Non-FIFO vehicle trajectory estimation algorithm under non-free traffic flow

HU Yao; ZHAO Rui-sha

doi:10.19818/j.cnki.1671-1637.2022.02.019

Volume 22 Issue 2

Apr. 2022

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Article Navigation > Journal of Traffic and Transportation Engineering > 2022 > 22(2): 246-258

HU Yao, ZHAO Rui-sha. Non-FIFO vehicle trajectory estimation algorithm under non-free traffic flow[J]. Journal of Traffic and Transportation Engineering, 2022, 22(2): 246-258. doi: 10.19818/j.cnki.1671-1637.2022.02.019

Citation:

HU Yao, ZHAO Rui-sha. Non-FIFO vehicle trajectory estimation algorithm under non-free traffic flow[J]. Journal of Traffic and Transportation Engineering, 2022, 22(2): 246-258. doi: 10.19818/j.cnki.1671-1637.2022.02.019

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Non-FIFO vehicle trajectory estimation algorithm under non-free traffic flow

doi: 10.19818/j.cnki.1671-1637.2022.02.019

HU Yao^{1, 2
,},
ZHAO Rui-sha^{1
,
,}

1.
School of Mathematics and Statistics, Guizhou University, Guiyang 550025, Guizhou, China
2.
State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, Guizhou, China

Funds:

National Natural Science Foundation of China 12161016

National Natural Science Foundation of China 11661018

Guizhou Province Science and Technology Planning Project [2020]5016

Guizhou Province Science and Technology Planning Project [2017]5788

More Information

Author Bio:
HU Yao(1971-), male, professor, yhu1@gzu.edu.cn

ZHAO Rui-sha(1997-), female, graduate student, 1019180540@qq.com
Received Date: 2021-12-16
Publish Date: 2022-04-25

Abstract

Abstract

The traffic state of the triangular fundamental diagram (TFD) was subdivided into free flow, breakdown, and jam. According to the non-free flow characteristics, the U-shaped spatial-temporal domain was re-divided to find the suitable wave velocity range. The cumulative flow of the upstream boundary was redefined so that the description of the boundary function was not too broad. The Newell's model under non-free flow was established, and the criterion of whether the model can be used was proposed. A parameter of vehicle's rank was introduced to realize the goal of describing multilane overtaking phenomena, and a more accurate estimation model of vehicle's rank was established. Then the Newell's extended model under non-free flow was developed. The vehicle trajectory estimation algorithms were proposed for the two situations of non-free flow, namely first-in-first-out (FIFO) situation and non-first-in-first-out (non-FIFO) situation, which were divided according to whether there was an overtaking phenomenon. The effectiveness of the algorithms was then verified by numerical simulation and real traffic cases. Analysis results show that the trajectory estimation algorithms are effective in both situations. When an overtaking phenomenon occurs, the estimation effect of the non-FIFO situation is more accurate and robust. In the numerical simulation study, the estimation error of the non-FIFO situation decreases by 13.45% compared with that of the FIFO situation, namely that the result of the non-FIFO situation is better. In the real traffic cases, the non-FIFO situation has 2.38% and 2.04% lower estimation errors than the FIFO situation on two car datasets, respectively, and the estimation errors all follow the Gaussian mixture model (GMM). Because there is no overtaking phenomenon in the bus dataset, the estimation errors of the non-FIFO situation and the FIFO situation are equal, which are both 4.90% and follow the Gamma distribution. Therefore, the established Newell's model under the non-free flow is effective and feasible for the traffic data with a large proportion of breakdowns or jams, and the proposed trajectory estimation algorithms of FIFO and non-FIFO situations perform well. 6 tabs, 13 figs, 30 refs.
- traffic flow,
- trajectory estimation,
- Newell's model under non-free flow,
- non-FIFO situation,
- vehicle's rank,
- correction coefficient

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References(30)

References

[1]	胡明伟, 王守峰, 黄文柯, 等. 基于车牌识别数据的行驶轨迹重构和排放测算[J]. 深圳大学学报(理工版), 2020, 37(2): 111-120. https://www.cnki.com.cn/Article/CJFDTOTAL-SZDL202002001.htm HU Ming-wei, WANG Shou-feng, HUANG Wen-ke, et al. Vehicle trajectory reconstruction and emission estimation based on license plate recognition data[J]. Journal of Shenzhen University (Science and Engineering), 2020, 37(2): 111-120. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SZDL202002001.htm
[2]	陆键, 王可, 蒋愚明. 基于车辆行驶轨迹的道路不良驾驶行为实时辨识方法[J]. 交通运输工程学报, 2020, 20(6): 227-235. doi: 10.19818/j.cnki.1671-1637.2020.06.020 LU Jian, WANG Ke, JIANG Yu-ming. Real-time identification method of abnormal road driving behavior based on vehicle driving trajectory[J]. Journal of Traffic and Transportation Engineering, 2020, 20(6): 227-235. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2020.06.020
[3]	TAN Chao-peng, LIU Lei, WU Hao, et al. Fuzing license plate recognition data and vehicle trajectory data for lane-based queue length estimation at signalized intersections[J]. Journal of Intelligent Transportation Systems, 2020, 24(5): 449-466. doi: 10.1080/15472450.2020.1732217
[4]	NEWELL G F. A simplified theory of kinematic waves in highway traffic, Part Ⅰ: general theory[J]. Transportation Research Part B: Methodological, 1993, 27(4): 281-287. doi: 10.1016/0191-2615(93)90038-C
[5]	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 A: Mathematical, Physical and Engineering Sciences, 1955, 229: 317-345.
[6]	RICHARDS P I. Shock waves on the highway[J]. Operations Research, 1956, 4(1): 42-51. doi: 10.1287/opre.4.1.42
[7]	MAZARÉ P E, DEHWAH A H, CLAUDEL C G, et al. Analytical and grid-free solutions to the Lighthill-Whitham-Richards traffic flow model[J]. Transportation Research Part B: Methodological, 2011, 45(10): 1727-1748. doi: 10.1016/j.trb.2011.07.004
[8]	MEHRAN B, KUWAHARA M, NAZNIN F, et al. Implementing kinematic wave theory to reconstruct vehicle trajectories from fixed and probe sensor data[J]. Transportation Research Part C: Emerging Technologies, 2012, 20(1): 144-163. doi: 10.1016/j.trc.2011.05.006
[9]	JIN Wen-long. Continuous formulations and analytical properties of the link transmission model[J]. Transportation Research Part B: Methodological, 2015, 74: 88-103. doi: 10.1016/j.trb.2014.12.006
[10]	MO B, LI Rui-ming, ZHAN Xian-yuan, et al. Speed profile estimation using license plate recognition data[J]. Transportation Research Part C: Emerging Technologies, 2017, 82: 358-378. doi: 10.1016/j.trc.2017.07.006
[11]	LI Jia, PERRINE K, WU Li-dong, et al. Cross-validating traffic speed measurements from probe and stationary sensors through state reconstruction[J]. International Journal of Transportation Science and Technology, 2019, 8(3): 290-303. doi: 10.1016/j.ijtst.2019.04.002
[12]	SUN Zhan-bo, HAO Peng, BAN Xue-gang, et al. Trajectory-based vehicle energy/emissions estimation for signalized arterials using mobile sensing data[J]. Transportation Research Part D: Transport and Environment, 2015, 34: 27-40. doi: 10.1016/j.trd.2014.10.005
[13]	WAN Nian-feng, VAHIDI A, LUCKOW A. Reconstructing maximum likelihood trajectory of probe vehicles between sparse updates[J]. Transportation Research Part C: Emerging Technologies, 2016, 65: 16-30. doi: 10.1016/j.trc.2016.01.010
[14]	刘春立, 黄琳娜. 交通事故中车辆运动轨迹估计方法研究与仿真[J]. 计算机仿真, 2012, 29(4): 375-378. doi: 10.3969/j.issn.1006-9348.2012.04.092 LIU Chun-li, HUANG Lin-na. Research and simulation of estimate method of traffic accident vehicle trajectory[J]. Computer Simulation, 2012, 29(4): 375-378. (in Chinese) doi: 10.3969/j.issn.1006-9348.2012.04.092
[15]	JIANG Zhou-tong, CHEN Xi-qun, OUYANG Yan-feng, et al. Traffic state and emission estimation for urban expressways based on heterogeneous data[J]. Transportation Research Part D: Transport and Environment, 2017, 53: 440-453. doi: 10.1016/j.trd.2017.04.042
[16]	季学武, 费聪, 何祥坤, 等. 基于LSTM网络的驾驶意图识别及车辆轨迹预测[J]. 中国公路学报, 2019, 32(6): 34-42. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201906004.htm JI Xue-wu, FEI Cong, HE Xiang-kun, et al. Intention recognition and trajectory prediction for vehicles using LSTM network[J]. China Journal of Highway and Transport, 2019, 32(6): 34-42. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201906004.htm
[17]	LI Xiao, ROSMAN G, GILITSCHENSKI I, et al. Vehicle trajectory prediction using generative adversarial network with temporal logic syntax tree features[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 3459-3466. doi: 10.1109/LRA.2021.3062807
[18]	DONG Shuo-xuan, ZHOU Yang, CHEN Tian-yi, et al. An integrated empirical mode decomposition and Butterworth filter based vehicle trajectory reconstruction method[J]. Physica A: Statistical Mechanics and its Applications, 2021, 583: 126295. doi: 10.1016/j.physa.2021.126295
[19]	JIN Wen-long. Unifiable multi-commodity kinematic wave model[J]. Transportation Research Procedia, 2017, 23: 137-156. doi: 10.1016/j.trpro.2017.05.009
[20]	JIN Wen-long, YAN Qing-long. A formulation of unifiable multi-commodity kinematic wave model with relative speed ratios[J]. Transportation Research Part B: Methodological, 2019, 128: 236-253. doi: 10.1016/j.trb.2019.08.007
[21]	REY A, JIN Wen-long, RITCHIE S G, et al. An extension of Newell's simplified kinematic wave model to account for first-in-first-out violation: with an application to vehicle trajectory estimation[J]. Transportation Research Part C: Emerging Technologies, 2019, 109: 79-94. doi: 10.1016/j.trc.2019.10.005
[22]	WANG Ying, WEI Chong. A universal trajectory planning method for automated lane-changing and overtaking maneuvers[J]. Mathematical Problems in Engineering, 2020, 2020: 1023975.
[23]	HUI Fei, WEI Cheng, SHANGGUAN Wei, et al. Deep encoder-decoder-NN: a deep learning-based autonomous vehicle trajectory prediction and correction model[J]. Physica A: Statistical Mechanics and its Applications, 2022, 593: 126869. doi: 10.1016/j.physa.2022.126869
[24]	胡尧, 韦维, 商明菊, 等. 基于交通流生存函数的交叉口通行能力计算模型[J]. 交通运输工程学报, 2019, 19(4): 137-150. doi: 10.3969/j.issn.1671-1637.2019.04.013 HU Yao, WEI Wei, SHANG Ming-ju, et al. Calculation model of intersection capacity based on traffic flow survival function[J]. Journal of Traffic and Transportation Engineering, 2019, 19(4): 137-150. (in Chinese) doi: 10.3969/j.issn.1671-1637.2019.04.013
[25]	DENG Xiao-li, HU Yao, HU Qian. Fundamental diagram estimation based on random probe pairs on sub-segments[J]. Promet-Traffic and Transportation, 2020, 33(5): 717-730.
[26]	LI Li, JIANG Rui, HE Zheng-bing, et al. Trajectory data-based traffic flow studies: a revisit[J]. Transportation Research Part C: Emerging Technologies, 2020, 114: 225-240. doi: 10.1016/j.trc.2020.02.016
[27]	曹堉, 王成, 杨岳铭, 等. 基于贝叶斯网络的城市道路交通拥堵多原因自动实时识别[J]. 公路交通科技, 2020, 37(11): 89-97. doi: 10.3969/j.issn.1002-0268.2020.11.012 CAO Yu, WANG Cheng, YANG Yue-ming, et al. Multi-cause automatic real-time recognition of urban road traffic congestion based on Bayesian network[J]. Journal of Highway and Transportation Research and Development, 2020, 37(11): 89-97. (in Chinese) doi: 10.3969/j.issn.1002-0268.2020.11.012
[28]	MUNJAL P K, HSU Y S, LAWRENCE R L. Analysis and validation of lane-drop effects on multi-lane freeways[J]. Transportation Research, 1971, 5: 257-266. doi: 10.1016/0041-1647(71)90037-2
[29]	SUN Zhe, JIN Wen-Long, RITCHIE S G. Simultaneous estimation of states and parameters in Newell's simplified kinematic wave model with Eulerian and Lagrangian traffic data[J]. Transportation Research Part B: Methodological, 2017, 104: 106-122. doi: 10.1016/j.trb.2017.06.012
[30]	REYNOLDS D. Gaussian mixture models[J]. Encyc-lopedia of biometrics, 2009, 741: 659-663.

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Non-FIFO vehicle trajectory estimation algorithm under non-free traffic flow

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