考虑尾气排放的交叉口信号配时多目标优化研究

doi:10.16182/j.issn1004731x.joss.24-0376

摘要/Abstract

摘要：

为缓解城市道路拥堵，提高交叉口交通效益与环境效益，建立了一种以交叉口总延误时间、总停车次数、通行能力和尾气排放总量为优化目标的多目标配时优化模型。将尾气排放量纳入到数学优化模型中，通过构建一种基于比功率的尾气排放总量测算算法对交通效益指标与尾气排放量的数学关系进行量化，根据交叉口延误时间和停车次数可实现尾气排放总量测算。引入NDX交叉算子和SDE(shift-based density estimation)策略对传统NSGA-II算法进行改进，对配时优化模型和改进NSGA-II算法进行编码，实现多目标配时优化模型求解。仿真结果表明：尾气排放总量测算模型结果误差小于10%，与传统NSGA-II算法相比，改进的NSGA-II算法收敛速度提升了约54%。

关键词: 信号交叉口, 多目标优化模型, 尾气排放测算, 改进NSGA-II, SDE策略, 环境效益

Abstract:

In order to alleviate urban road congestion and improve the traffic and environmental benefits at intersections, a multi-objective timing optimization model with total delay time, total number of stops, capacity, and total tailpipe emission at intersections as optimization objectives was developed. The model incorporated tailpipe emissions into a mathematical optimization model and quantified the mathematical relationship between traffic efficiency indicators and tailpipe emissions by constructing a specific power-based algorithm for measuring total tailpipe emissions. According to the intersection delay time and the number of stops, the total tailpipe emissions could be estimated. Both the NDX crossover operator and the shift-based density estimation (SDE) strategy were introduced to improve the design of the traditional NSGA-II algorithm, and the timing optimization model and the improved NSGA-II algorithm were coded to solve the multi-objective timing optimization model. The simulation results show that the error of the total tailpipe emission measurement model is less than 10%, and the convergence speed of the improved NSGA-II algorithm is improved by about 54% compared with the traditional NSGA-II algorithm.

Key words: signalized intersection, multi-objective optimization model, tailpipe emission measurement, improved NSGA-II, SDE strategy, environmental benefit

中图分类号:

T391.9

丁新桓,王华庆,党旭 . 考虑尾气排放的交叉口信号配时多目标优化研究[J]. 系统仿真学报, 2025, 37(10): 2687-2700.

Ding Xinhuan,Wang Huaqing,Dang Xu . Multi-objective Optimization of Signal Timing at Intersections Considering Tailpipe Emissions[J]. Journal of System Simulation, 2025, 37(10): 2687-2700.

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工况	比功率区间/(kW/t)	CO/(g/s)	HC/(g/s)	NO_x/(g/s)
1	(-∞, -2)	0.011 0	0.000 9	0.001 0
2	[-2, 0)	0.008 7	0.000 9	0.001 0
3	[0, 1)	0.004 7	0.000 8	0.000 4
4	[1, 4)	0.012 2	0.001 0	0.001 6
5	[4, 7)	0.016 7	0.001 3	0.002 6
6	[7, 10)	0.023 3	0.001 7	0.003 8
7	[10, 13)	0.029 3	0.002 1	0.005 1
8	[13, 16)	0.036 9	0.002 3	0.006 4
9	[16, 19)	0.049 5	0.002 8	0.007 7
10	[19, 23)	0.063 8	0.003 0	0.009 9
11	[23, 28)	0.105 4	0.003 8	0.012 7
12	[28, 33)	0.247 8	0.004 6	0.014 4
13	[33, 39)	0.413 1	0.005 7	0.015 6
14	[39, +∞)	0.624 7	0.007 2	0.016 7

进口	车道组	饱和流率	实际到达量
南进口	左转(1)	1 527	198
	直行(2)	3 122	498
	右转(1)	1 620	246
北进口	左转(1)	1 420	168
	直行(2)	2 980	436
	右转(1)	1 580	178
东进口	左转(1)	1 650	238
	直行(2)	3 363	899
	右转(1)	1 566	194
西进口	左转(1)	1 600	233
	直行(2)	3 560	944
	右转(1)	1 550	104

场景	权重设置		优化后配时方案/s					目标函数
场景	α	β	g₁	g₂	g₃	g₄	C	f(x)
Ⅰ	0.90	0.10	17	17	21	22	97	0.630
Ⅱ	0.80	0.20	17	18	21	20	95	0.654
Ⅲ	0.70	0.30	16	16	20	20	92	0.793
Ⅳ	0.60	0.40	18	17	22	20	97	0.680
Ⅴ	0.50	0.50	18	18	21	22	99	0.733
Ⅵ	0.40	0.60	18	18	21	20	97	0.751
Ⅶ	0.30	0.70	18	18	22	20	98	0.777
Ⅷ	0.20	0.80	17	18	21	22	98	0.795
Ⅸ	0.10	0.90	18	17	21	20	97	0.703

场景	总延误时间/s	车均延误/(s/pcu)	总停车次数	平均停车次数	通行能力/(pcu/h)
现状	133 175.0	37.80	3 375.00	0.93	4 152.00
Ⅰ	95 648.1	27.15	3 231.97	0.89	4 047.88
Ⅱ	94 814.7	26.91	3 242.61	0.90	4 029.23
Ⅲ	91 625.8	26.01	3 222.27	0.87	4 017.34
Ⅳ	96 019.0	27.25	3 239.44	0.89	4 088.74
Ⅴ	97 607.6	27.70	3 236.02	0.90	4 057.69
Ⅵ	96 568.9	27.41	3 240.86	0.91	4 032.74
Ⅶ	97 246.0	27.60	3 242.93	0.92	4 047.04
Ⅷ	96 568.3	27.41	3 231.63	0.88	4 053.13
Ⅸ	95 860.0	27.21	3 239.88	0.88	3 989.45

场景	尾气排放量/g				增长率/%
场景	总排放	CO	NO_x	HC	总排放	CO	NO_x	HC
现状	4 453.32	3 729.04	475.24	349.05
Ⅰ	3 780.21	3 165.40	392.15	222.81	-15.11	-14.72	-17.52	-36.17
Ⅱ	3 758.83	3 147.50	390.24	221.33	-15.59	-15.19	-17.94	-36.60
Ⅲ	3 682.79	3 083.83	383.60	215.36	-17.30	-16.85	-19.28	-38.31
Ⅳ	3 787.60	3 171.59	393.80	222.21	-14.95	-14.56	-17.14	-36.34
Ⅴ	3 826.46	3 204.16	390.26	232.14	-14.08	-13.71	-17.89	-33.50
Ⅵ	3 800.70	3 182.56	389.53	228.64	-14.65	-14.27	-18.04	-34.50
Ⅶ	3 816.53	3 195.79	395.09	225.71	-14.30	-13.93	-16.88	-35.34
Ⅷ	3 789.54	3 173.22	394.80	221.52	-14.91	-14.52	-16.93	-36.54
Ⅸ	3 733.50	3 126.29	388.61	218.61	-16.16	-15.74	-18.23	-37.38