借道左转交叉口的网联左转车辆最佳轨迹控制

doi:10.13229/j.cnki.jdxbgxb.20230472

Abstract

Abstract:

To avoid frequent start-stop of vehicles and reduce delays and fuel consumption at exit lane for left-turn intersections， an optimal trajectory control model for left-turning vehicles is proposed. The model is applicable to exit lane for left-turn intersections in an intelligent connected vehicle environment. Firstly， the unique features of the exit lane for left-turn intersections are analyzed in terms of fuel consumption compared to traditional intersections. Secondly， the corresponding trajectory control double-layer model is established by considering the geometric design and traffic signals of the intersection， optimizing the lane selection and the time to enter the exit lane for left-turn. Finally， SUMO and Python software are used to simulate and verify the proposed control model and simulated experiments are designed to analyze the effects of different parameters such as traffic volume， green signal ratio and length of exit lane for left-turn on the model's performance. The experiments show that the proposed optimal trajectory control model effectively reduces delays and fuel consumption， with an average reduction of 26.6% in vehicle delay and 50.2% in fuel consumption compared to the Krauss control model. Moreover， the optimal trajectory control model exhibits robustness under different lengths of the exit lane for left-turn and has wide applicability.

Key words: engineering of communication and transportation, exit lane for left-turn, intelligent network environment, trajectory control, traffic simulation

CLC Number:

U491

Yong-heng CHEN,Jia-wei YANG,Jing-yu SUN. Optimal trajectory control for connected left-turn vehicles at exit lane for left-turn intersections[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(2): 614-622.

Figures/Tables 12

Fig.1

Fig.2

Fig.3

Table 1

Model input parameters"

参数	数值	参数	数值
$d l$ /m	6	$ω$	0.1
$d s$ /m	1	$C$ /s	120
$v m a x$ /（m·s^-1）	16	$v m i n$ /（m·s^-1）	0
$a m i n$ /（m·s^-2）	-5	$s d$ /m	15
$a m a x$ /（m·s^-2）	5	$x c$ /m	235
$h s$ /s	2	$x s$ /m	300
$g s m$ /s	0	T/s	120
$g e m$ /s	30	$M$	10 000
$g s p$ /s	-21	$m$	0.000 01

Table 1

Fig.4

Fig.5

Table 2

Fig.6

Table 3

Fig.7

Table 4

Fig.8

References 14

1	Zhang S M, Deng W W, Zhao Q R, et al. Dynamic trajectory planning for vehicle autonomous driving[C]∥2013 IEEE International Conference on Systems, Man, and Cybernetics, Manchester, UK, 2013: 4161-4166.
2	Luo Y G, Xiang Y, Cao K, et al. A dynamic automated lane change maneuver based on vehicle-to-vehicle communication[J]. Transportation Research Part C: Emerging Technologies, 2016, 62: 87-102.
3	Lu G Y, Nie Y, Liu X B, et al. Trajectory-based traffic management inside an autonomous vehicle zone[J]. Transportation Research Part B: Methodological, 2019, 120: 76-98.
4	贾彦峰, 曲大义, 林璐, 等. 基于运行轨迹的网联混合车流速度协调控制[J]. 吉林大学学报: 工学版, 2021, 51(6): 2051-2060.
	Jia Yan-feng, Qu Da-yi, Lin Lu, et al. Coordinated speed control of connected mixed traffic flow based on trajectory[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2051-2060.
5	Malikopoulos A A, Beaver L, Chremos I V. Optimal time trajectory and coordination for connected and automated vehicles[J]. Automatica, 2021, 125(6):No. 109469.
6	Liu Q, Zhao J, Zhou X. Optimal trajectory control for left-turn vehicles at exit lane for left-turn intersections[J]. Journal of Transportation Engineering, Part A: Systems, 2021(10):No. 0000576.
7	Jiang H, Hu J, An S, et al. Eco approaching at an isolated signalized intersection under partially connected and automated vehicles environment[J]. Transportation Research Part C: Emerging Technologies, 2017, 79(6): 290-307.
8	Ma J, Li X, Zhou F, et al. Parsimonious shooting heuristic for trajectory design of connected automated traffic part II: computational issues and optimization[J]. Transportation Research Part B, 2017, 95: 421-441.
9	Zhou F, Li X, Ma J. Parsimonious shooting heuristic for trajectory design of connected automated traffic part I: theoretical analysis with generalized time geography[J]. Transportation Research Part B Methodological, 2016, 95(1): 394-420.
10	Zhang L G, Liang W J, Zheng X Z. Eco-driving for public transit in cyber-physical systems using v2i communication[J]. International Journal of Intelligent Transportation Systems Research, 2018, 16(2): 79-89.
11	Zhao W M, Dong N. A platoon based cooperative eco-driving model for mixed automated and human-driven vehicles at a signalised intersection[J]. Transportation Research Part C: Emerging Technologies, 2018, 95: 802-821.
12	黄意然,宋国华,彭飞,等. 考虑排队长度的信号交叉口生态驾驶轨迹优化[J]. 交通运输工程与信息学报, 2022, 20(3): 43-56.
	Huang Yi-ran, Song Guo-hua, Peng Fei, et al. Trajectory optimization for eco-driving taking into account queuing Length at signalized intersections[J]. Journal of Transportation Engineering and Information, 2022, 20(3): 43-56.
13	刘显贵, 王晖年, 洪经纬, 等. 网联环境下信号交叉口车速控制策略及优化[J]. 交通运输系统工程与信息, 2021, 21(2): 82-90.
	Liu Xian-gui, Wang Hui-nian, Hong Jing-wei, et al. Speed control stsrategy and optimization of signalized intersection in network environment[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(2): 82-90.
14	Yao H D, Li X P. Lane-change-aware connected automated vehicle trajectory optimization at a signalized intersection with multi-lane roads[J]. Transportation Research Part C Emerging Technologies, 2021, 129:No. 103182.

Related Articles 15

[1]	Xi-zhen ZHOU,He GONG,Dun-dun LI,Yan-jie JI,Jie YAN. Nonlinear model for impact of built environment on curb parking spaces occupancy [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(9): 2520-2530.
[2]	Ya-qin QIN,Zheng-fu QIAN,Ji-ming XIE. Vehicle cooperative obstacle avoidance strategy driven by CLAM model and trajectory data [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(5): 1311-1322.
[3]	Ming-chen GU,Hui-yuan XIONG,Zeng-jun LIU,Qing-yu LUO,Hong LIU. Weight estimation model for trucks integrating multi-head attention mechanism [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(10): 2771-2780.
[4]	Wen-cai SUN,Xu-ge HU,Zhi-fa YANG,Fan-yu MENG,Wei SUN. Optimization of infrared-visible road target detection by fusing GPNet and image multiscale features [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(10): 2799-2806.
[5]	Hong-tao LI,Lin-hong WANG,Jun-da LI. Influence of lighting and speed limit on visual search ability at highway intersections [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(8): 2287-2297.
[6]	Wei-tiao WU,Kun ZENG,Wei ZHOU,Peng LI,Wen-zhou JIN. Deep learning method for bus passenger flow prediction based on multi-source data and surrogate-based optimization [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(7): 2001-2015.
[7]	Zhen-liang LIU,Cun-bao ZHAO,Yun-peng WU,Mi-na MA,Long-shuang MA. Life⁃cycle seismic resilience assessment of highway bridge networks using data⁃driven method [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(6): 1695-1701.
[8]	Hong-fei JIA,Ying-jun XU,Li-li YANG,Nan WANG. League member selection and benefit distribution of commercial vehicles multi⁃modal transportation [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(4): 1060-1069.
[9]	Yu-lin CHANG,Wen-qian XU,Chao SUN,Peng ZHANG. Day⁃to⁃day equilibrium of hybrid traffic considering obedience degree under internet of vehicles environment [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(4): 1085-1093.
[10]	Chao SUN,Hao-wei YIN,Wen-yun TANG,Zhao-ming CHU. Sensor deployment strategy and expansion inference of mobile phone data for traffic demand estimation [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(4): 1070-1077.
[11]	Hui-zhen ZHANG,Zheng-kai GAO,Jian-qiang LI,Chen-xi WANG,Yu-biao PAN,Cheng WANG,Jing WANG. Short⁃term passenger flow forecasting of urban rail transit based on recurrent neural network [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 430-438.
[12]	Jin XU,Yan-peng WANG,Hai-yuan CHEN,Xiao-bo ZHANG,Cun-shu PAN. Longitudinal driving characteristics and operating speed prediction model of cars on hairpin curves of mountainous roads [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(12): 3432-3445.
[13]	Zhi-wei LIU,Zheng-yun SONG,Jian-rong LIU. Impact of shared autonomous vehicles on choice of subway station connection methods [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(12): 3424-3431.
[14]	Xiu-feng CHEN,Yu-tong GUO,Yue-chen WU,Da-yi QU,Meng-yuan GAO. Multi-objective optimization of trafﬁc signal timings based on dandelion algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(11): 3122-3129.
[15]	Wen-jing WU,Kang-bei XIONG,Li-li YANG,Si-xu PU. Optimization of bus transfer preferential strategy in passenger corridor based on mental account theory [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(11): 3113-3121.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

对比项	流量/（veh·h^-1）
对比项	100	200	300	400	500	600
延误/%	25.46	26.17	25.17	25.60	29.81	27.29
油耗/%	48.98	49.86	49.85	51.20	51.16	50.44

对比项	绿信比
对比项	0.10	0.15	0.2	0.25	0.3	0.35
延误/%	29.52	33.27	31.57	25.60	32.97	36.12
油耗/%	55.72	56.86	55.05	52.25	53.14	51.27

对比项	借道长度/m
对比项	20	35	50	65	80	95
延误/%	26.48	26.59	25.60	25.83	25.25	25.89
油耗/%	50.23	52.39	52.25	53.47	53.05	53.04

Optimal trajectory control for connected left-turn vehicles at exit lane for left-turn intersections

RICH HTML

PDF (PC)