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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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

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

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