基于模糊非支配排序遗传算法的多车型快速路交通拥堵和排放优化

doi:10.12066/j.issn.1007-2861.2244

上海大学学报(自然科学版) ›› 2021, Vol. 27 ›› Issue (4): 766-784.doi: 10.12066/j.issn.1007-2861.2244

基于模糊非支配排序遗传算法的多车型快速路交通拥堵和排放优化

陈娟(), 荆昊, 孙向阳

上海大学悉尼工商学院, 上海 201899

收稿日期:2019-10-14 出版日期:2021-08-20 发布日期:2021-07-22
通讯作者: 陈娟 E-mail:chenjuan82@shu.edu.cn
作者简介:陈娟(1975—), 女, 副教授, 博士, 研究方向为智能交通系统. E-mail: chenjuan82@shu.edu.cn
基金资助:
国家自然科学基金资助项目(61104166)

Multi-class expressway traffic control for reducing congestion and emissions based on fuzzy non-dominated sorting genetic algorithm

CHEN Juan(), JING Hao, SUN Xiangyang

SHU-UTS SILC Business School, Shanghai University, Shanghai 201899, China

Received:2019-10-14 Online:2021-08-20 Published:2021-07-22
Contact: CHEN Juan E-mail:chenjuan82@shu.edu.cn

摘要/Abstract

摘要：

以优化城市多车型快速路交通系统拥堵和排放为目标, 综合考虑了走行时间(total time spent, TTS)、走行距离(total travel distance, TTD)、匝道排队、尾气排放和燃油消耗这5个性能指标, 改进了多车型快速路宏观交通流模型Multi-class METANET和多车型排放模型Multi-class VT-macro. 提出了一个新的高维多目标优化算法——模糊非支配排序遗传算法(fuzzy non-dominated sorting genetic algorithm, FNSGA-Ⅲ), 对快速路的匝道汇入率和主路的可变限速(variable speed limit, VSL)值进行了优化, 实现了缓解主路和匝道交通拥堵以及节能减排的目标. 提出的FNSGA-Ⅲ算法, 基于自适应模糊推理系统(adaptive network-base fuzzy inference system, ANFIS), 对下一时刻高维多目标优化的超平面进行预测, 能够有效引导算法在迭代过程中的进化方向, 提高算法的收敛速度. 基于上海市广中路实际路网进行仿真实验. 结果表明, 与现有的单目标遗传算法和高维多目标NSGA-Ⅲ算法相比, FNSGA-Ⅲ算法结合改进的多车型宏观交通流模型, 可以更合理地设置期望速度与匝道控制策略, 更为有效地环缓解快速路的交通拥堵和排放.

关键词: 高维多目标遗传算法, 多车型, 快速路, 拥堵, 排放

Abstract:

To minimise traffic congestion and emissions on urban multi-class expressways, five performance indicators were considered, such as total travel spent (TTS), total travel distance (TTD), ramp queuing, exhaust emissions, and fuel consumption. The macro traffic flow model (Multi-class METANET) and emission model (Multi-class VT-macro) were improved. A new high-dimensional multi-objective optimisation algorithm, fuzzy non-dominated sorting genetic algorithm (FNSGA-Ⅲ), was proposed to optimise the on-ramp inflow rate on expressways and the variable speed limit (VSL) on main roads. The new algorithm, FNSGA-Ⅲ, aimed to alleviate traffic congestion, save energy, and reduce emissions simultaneously. This proposed algorithm, which was based on an adaptive network-base fuzzy inference system (ANFIS), could effectively guide the evolution direction of the optimisation algorithm in the iteration process and improve the convergence speed. Based on the actual road network of Guangzhong Road in Shanghai, a simulation was performed. The results showed that the proposed FNSGA-Ⅲ algorithm and the improved multi-class macro traffic flow model could reasonably set the desired speed and ramp control strategy. Hence, compared with the existing single-objective genetic algorithm and multi-objective NSGA-Ⅲ, the proposed algorithm and improved model could effectively alleviate traffic congestion and reduce emissions.

Key words: high-dimensional multi-objective genetic algorithm, multi-class, expressway, congestion, emission

中图分类号:

TP312

陈娟, 荆昊, 孙向阳. 基于模糊非支配排序遗传算法的多车型快速路交通拥堵和排放优化[J]. 上海大学学报(自然科学版), 2021, 27(4): 766-784.

CHEN Juan, JING Hao, SUN Xiangyang. Multi-class expressway traffic control for reducing congestion and emissions based on fuzzy non-dominated sorting genetic algorithm[J]. Journal of Shanghai University（Natural Science Edition）, 2021, 27(4): 766-784.

图/表 17

图1

图2

图3

图4

表1

图5

表2

图6

图7

图8

图9

表3

表4

图10

图11

图12

图13

参考文献 17

[1]	梁振杰, 谢邦昌, 谢宁. 城市交通大数据研究与实践[J]. 中国统计, 2018(2):16-17.
	Liang Z J, Xie B C, Xie N. Research and practice of urban traffic big data[J]. China Statistics, 2018(2):16-17.
[2]	Cecchel S, Chindamo D, Turrini E, et al. Impact of reduced mass of light commercial vehicles on fuel consumption, CO$_{2}$ emissions, air quality, and socio-economic costs[J]. Science of the Total Environment, 2017, 613:409.
[3]	Deb K, Jain H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part Ⅰ: solving problems with box constraints[J]. IEEE Transactions on Evolutionary Computation, 2014, 18(4):577-601. doi: 10.1109/TEVC.2013.2281535
[4]	Shuai L, Hans H, Bart D S. Model predictive control for freeway networks based on multi-class traffic flow and emission models[J]. IEEE Transactions on Intelligent Transportation Systems, 2016, 18(2):1-15. doi: 10.1109/TITS.6979
[5]	Trivedi A, Srinivasan D, Sanyal K, et al. A survey of multiobjective evolutionary algorithms based on decomposition[J]. IEEE Transactions on Evolutionary Computation, 2017, 21(3):440-462.
[6]	Tian Y, Cheng R, Zhang X, et al. An indicator based multi-objective evolutionary algorithm with reference point adaptation for better versatility[J]. IEEE Transactions on Evolutionary Computation, 2018, 22(4):609-622. doi: 10.1109/TEVC.2017.2749619
[7]	Ishibuchi H, Setoguchi Y, Masuda H, et al. Performance of decomposition-based many-objective algorithms strongly depends on pareto front shapes[J]. IEEE Transactions on Evolutionary Computation, 2017, 21(2):169-190. doi: 10.1109/TEVC.2016.2587749
[8]	许超. 基于实测数据的宏观交通流模型标定研究与实现[D]. 北京: 北京工业大学, 2017.
	Xu C. Research and implementation of macro traffic flow model calibration based on measured data[D]. Beijing: Beijing University of Technology, 2017.
[9]	Zegeye S K, De-Schutter B, Hellendoorn J, et al. Integrated macroscopic traffic flow, emission, and fuel consumption model for control purposes[J]. Transportation Research Part C (Emerging Technologies), 2013, 31:158-171.
[10]	Han Y, Ahn S. Variable speed release (VSR): speed control to increase bottleneck capacity[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 21(1):1-10.
[11]	Li S, Cao D. Variable speed limit strategies' analysis with cell transmission model onfreeway[J]. Modern Physics Letters B, 2017, 31(24):17502190.
[12]	Iordanidou G R, Papamichail I, Roncoli C, et al. Feedback-based integrated motorway traffic flow control with delay balancing[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(9):2319-2329. doi: 10.1109/TITS.2016.2636302
[13]	Pasquale C, Sacone S, Siri S, et al. Optimal control for reducing congestion and improving safety in freeway systems[J]. IEEE Transactions on Intelligent Transportation Systems, 2018, 19(11):3613-3625. doi: 10.1109/TITS.2018.2868987
[14]	Kromer P, Platos J. Simultaneous prediction of wind speed and direction by evolutionary fuzzy rule forest[J]. Procedia Computer Science, 2017, 108:295-304. doi: 10.1016/j.procs.2017.05.195
[15]	Deb K, Agrawal S, Pratap A, et al. A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-Ⅱ[J]. Lecture Notes in Computer Science, 2000, 1917:849-858.
[16]	Khondaker B, Kattan L. Variable speed limit: a microscopic analysis in a connected vehicle environment[J]. Transportation Research Part C (Emerging Technologies), 2015, 58:146-159.
[17]	Pasquale C, Papamichail I, Roncoli C, et al. Two-class freeway traffic regulation to reduce congestion and emissions via nonlinear optimal control[J]. Transportation Research Part C (Emerging Technologies), 2015, 55:85-99.

基于模糊非支配排序遗传算法的多车型快速路交通拥堵和排放优化

Multi-class expressway traffic control for reducing congestion and emissions based on fuzzy non-dominated sorting genetic algorithm

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 17

参考文献 17

相关文章 1

编辑推荐

Metrics

本文评价