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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
27.08.2024
DOI
https://doi.org/10.7307/ptt.v36i4.505
ISSUE
2024 (Vol 36), Issue 4
LICENSE
Copyright (c) 2024 Yuming ZHOU, Min ZHANG, Chi ZHANG, Zilong XIE, Bo WANG
Creative Commons 4.0 logo
This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

A Novel Traffic Speed Prediction for Road Weaving Sections: Incorporating Traffic Flow Characteristics

Authors:Yuming ZHOU, Min ZHANG, Chi ZHANG, Zilong XIE, Bo WANG

Abstract

Weaving sections on roads are crucial areas with high concentrations of mandatory lane changes, which can increase the likelihood of traffic accidents. Speed is a key factor in determining traffic safety, and the development of an accurate speed prediction method is essential for improving safety in weaving sections. While current methods are effective in predicting speeds in straightforward situations, they face challenges in more complex scenarios such as weaving sections. This study presents a refined traffic speed prediction approach specifically designed for weaving sections in order to tackle the aforementioned issue. Initially, novel variables were formulated to capture the unique traffic flow attributes present in weaving sections, which distinguish them from standard road segments. Subsequently, supplementary empirical variables that are known to impact speed were incorporated. We conducted a variable importance assessment to ascertain the extent and direction of each variable’s contribution. Lastly, variables with significant positive effects were chosen as inputs for three machine learning algorithms: Random Forest (RF), Backpropagation Neural Network optimised with Genetic Algorithm (BPNN-GA) and Support Vector Regression (SVR). This method was evaluated using aerial footage from five distinct weaving sections in China, maintaining an approximately 3 km/h prediction error. In addition, the study also finds that the speed distribution in weaving sections is negatively correlated with the number of lane-changing. Vehicles experience deceleration at both on-ramp and off-ramp, with a more significant deceleration occurring at the on-ramp. Speed is significantly affected by short length, number of lanes and proportion of large vehicles. The proposed method can be embedded into intelligent traffic systems for safe speeds of autonomous vehicles in weaving sections. Reconstructing spatiotemporal patterns of traffic congestion, predicting traffic accidents and implementing active traffic management strategies in weaving sections could be investigated in the future.

Keywords:traffic safety, weaving sections, speed prediction method, vehicle trajectory data, variable importance

References

  1. [1] Prasetijo J, et al. Analysis of maximum weaving length and lane-changing rate for two-sided weaving section for federal road FT050 based on HCM 2010. International Journal of Integrated Engineering. 2018;10(2):56-60. DOI: 10.30880/ijie.2018.10.02.011.
  2. [2] Zhao CW, et al. Choice of lane-changing point in an urban intertunnel weaving section based on random forest and support vector machine. Promet-Traffic & Transportation. 2023;35(2):161-174. DOI: 10.7307/ptt.v35i2.60.
  3. [3] Liao YG, et al. Risk distribution characteristics and optimization of short weaving area for complex municipal interchanges. Journal of Advanced Transportation. 2021;2021:1-10. DOI: 10.1155/2021/5573335.
  4. [4] Ma CX, et al. A novel STFSA-CNN-GRU hybrid model for short-term traffic speed prediction. IEEE Transactions on Intelligent Transportation Systems. 2023;24(4):3728-3737. DOI: 10.1109/tits.2021.3117835.
  5. [5] Vlahogianni EI, Karlaftis MG, Golias JC. Short-term traffic forecasting: Where we are and where we’re going. Transportation Research Part C-Emerging Technologies. 2014;43:3-19. DOI: 10.1016/j.trc.2014.01.005.
  6. [6] Lighthill MJ, Whitham GB. On kinematic waves. II. A theory of traffic flow on long crowded roads. Proceedings of The Royal Society A Mathematical Physical and Engineering Sciences. 1955;229(1178):317-345. DOI: 10.1098/rspa.1955.0089.
  7. [7] Jiang H, et al. Short-term speed prediction using remote microwave sensor data: Machine learning versus statistical model. Mathematical Problems in Engineering. 2016;2016:1-13. DOI: 10.1155/2016/9236156.
  8. [8] Yang XX, et al. Evaluation of short-term freeway speed prediction based on periodic analysis using statistical models and machine learning models. Journal of Advanced Transportation. 2020;2020:1-16. DOI: 10.1155/2020/9628957.
  9. [9] Ye Q, Szeto WY,Wong SC. Short-term traffic speed forecasting based on data recorded at irregular intervals. IEEE Transactions on Intelligent Transportation Systems. 2012;13(4):1727-1737. DOI: 10.1109/tits.2012.2203122.
  10. [10] Williams BM, et al. Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: Theoretical basis and empirical results. Journal of Transportation Engineering. 2003;129(6):664-672. DOI: 10.1061/(ASCE)0733-947X(2003)129:6(664).
  11. [11] Qi Y, Ishak S. A hidden Markov model for short term prediction of traffic conditions on freeways. Transportation Research Part C-Emerging Technologies. 2014;43:95-111. DOI: 10.1016/j.trc.2014.02.007.
  12. [12] Zhan AY, et al. A traffic flow forecasting method based on the GA-SVR. Journal of High Speed Networks. 2022;28(2):97-106. DOI: 10.3233/jhs-220682.
  13. [13] Meng XW, et al. D-LSTM: Short-term road traffic speed prediction model based on GPS positioning data. IEEE Transactions on Intelligent Transportation Systems. 2022;23(3):2021-2030. DOI: 10.1109/tits.2020.3030546.
  14. [14] Guo SN, et al. Deep spatial-temporal 3D convolutional neural networks for traffic data forecasting. IEEE Transactions on Intelligent Transportation Systems. 2019;20(10):3913-3926. DOI: 10.1109/tits.2019.2906365.
  15. [15] Yuan Y, et al. Macroscopic traffic flow modeling with physics regularized Gaussian process: A new insight into machine learning applications in transportation. Transportation Research Part B-Methodological. 2021;146:88-110. DOI: 10.1016/j.trb.2021.02.007.
  16. [16] Tian Y, Wei C, Xu D. Traffic flow prediction based on stack autoencoder and long short-term memory network. 2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE), 20-22 November 2020, Shenyang, China. 2021. p. 385-388. DOI: 10.1109/AUTEEE50969.2020.9315723.
  17. [17] Li LC, et al. Traffic speed prediction for intelligent transportation system based on a deep feature fusion model. Journal of Intelligent Transportation Systems. 2019;23(6):605-616. DOI: 10.1080/15472450.2019.1583965.
  18. [18] Song ZG, et al. Short-term traffic speed prediction under different data collection time intervals using a SARIMASDGM hybrid prediction model. Plos One. 2019;14(6):1-19. DOI: 10.1371/journal.pone.0218626.
  19. [19] Cao MM, Li V, Chan V. A CNN-LSTM model for traffic speed prediction. IEEE 91st Vehicular Technology Conference (VTC2020-Spring), 25-28 May 2020, Antwerp, Belgium. 2020. p. 1-5. DOI: 10.1109/VTC2020 Spring48590.2020.9129440.
  20. [20] Niu K, et al. A novel spatio-temporal model for city-scale traffic speed prediction. IEEE ACCESS. 2019;7:30050-30057. DOI: 10.1109/access.2019.2902185.
  21. [21] Zhao YC, et al. Dynamic analysis of Kalman filter for traffic flow forecasting in sensor nets. Information Technology Journal. 2012;11(10):1508-1512. DOI: 10.3923/itj.2012.1508.1512.
  22. [22] Guo JH, Huang W, Williams BM. Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification. Transportation Research Part C-Emerging Technologies. 2014;43:50-64. DOI: 10.1016/j.trc.2014.02.006.
  23. [23] Cui Z, et al. Deep bidirectional and unidirectional LSTM recurrent neural network for network-wide traffic speed prediction. 2018:1-12. DOI: 10.48550/arXiv.1801.02143.
  24. [24] Ahmed I, et al. Lane change rates at freeway weaving sites: Trends in HCM6 and from NGSIM trajectories. Transportation Research Record. 2019;2673(5):627-636. DOI: 10.1177/0361198119841281.
  25. [25] Xu DZ, et al. Modeling framework for capacity analysis of freeway segments: Application to ramp weaves. Transportation Research Record. 2020;2674(1):148-159. DOI: 10.1177/0361198119900157.
  26. [26] Sun J, Jiang JR, Zheng JX. Improved speed prediction models on weaving segments of urban expressway. China Journal of Highway and Transport. 2017;30(01):83-91. DOI: 10.19721/j.cnki.1001-7372.2017.01.011.
  27. [27] Yu R, et al. Deep learning: A generic approach for extreme condition traffic forecasting. Proceedings of the 2017 SIAM International Conference on Data Mining. 2017. p. 777-785. DOI: 10.1137/1.9781611974973.87.
  28. [28] Ma XL, et al. Long short-term memory neural network for traffic speed prediction using remote microwave sensor data. Transportation Research Part C-Emerging Technologies. 2015;54:187-197. DOI: 10.1016/j.trc.2015.03.014.
  29. [29] Dai XY, et al. Deeptrend 2.0: A light-weighted multi-scale traffic prediction model using detrending. Transportation Research Part C-Emerging Technologies. 2019;103:142-157. DOI: 10.1016/j.trc.2019.03.022.
  30. [30] Song C, et al. Traffic speed prediction under weekday using convolutional neural networks concepts. 28th IEEE Intelligent Vehicles Symposium (IV), 11-14 June 2017, Los Angeles, CA. 2017. p. 1293-1298. DOI: 10.1109/IVS.2017.7995890.
  31. [31] Ma XL, et al. Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction. Sensors. 2017;17(4):1-16. DOI: 10.3390/s17040818.
  32. [32] Wang JY, et al. Traffic speed prediction and congestion source exploration: A deep learning method. 16th IEEE International Conference on Data Mining (ICDM), 12-15 December 2016, Barcelona, Spain. 2017. p. 499-508. DOI: 10.1109/icdm.2016.51.
  33. [33] Ke RM, et al. Two-stream multi-channel convolutional neural network for multi-lane traffic speed prediction considering traffic volume impact. Transportation Research Record. 2020;2674(4):459-470. DOI: 10.1177/0361198120911052.
  34. [34] Fan Q, et al. Space-time hybrid model for short-time travel speed prediction. Discrete Dynamics in Nature and Society. 2018;2018:1-9. DOI: 10.1155/2018/7696592.
  35. [35] Tan MC, Feng YB, Xu JM. Traffic flow prediction based on hybrid ARIMA and ANN model. China Journal of Highway and Transport. 2007;(4):118-121. DOI: 10.19721/j.cnki.1001-7372.2007.04.023.
  36. [36] Wang HZ, et al. Empirical mode decomposition-autoregressive integrated moving average hybrid short-term traffic speed prediction model. Transportation Research Record. 2014;(2460):66-76. DOI: 10.3141/2460-08.
  37. [37] Wang HZ, et al. A novel work zone short-term vehicle-type specific traffic speed prediction model through the hybrid EMD-ARIMA framework. Transportmetrica B-Transport Dynamics. 2016;4(3):159-186. DOI: 10.1080/21680566.2015.1060582.
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