Global traffic state prediction method for non-sensing locations on freeways
Article Text (Baidu Translation)
-
摘要: 针对既有高速公路交通状态预测研究较少考虑非检测点位和道路拓扑变化的问题，分析了现有研究方法的局限性，提出了一种结合宏观交通流模型、扩展卡尔曼滤波、数据驱动长短时记忆网络(LSTM)的交通状态预测方法，充分发挥机器学习在时域特征表达与可信交通流模型在空间动态跟踪上的优势；基于有限检测点位的流量和速度数据，构建了高速公路网络交通流体模型(METANET)并完成全局模型参数和基本图参数标定，设计了基于METANET和扩展卡尔曼滤波的交通状态估计器，继而训练机器学习模型实现全部检测点位的交通状态预测，并驱动交通状态估计器实现全域交通状态预测。研究结果表明：本文提出的交通状态预测方法能够显著提升高速公路的流量和速度预测精度，其中5 min流量和速度预测平均绝对百分比误差为6.92%和5.29%，相比基线方法分别改善29.62%和24.28%；30 min流量和速度预测平均绝对百分比误差为10.02%和8.62%，相比基线方法分别改善24.84%和15.87%；本文方法充分考虑了出入口匝道流量对主线交通状态的影响，也明显改善了主线流量预测性能。Abstract: In view of the problem that existing research on traffic state prediction of freeways rarely considers non-sensing locations or road topology changes, the limitations of existing research methods were analyzed. A traffic state prediction method combining macroscopic traffic flow model, extended Kalman filtering, and data-driven long short-term memory (LSTM) was proposed, aiming to fully leverage the advantages of machine learning in temporal feature expression and trustworthy traffic flow models in spatial dynamic tracking. Based on the flow and speed data of limited sensing locations, a model of ecoulement of traffic autoroute for networks (METANET) was constructed, and the global model parameters and fundamental diagram parameters were calibrated. A traffic state estimator based on METANET and extended Kalman filtering was designed. The machine learning model was trained to predict the traffic state of all sensing points, and the traffic state estimator was driven to predict the global traffic state. Research results show that the proposed traffic state prediction method can significantly improve the prediction accuracy of flow and speed of freeways. The mean absolute percentage errors of 5-minute flow and speed predictions are 6.92% and 5.29%, which perform 29.62% and 24.28% better than baseline method, respectively, and those of 30-minute flow and speed predictions are 10.02% and 8.62%, which perform 24.84% and 15.87% better than baseline method, respectively. In addition, the proposed method fully considers the impact of on/off ramp flow on the mainline traffic state, so the performance of mainline traffic flow prediction is significantly improved.
-
图 4 高速公路交通状态预测方法架构
Figure 4. Methodological framework of freeway traffic state prediction
图 7 国内测试道路的交通状态预测方法性能评估
Figure 7. Performance evaluations of traffic state prediction approaches for domestic testing freeway
图 8 国外测试道路的交通状态预测方法性能评估
Figure 8. Performance evaluations of traffic state prediction approaches for overseas testing freeway
图 9 国内测试道路评估检测器ZX8的预测结果
Figure 9. Prediction results at evaluation detector ZX8 for domestic testing freeway
图 10 国外测试道路的评估检测器PM2和PM10的预测结果
Figure 10. Prediction results at evaluation detector PM2 and PM10 for overseas testing freeway
图 11 国外测试道路的真实交通状态热力图
Figure 11. Thermodynamic diagrams of real traffic state for overseas testing freeway
图 12 国外测试道路的全域交通状态预测结果
Figure 12. Global traffic state prediction results for overseas testing freeway
图 13 国内测试道路的真实交通状态热力图
Figure 13. Thermodynamic diagrams of real traffic state for domestic testing freeway
图 14 国内测试道路的全域交通状态预测结果
Figure 14. Global traffic state prediction results for domestic testing freeway
表 1 METANET模型参数
Table 1. Parameters of METANET
测试道路 标定日期 vf ρcr qcap τ ν κ φ δ 国内道路 2022年3月3日 68.73 33.32 1 913.23 11.67 9.77 7.09 0.006 0.590 国外道路 2023年1月26日 107.15 35.19 2 166.82 17.81 30.10 49.54 0.010 0.012 
下载: 导出CSV
表 2 国内测试道路的交通状态预测方法性能评估
Table 2. Performance evaluations of traffic state prediction approaches for domestic testing freeway
预测方法 预测时长/min MAPE MAE RMSE 流量/% 速度/% 流量/(veh·min-1) 速度/(km·h-1) 流量/(veh·min-1) 速度/(km·h-1) G1 0 24.87 11.95 17.50 5.78 22.11 7.37 5 25.63 12.53 17.94 6.05 22.59 7.58 10 25.93 12.91 17.99 6.20 22.64 7.79 15 26.22 13.42 18.09 6.42 22.77 8.02 20 26.45 13.94 18.18 6.63 22.82 8.27 30 26.85 14.98 18.39 7.07 22.97 8.76 G2 0 2.18 11.95 1.29 5.78 1.79 7.37 5 7.51 12.53 4.72 6.05 6.24 7.58 10 8.43 12.91 5.33 6.20 6.82 7.79 15 9.23 13.42 5.75 6.42 7.26 8.02 20 9.90 13.94 6.07 6.63 7.66 8.27 30 10.89 14.98 6.57 7.07 8.38 8.76 G3 0 24.87 11.95 17.50 5.78 22.11 7.37 5 25.13 12.13 17.75 5.88 22.41 7.35 10 25.64 12.61 18.09 6.06 22.76 7.60 15 25.80 13.52 18.12 6.36 22.84 8.06 20 25.83 13.58 17.98 6.42 22.65 7.98 30 26.25 14.94 18.37 6.96 23.11 8.59 G4 0 2.18 11.95 1.29 5.78 1.79 7.37 5 6.62 12.13 4.38 5.88 5.61 7.35 10 8.21 12.61 5.28 6.06 6.72 7.60 15 8.91 13.52 5.64 6.36 7.21 8.06 20 9.59 13.58 5.97 6.42 7.68 7.98 30 10.37 14.94 6.56 6.96 8.26 8.59 G5 0 2.34 4.09 1.49 1.98 2.17 2.83 5 6.93 5.70 4.51 2.67 5.78 3.64 10 8.34 6.65 5.34 3.09 6.79 4.10 15 9.12 7.12 5.78 3.25 7.33 4.42 20 9.70 7.74 6.07 3.58 7.72 4.75 30 10.62 8.99 6.75 4.09 8.42 5.50
下载: 导出CSV
表 3 国外测试道路的交通状态预测方法性能评估
Table 3. Performance evaluations of traffic state prediction approaches for overseas testing freeway
预测方法 预测时长/min MAPE MAE RMSE 流量/% 速度/% 流量/(veh·min-1) 速度/(km·h-1) 流量/(veh·min-1) 速度/(km·h-1) G1 0 7.41 7.92 4.14 4.71 6.75 7.80 5 8.30 8.02 5.20 4.91 7.22 7.96 10 9.89 8.46 6.10 5.19 8.03 8.34 15 10.68 8.96 6.38 5.47 8.30 8.83 20 11.55 9.40 6.74 5.72 8.62 9.37 30 12.98 10.26 7.24 6.17 9.19 10.43 G2 0 4.70 7.92 2.77 4.71 4.08 7.80 5 7.22 8.02 4.40 4.91 5.52 7.96 10 7.93 8.46 5.40 5.19 6.79 8.34 15 8.79 8.96 5.58 5.47 7.05 8.83 20 9.78 9.40 5.80 5.72 7.33 9.37 30 11.34 10.26 6.30 6.17 8.03 10.43 G3 0 7.41 7.92 4.14 4.71 6.75 7.80 5 8.62 7.95 5.36 4.89 7.48 7.85 10 9.53 8.13 6.01 5.02 8.11 7.96 15 9.82 8.47 6.20 5.07 8.30 8.06 20 10.42 9.09 6.59 5.42 8.72 8.66 30 10.82 11.40 6.68 6.10 8.79 9.97 G4 0 4.70 7.92 2.77 4.71 4.08 7.80 5 6.46 7.95 4.37 4.89 5.52 7.85 10 7.60 8.13 5.14 5.02 6.48 7.96 15 7.97 8.47 5.37 5.07 6.71 8.06 20 8.70 9.09 5.82 5.42 7.28 8.66 30 9.34 11.40 6.08 6.10 7.60 9.97 G5 0 5.12 4.47 2.96 3.55 4.28 5.27 5 6.90 4.87 4.46 3.67 5.83 5.56 10 7.86 5.79 4.95 4.20 6.50 6.73 15 8.09 6.17 5.10 4.39 6.67 7.48 20 8.91 6.90 5.59 4.78 7.29 8.57 30 9.42 8.24 5.90 5.07 7.79 9.49
下载: 导出CSV
-
[1] 《中国公路学报》编辑部. 中国交通工程学术研究综述·2016[J]. 中国公路学报, 2016, 29(6): 1-161. doi: 10.3969/j.issn.1001-7372.2016.06.001Editorial Department of China Journal of Highway and Transport. Review on China's traffic engineering research progress: 2016[J]. China Journal of Highway and Transport, 2016, 29(6): 1-161. doi: 10.3969/j.issn.1001-7372.2016.06.001 [2] HOBEIKA A G, KIM C K. Traffic-flow-prediction systems based on upstream traffic[C]//IEEE. Proceedings of VNIS'94—1994 Vehicle Navigation and Information Systems Conference. New York: IEEE, 1994: 345-350. [3] SMITH B L, DEMETSKY M J. Traffic flow forecasting: comparison of modeling approaches[J]. Journal of Transportation Engineering, 1997, 123(4): 261-266. doi: 10.1061/(ASCE)0733-947X(1997)123:4(261) [4] WILLIAMS B M, DURVASULA P K, BROWN D E. Urban freeway traffic flow prediction: application of seasonal autoregressive integrated moving average and exponential smoothing models[J]. Transportation Research Record: Journal of the Transportation Research Board, 1998, 1644(1): 132-141. doi: 10.3141/1644-14 [5] SUN Shi-liang, ZHANG Chang-shui, YU Guo-qiang. A Bayesian network approach to traffic flow forecasting[J]. IEEE Transactions on Intelligent Transportation Systems, 2006, 7(1): 124-132. doi: 10.1109/TITS.2006.869623 [6] 朱征宇, 刘琳, 崔明. 一种结合SVM与卡尔曼滤波的短时交通流预测模型[J]. 计算机科学, 2013, 40(10): 248-251, 278. doi: 10.3969/j.issn.1002-137X.2013.10.052ZHU Zheng-yu, LIU Lin, CUI Ming. Short-term traffic flow forecasting model combining SVM and Kalman filtering[J]. Computer Science, 2013, 40(10): 248-251, 278. doi: 10.3969/j.issn.1002-137X.2013.10.052 [7] YANG Yi-jiao, DUAN Zheng-yu. A novel prediction method of traffic flow: least squares support vector machines based on spatial relation[C]//ASCE. 14th COTA International Conference of Transportation Professionals: Safe, Smart, and Sustainable Multimodal Transportation Systems. Reston: ASCE, 2014: 1807-1818. [8] LI Yuan-yuan, XU Wei-xiang. Short-term traffic flow forecasting based on SVR[C]//AMCCE. Proceedings of the 2018 3rd International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 2018). Paris: Atlantis Press, 2018: 57-61. [9] HUANG Wen-hao, SONG Guo-jie, HONG Hai-kun, et al. Deep architecture for traffic flow prediction: deep belief networks with multitask learning[J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(5): 2191-2201. doi: 10.1109/TITS.2014.2311123 [10] LYU Yi-sheng, DUAN Yan-jie, KANG Wen-wen, et al. Traffic flow prediction with big data: a deep learning approach[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(2): 865-873. [11] WANG Jing-yuan, GU Qian, WU Jun-jie, et al. Traffic speed prediction and congestion source exploration: a deep learning method[C]//IEEE. 2016 IEEE 16th International Conference on Data Mining. New York: IEEE, 2016: 499-508. [12] WANG Jing-yuan, HU Fei, LI Li, et al. Deep Bi-directional long short-term memory model for short-term traffic flow prediction[C]//Springer. 24th International Conference on Neural Information Processing. Berlin: Springer, 2017: 306-316. [13] LIU Bo-yi, CHENG Jie-ren, CAI Kuan-qi, et al. Singular point probability improve LSTM network performance for long-term traffic flow prediction[C]//Springer. 35th National Conference on Theoretical Computer Science. Berlin: Springer, 2017: 328-340. [14] YU Bing, YIN Hao-teng, ZHU Zhan-xing. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting[C]//International Joint Conferences on Artificial Intelligence. Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJGAI-18). California: International Joint Conferences on Artificial Intelligence, 2018: 3634-3640. [15] KUMAR N, RAUBAL M. Applications of deep learning in congestion detection, prediction and alleviation: a survey[J]. Transportation Research Part C: Emerging Technologies, 2021, 133: 103432. doi: 10.1016/j.trc.2021.103432 [16] CUI H P, MENG Q, TENG T H, et al. Spatiotemporal correlation modelling for machine learning-based traffic state predictions: state-of-the-art and beyond[J]. Transport Reviews, 2023, 43(4): 780-804. doi: 10.1080/01441647.2023.2171151 [17] 刘起东, 刘超越, 邱紫鑫, 等. 基于时间感知Transformer的交通流预测方法[J]. 计算机科学, 2023, 50(11): 88-96. doi: 10.11896/jsjkx.221000201LIU Qi-dong, LIU Chao-yue, QIU Zi-xin, et al. Time-aware transformer for traffic flow forecasting[J]. Computer Science, 2023, 50(11): 88-96. doi: 10.11896/jsjkx.221000201 [18] YANG Han-yi, DU Li-li, ZHANG Guo-hui, et al. A traffic flow dependency and dynamics based deep learning aided approach for network-wide traffic speed propagation prediction[J]. Transportation Research Part B: Methodological, 2023, 167: 99-117. doi: 10.1016/j.trb.2022.11.009 [19] YANG Han-yi, YU Wan-xin, ZHANG Guo-hui, et al. Network-wide traffic flow dynamics prediction leveraging macroscopic traffic flow model and deep neural networks[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(5): 4443-4457. doi: 10.1109/TITS.2023.3329489 [20] FOULADGAR M, PARCHAMI M, ELMASRI R, et al. Scalable deep traffic flow neural networks for urban traffic congestion prediction[C]//IEEE. 2017 Proceedings of the International Joint Conference on Neural Networks (IJCNN). New York: IEEE, 2017: 2251-2258. [21] KAN Yu-heng, WANG Yi-bing, WANG Dian-hai, et al. A novel approach to estimating missing pairs of on/off ramp flows[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(2): 1287-1305. doi: 10.1109/TITS.2020.2989365 [22] 姚俊峰, 何瑞, 史童童, 等. 基于机器学习的交通流预测方法综述[J]. 交通运输工程学报, 2023, 23(3): 44-67. doi: 10.19818/j.cnki.1671-1637.2023.03.003YAO Jun-feng, HE Rui, SHI Tong-tong, et al. Review on machine learning-based traffic flow prediction methods[J]. Journal of Traffic and Transportation Engineering, 2023, 23(3): 44-67. doi: 10.19818/j.cnki.1671-1637.2023.03.003 [23] 崔建勋, 要甲, 赵泊媛. 基于深度学习的短期交通流预测方法综述[J]. 交通运输工程学报, 2024, 24(2): 50-64. doi: 10.19818/j.cnki.1671-1637.2024.02.003CUI Jian-xun, YAO Jia, ZHAO Bo-yuan. Review on short-term traffic flow prediction methods based on deep learning[J]. Journal of Traffic and Transportation Engineering, 2024, 24(2): 50-64. doi: 10.19818/j.cnki.1671-1637.2024.02.003 [24] VLAHOGIANNI E I, KARLAFTIS M G, GOLIAS J C. Short-term traffic forecasting: where we are and where we're going[J]. Transportation Research Part C: Emerging Technologies, 2014, 43: 3-19. [25] LI Ya-guang, SHAHABI C. A brief overview of machine learning methods for short-term traffic forecasting and future directions[J]. SIGSPATIAL Special, 2018, 10(1): 3-9. [26] TEDJOPURNOMO D A, BAO Z F, ZHENG B H, et al. A survey on modern deep neural network for traffic prediction: trends, methods and challenges[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(4): 1544-1561. [27] LIGHTHILL M J, WHITHAM G B. On kinematic waves Ⅱ. A theory of traffic flow on long crowded roads[J]. Proceedings of the Royal Society of London. Series A: Mathematical and Physical Sciences, 1955, 229(1178): 317-345. [28] RICHARDS P I. Shock waves on the highway[J]. Operations Research, 1956, 4(1): 42-51. doi: 10.1287/opre.4.1.42 [29] DAGANZO C F. The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory[J]. Transportation Research Part B: Methodological, 1994, 28(4): 269-287. doi: 10.1016/0191-2615(94)90002-7 [30] PAPAGEORGIOU M, SCHMIDT G. Freeway traffic modelling and control[J]. IFAC Proceedings Volumes, 1983, 16(4): 195-202. doi: 10.1016/S1474-6670(17)62562-X [31] TREIBER M, HENNECKE A, HELBING D. Derivation, properties, and simulation of a gas-kinetic-based, nonlocal traffic model[J]. Physical Review E, 1999, 59(1): 239-253. doi: 10.1103/PhysRevE.59.239 [32] AW A, RASCLE M. Resurrection of "second order" models of traffic flow[J]. SIAM Journal on Applied Mathematics, 2000, 60(3): 916-938. [33] ZHANG H M. A non-equilibrium traffic model devoid of gas-like behavior[J]. Transportation Research Part B: Methodological, 2002, 36(3): 275-290. [34] PAPAGEORGIOU M, BLOSSEVILLE J M, HADJ-SALEM H. Macroscopic modelling of traffic flow on the boulevard Périphérique in Paris[J]. Transportation Research Part B: Methodological, 1989, 23(1): 29-47. [35] PAPAGEORGIOU M, BLOSSEVILLE J M, HADJ-SALEM H. Modelling and real-time control of traffic flow on the southern part of Boulevard Peripherique in Paris, part Ⅰ: modelling[J]. Transportation Research Part A: General, 1990, 24(5): 345-359. [36] KONTORINAKI M, SPILIOPOULOU A, RONCOLI C, et al. First-order traffic flow models incorporating capacity drop: overview and real-data validation[J]. Transportation Research Part B: Methodological, 2017, 106: 52-75. [37] MOHAMMADIAN S, ZHENG Z D, HAQUE M M, et al. Performance of continuum models for realworld traffic flows: comprehensive benchmarking[J]. Transportation Research Part B: Methodological, 2021, 147: 132-167. [38] KOTSIALOS A, PAPAGEORGIOU M, DIAKAKI C, et al. Traffic flow modeling of large-scale motorway networks using the macroscopic modeling tool METANET[J]. IEEE Transactions on Intelligent Transportation Systems, 2002, 3(4): 282-292. [39] WANG Yi-bing, YU Xiang-hua, GUO Jing-qiu, et al. Macroscopic traffic flow modelling of large-scale freeway networks with field data verification: state-of-the-art review, benchmarking framework, and case studies using METANET[J]. Transportation Research Part C: Emerging Technologies, 2022, 145: 103904. [40] ZHAO Ming-ming, RONCOLI C, WANG Yi-bing, et al. Generic approaches to estimating freeway traffic state and percentage of connected vehicles with fixed and mobile sensing[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(8): 13155-13177. [41] WANG Yi-bing, ZHAO Ming-ming, YU Xiang-hua, et al. Real-time joint traffic state and model parameter estimation on freeways with fixed sensors and connected vehicles: state-of-the-art overview, methods, and case studies[J]. Transportation Research Part C: Emerging Technologies, 2022, 134: 103444. [42] CARLSON R C, PAPAMICHAIL I, PAPAGEORGIOU M, et al. Optimal motorway traffic flow control involving variable speed limits and ramp metering[J]. Transportation Science, 2010, 44(2): 238-253. [43] WANG Yi-bing, YU Xiang-hua, ZHANG Si-yu, et al. Freeway traffic control in presence of capacity drop[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(3): 1497-1516. [44] DERVISOGLU G, GOMES G, KWON J, et al. Automatic calibration of the fundamental diagram and empirical observations on capacity[C]//TRB. Transportation Research Board 88th Annual Meeting. Washington DC: TRB, 2009: 1-14. [45] QU Xiang-bo, WANG Shuai-an, ZHANG Jin. On the fundamental diagram for freeway traffic: a novel calibration approach for single-regime models[J]. Transportation Research Part B: Methodological, 2015, 73: 91-102. [46] WANG Yi-bing, PAPAGEORGIOU M. Real-time freeway traffic state estimation based on extended Kalman filter: a general approach[J]. Transportation Research Part B: Methodological, 2005, 39(2): 141-167. [47] WANG Yi-bing, PAPAGEORGIOU M, MESSMER A. RENAISSANCE—A unified macroscopic model-based approach to real-time freeway network traffic surveillance[J]. Transportation Research Part C: Emerging Technologies, 2006, 14(3): 190-212. [48] WANG Yi-bing, PAPAGEORGIOU M, MESSMER A. Real-time freeway traffic state estimation based on extended Kalman filter: adaptive capabilities and real data testing[J]. Transportation Research Part A: Policy and Practice, 2008, 42(10): 1340-1358. [49] RAISSI M, PERDIKARIS P, KARNIADAKIS G E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations[J]. Journal of Computational Physics, 2019, 378: 686-707. [50] DI Xuan, SHI Rong-ye, MO Zhao-bin, et al. Physics-informed deep learning for traffic state estimation: a survey and the outlook[J]. Algorithms, 2023, 16(6): 305. -
点击查看大图
图(14) / 表(3)
计量
- 文章访问数: 25
- HTML全文浏览量: 31
- PDF下载量: 9
- 被引次数: 0
