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A data-driven operational integrated driving behavioral model on highways

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Abstract

Car-following (CF) and lane-changing (LC) behavior models have been widely studied separately as the core models of traffic simulation. However, in practice, CF and LC are inseparable and thus integrated driving (ID) models containing CF and LC behaviors emerge. Here, we proposed a new work to introduce the social force (SF) model to the operational ID behavioral model on the highway. First, a data-driven-based operational ID behavioral model is proposed in the hierarchical social force behavioral model framework. Then, the inputs/output of the SF-ID behavioral model is determined. SF-ID model is built by the feed-forward neural networks (FNN), and the network structure and other parameters are calibrated and verified by field data. Results of the test on CF and LC situations show that our proposed FNN SF-ID model has a good capability in reproducing/predicting the operational ID behaviors on the highway. In addition, we also analyzed the structural features of the FNN SF-ID models, and refine the original models by removing insignificant inputs. The comparison results showed that the refined model—FNN SF-ID (R)—performed better than the original models.

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References

  1. Anvari B, Bell Michael GH, Sivakumar A, Ochieng WY (2015) Modelling shared space users via rule-based social force model. Transp Res Part C 51:83–103

    Google Scholar 

  2. Bakhit PR, Osman OA, Ishak S (2017) Detecting imminent lane change maneuvers in connected vehicle environments. Transp Res Rec 2645:168–175. https://doi.org/10.3141/2645-18

    Article  Google Scholar 

  3. Blum AL, Langley P (1997) Selection of relevant features and examples in machine learning. Artif Intell 97:245–271

    MathSciNet  MATH  Google Scholar 

  4. Butakov V, Ioannou P (2015) Personalized driver/vehicle lane change models for ADAS. IEEE Trans Veh Technol 64(10):4422–4431

    Google Scholar 

  5. Cascianelli S, Costante G, Ciarfuglia TA, Valigi P, Fravolini M (2018) Full-GRU natural language video description for service robotics applications. IEEE Robot Autom Lett 3(2):841–848

    Google Scholar 

  6. Chung Y-S, Wong J-T (2010) Investigating driving styles and their connections to speeding and accident experience. J East Asia Soc Transp Stud 8:1944–1958

    Google Scholar 

  7. Dang HQ, Furnkranz J, Biedermann A, et al (2017) Time-to-lane-change prediction with deep learning[C]//2017. In: IEEE 20th international conference on intelligent transportation systems (ITSC). IEEE

  8. Delpiano R, Laval J, Coeymans JE, Herrera JC (2015) The kinematic wave model with finite decelerations: a social force car-following model approximation. Transp Res Part B Methodol 71:182–193

    Google Scholar 

  9. Díaz-Álvarez A, Clavijo M, Jiménez F, Talavera E, Serradilla F (2018) Modelling the human lane-change execution behaviour through Multilayer Perceptrons and Convolutional Neural Networks, 2018. Transp Res Part F 56:134–148

    Google Scholar 

  10. Qi G, Wu J, Zhou Y, Du Y, et al. (2018) Recognizing driving styles based on topic models. Transp Res Part D (in Press)

  11. Elkosantini S, Darmoul S (2018) A new framework for the computer modelling and simulation of car driver behavior. Simulation 94(12):1081–1097

    Google Scholar 

  12. Federal Highway Administration (FHWA) (2001) Chapter 4: car following models, in revised monograph on traffic flow theory [online]. https://www.fhwa.dot.gov/publications/research/operations/tft/chap4.pdf

  13. Federal Highway Administration (FHWA) (2008) The next generation simulation (NGSIM) [online]. https://ops.fhwa.dotgov/trafficanalysistools/ngsim.htm

  14. Gipps PG (1981) A behavioural car-following model for computer simulation. Transp Res Part B Methodol 15(2):105–111

    Google Scholar 

  15. Gipps PG (1986) A model for the structure of lane-changing decisions. Transp Res Part B (Methodol) 20(5):403–414

    Google Scholar 

  16. Guo K, Zhang R, Kuang L (2016) TMR: towards an efficient semantic-based heterogeneous transportation media big data retrieval. Neurocomputing 181:122–131. https://doi.org/10.1016/j.neucom.2015.06.101

    Article  Google Scholar 

  17. He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on Image-net classification. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 1026–1034

  18. He Z, Zheng L, Guan W (2015) A simple nonparametric car-following model driven by field data. Transp Res Part B 80:185–201

    Google Scholar 

  19. Helbing D, Molnar O (1995) Social force model for pedestrian dynamics. Phys Rev Part E 51:4282–4286

    Google Scholar 

  20. Helbing D, Tilch B (1998) Generalized force model of traffic dynamics. Phys Rev Part E 58:133–138

    Google Scholar 

  21. Helbing D, Farkas I, Vicsek T (2000) Simulating dynamical features of escape panic. Nature 407:407–487

    Google Scholar 

  22. Hidas P (2005) Modelling vehicle interactions in microscopic simulation of merging and weaving. Transp Res Part C 13(1):37–62

    Google Scholar 

  23. Nassim M, Amini S, Hoffmann S, et al. (2017) Modeling tactical lane-change behavior for automated vehicles: a supervised machine learning approach. In: Proceedings of the 5th IEEE international conference on models and technologies for intelligent transportation systems, MT-ITS 2017, pp 268–273

  24. Hollander Y, Liu R (2008) The principles of calibrating traffic microsimulation models. Transportation 35(3):347–362

    Google Scholar 

  25. Huang L, Wu J, You F, Lv Z, Song H (2017) Cyclist social force model at unsignalized intersections with heterogeneous traffic. IEEE Trans Ind Inf 13(2):782–792

    Google Scholar 

  26. Huang X, Sun J, Sun J (2018) A car-following model considering asymmetric driving behavior based on long short-term memory neural networks. Transp Res Part C Emerg Technol 95:346–362

    Google Scholar 

  27. Huang L, Guo H, Zhang R, Wang H, Wu J (2018) Capturing drivers’ lane changing behaviors on operational level by data driven methods. IEEE Access 6:57497–57506

    Google Scholar 

  28. Jia Y, Wu J, Ben-Akiva M, Seshadri R, Du Y (2017) Rainfall-integrated traffic speed prediction using deep learning method. IET Intel Transp Syst 11(9):531–536

    Google Scholar 

  29. Jin W-L (2013) A multi-commodity Lighthill-Whitham-Richards model of lane-changing traffic flow. Transp Res Part B 57:361–377

    Google Scholar 

  30. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436–444

    Google Scholar 

  31. Leonhardt V, Wanielik G (2017) Neural network for lane change prediction assessing driving situation, driver behavior and vehicle movement. 2017. In: IEEE 20th international conference on intelligent transportation systems (ITSC), Yokohama, Japan, 16–19 Oct 2017

  32. Lin D, Ma W, Li L, Wang Y (2016) A driving force model for non-strict priority crossing behaviors of right-turn drivers. Transp Res Part B Methodol 83:230–244

    Google Scholar 

  33. Backfrieder C, Ostermayer G, Lindorfer M, et al. (2016) Cooperative lane-change and longitudinal behaviour model extension for traffsim. In: Proceedings of Smart Cities - 1st International Conference, Smart-CT 2016, Lecture Notes in Computer Science, vol 9704, pp 52–62

  34. Litjens G, Kooi T, Bejnordi BE et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

    Google Scholar 

  35. Liu B, Liu H, Zhang H et al (2018) A social force evacuation model driven by video data. Simul Model Pract Theory 84:190–203

    Google Scholar 

  36. Peng J, Guo Y, Fu R, Yuan W, Wang C (2015) Multi-parameter prediction of drivers’ lane-changing behaviour with neural network model. Appl Ergon 50:207–217

    Google Scholar 

  37. Sagberg F, Selpi Piccinini G F et al (2015) A review of research on driving styles and road safety. Hum Fact 57(7):1248–1275

    Google Scholar 

  38. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Google Scholar 

  39. Tang T, Huang H, Wong SC et al (2008) A car-following model with the anticipation effect of potential lane changing. Acta Mech Sin 24:399. https://doi.org/10.1007/s10409-008-0163-0

    Article  MATH  Google Scholar 

  40. Tang J, Fang L, Wenhui Z, Ruimin K, Yajie Z (2018) Lane-changes prediction based on adaptive fuzzy neural network. Exp Syst Appl 91:452–463

    Google Scholar 

  41. Tian J, Jiang R, Jia B et al (2016) Empirical analysis and simulation of the concave growth pattern of traffic oscillations. Transp Res Part B Methodol 93(A):338–354

    Google Scholar 

  42. Toledo T, Koutsopoulos HN, Ben-Akiva M (2007) Integrated driving behavior modeling. Transp Res Part C 15(2):96–112

    Google Scholar 

  43. Toledo T, Koutsopoulos HN, Ben-Akiva M (2009) Estimation of an integrated driver behavior model. Transp Res Part C Emerg Technol 17(4):365–380

    Google Scholar 

  44. Treiber M, Kesting A, Helbing D (2000) Congested traffic states in empirical observations and microscopic simulations. Phys Rev E 62:1805–1824

    Google Scholar 

  45. Wang M, Hoogendoorn SP, Daamen W et al (2015) Game theoretic approach for predictive lane-changing and car-following control. Trans Res Part C 58:73–92

    Google Scholar 

  46. Wang H, Liu Z, Zhang Z et al (2015) Double-head car-following and lane-changing combined model [J]. J Southeast Univ (Nat Sci Ed) 45(5):985–989

    MATH  Google Scholar 

  47. Wang X, Jiang R, Li L, Lin Y, Zheng X, Wang F-Y (2018) Capturing car-following behaviors by deep learning. IEEE Trans Intell Transp Syst 19(3):910–920

    Google Scholar 

  48. Weng J, Li G, Yu Y (2017) Time-dependent drivers’ merging behavior model in work zone merging areas. Transp Res Part C 80:409–422

    Google Scholar 

  49. Wen-Xing Z, Lei J (2007) Analysis of a new car-following model with a consideration of multi-interaction of preceding vehicles. In: IEEE conference intelligent transportation systems

  50. Wu J, Brackstone M, McDonald M (2003) The validation of a microscopic simulation model: a methodological case study. Transp Res Part C 11(6):463–479

    Google Scholar 

  51. Yang Y (2015) Development of the regional freight transportation demand prediction models based on the regression analysis methods. Neurocomputing 158:42–47. https://doi.org/10.1016/j.neucom.2015.01.069

    Article  Google Scholar 

  52. Yang Q, Koutsopoulos HN (1996) A microscopic traffic simulator for evaluation of dynamic traffic management systems. Transp Res Part C 4(3):113–129

    Google Scholar 

  53. Yang Da, Gang Su, Danhong Wu et al (2018) Modeling drivers’ lane-changing decision behavior based on social force. J Southwest Jiaotong Univ 53(4):791–797

    Google Scholar 

  54. Zhang X-Y, Yin F, Zhang Y-M et al (2018) Drawing and Recognizing Chinese Characters with Recurrent Neural Network. IEEE Trans Pattern Anal Mach Intell 40(4):849–862

    Google Scholar 

  55. Zhao Y, Zhang HM (2017) A unified follow-the-leader model for vehicle, bicycle and pedestrian traffic. Transp Res Part B 105:315–327

    Google Scholar 

  56. Zheng Z (2014) Recent developments and research needs in modeling lane changing. Transp Res Part B 60:16–32

    Google Scholar 

  57. Zhou M, Qu X, Li X (2017) A recurrent neural network based microscopic car following model to predict traffic oscillation. Transp Res Part C 84:245–264

    Google Scholar 

  58. Zhou X, Hong H, Xing X, Bian K, Xie K, Mingliang X (2017) Discovering spatio-temporal dependencies based on time-lag in intelligent transportation data. Neurocomputing 259:76–84. https://doi.org/10.1016/j.neucom.2016.06.084

    Article  Google Scholar 

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Acknowledgements

This work was partially supported by National Natural Science Foundation of China (Grant Nos. 51408237, 51775565 and U1811463), Science and Technology Planning Project of Guangdong Province (Grant No. 2017A040405021), Fundamental Research Funds for the Central Universities (Grant No. 18lgpy83), and the Engineering and Physical Sciences Research Council of U.K. under the EPSRC Innovation Fellow scheme (Grant No. EP/S001956/1).

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Authors and Affiliations

  1. School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, 510640, People’s Republic of China

    Ling Huang & Hengcong Guo

  2. Guangdong Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou, 510275, People’s Republic of China

    Ronghui Zhang

  3. Department of Aeronautical and Automotive Engineering, Loughborough University, Loughborough, LE11 3TU, UK

    Dezong Zhao

  4. Department of Civil Engineering, Tsinghua University, Beijing, 100084, People’s Republic of China

    Jianping Wu

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  1. Ling Huang
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Correspondence to Ronghui Zhang.

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Huang, L., Guo, H., Zhang, R. et al. A data-driven operational integrated driving behavioral model on highways. Neural Comput & Applic 32, 13017–13033 (2020). https://doi.org/10.1007/s00521-020-04746-5

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