546–2554. 2012arXiv1206.2944S

  64. Bergstra J, Yamins D, Cox DD (2013) Hyperopt: a Python library for optimizing the hyperparameters of machine learning algorithms. In: 12th python in science conference (SCIPY 2013), No. Scipy, pp 13–20. https://doi.org/10.1088/1749-4699/8/1/014008

  65. Hutter F, Hoos HH, Leyton-Brown K (2011) Sequential model-based optimization for general algorithm configuration. Lecture notes in computer science (including subseries. Lecture notes in artificial intelligence and lecture notes in bioinformatics), vol 6683 LNCS, 2011, pp 507–523. https://doi.org/10.1007/978-3-642-25566-3_40

  66. Jones DR (2001) A taxonomy of global optimization methods based on response surfaces. J Global Optim 21:345–383. https://doi.org/10.1023/A:1012771025575

    Article  MathSciNet  MATH  Google Scholar 

  67. Rahman R (2019) Applications of deep learning models for traffic prediction problems. In: Electronic theses and dissertations, 2004–2019, p 6286. https://stars.library.ucf.edu/etd/6286

  68. TDOT (2020) Traffic design manual. Traffic operations division traffic engineering office. https://www.tn.gov/tdot/traffic-operationsdivision/traffic-operations-division-resources/traffic-design-manual.html

Download references

Acknowledgements

The authors acknowledge Prof. Mohamed Abdel-Aty and Yaobang Yang for providing us the InSync datasets for training the algorithms.

Author information

Authors and Affiliations

  1. Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA

    Rezaur Rahman & Samiul Hasan

Authors
  1. Rezaur Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samiul Hasan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Samiul Hasan.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rahman, R., Hasan, S. Real-time signal queue length prediction using long short-term memory neural network. Neural Comput & Applic 33, 3311–3324 (2021). https://doi.org/10.1007/s00521-020-05196-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05196-9

Keywords

Search

Navigation