Multi-objective Optimization of Signal Timing at Intersections Considering Tailpipe Emissions

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

Abstract

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

CLC Number:

T391.9

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.

Figures/Tables 17

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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