交通信息物理系统中的车辆协同运行优化调度
|
摘要
运输成本及温室气体的排放是衡量智能交通系统的重要指标,有效的运输调度可以降低运输成本和环境损害.针对路网中集成环保型货车的运输问题,本文提出一种基于交通信息物理系统(Transportation cyber physical system, TCPS)的大规模车辆协同调度及合并方案,以最大限度地降低运输成本和碳排放量.首先,采用局部调度策略,结合领队车辆选择算法及聚类分析,构建可合并车辆集合;然后,通过数学规划方法,实现每个车队集合中车辆路径与速度的改进优化处理;最后,通过突发情况的简易处理说明本文调度策略的可扩展性.仿真实验表明,用本文方法将车辆编组合并成车队行驶,较固定路径合并策略可显著降低路网中货运车辆的整体油耗.
Transportation costs and greenhouse gas emissions are important indicators of intelligent transportation systems. Effective transport scheduling can reduce transportation cost and environmental damage. This paper proposes a large-scale vehicle coordinated scheduling and merging strategy for large-scale vehicles based on transportation cyber physical systems(TCPS) to minimize transportation costs and carbon emissions. Firstly, a local scheduling strategy is used in combination with the leader vehicle selection algorithm and cluster analysis to construct the vehicle merging set.Then, through the mathematical programming method, the improvement and optimization of vehicle path and speed in each platooning set are realized. Finally, the expandability of the scheduling strategy is proved by the simple processing of emergency situations. Numerical simulation has shown that the method presented in this paper to schedule vehicle can greatly reduce the overall fuel consumption of vehicle fleets in fact compared to the fixed path merging strategy.
Transportation costs and greenhouse gas emissions are important indicators of intelligent transportation systems. Effective transport scheduling can reduce transportation cost and environmental damage. This paper proposes a large-scale vehicle coordinated scheduling and merging strategy for large-scale vehicles based on transportation cyber physical systems(TCPS) to minimize transportation costs and carbon emissions. Firstly, a local scheduling strategy is used in combination with the leader vehicle selection algorithm and cluster analysis to construct the vehicle merging set.Then, through the mathematical programming method, the improvement and optimization of vehicle path and speed in each platooning set are realized. Finally, the expandability of the scheduling strategy is proved by the simple processing of emergency situations. Numerical simulation has shown that the method presented in this paper to schedule vehicle can greatly reduce the overall fuel consumption of vehicle fleets in fact compared to the fixed path merging strategy.
引文
1 OECD/International Transport Forum,International Transport Outlook 2013:Funding Transport.Paris,France:OECD Publishing/ITF,2013.
2 Papageorgiou M.Overview of road traffic control strategies.Ifac Proceedings Volumes,2004,37(19):29-40
3 Crainic T G,Laporte G.Planning models for freight transportation.European Journal of Operational Research,1997,97(3):409-438
4 Demir E,Bekta T,Laporte G.A review of recent research on green road freight transportation.European Journal of Operational Research,2014,237(3):775-793
5 Karagiannis G.Vehicular networking:a survey and tutorial on requirements,architectures,challenges,standards and solutions.IEEE Communications Surveys and Tutorials,2011,13(4):584-616
6 Varaiya P.Control design of an automated highway system.Proceedings of the IEEE,2000,88(7):913-925
7 Liang K Y,Martensson J,Johansson K H.Heavy-duty vehicle platoon formation for fuel efficiency.IEEE Transactions on Intelligent Transportation Systems,2016,17(4):1051-1061
8 Larson J,Kammer C,Liang K Y.Coordinated route optimization for heavy-duty vehicle platoons.In:Proceedings of the 2013 IEEE Intelligent Transportation Systems.Hague,Netherlands:IEEE,2013.1196-1202
9 Van De Hoef S,Johansson K H,Dimarogonas D V.Fueloptimal centralized coordination of truck platooning based on shortest paths.In:Proceedings of the 2015 American Control Conference.Chicago,America:IEEE,2015.3740-3745
10 Meisen P,Seidl T,Henning K.A data-mining technique for the planning and organization of truck platoons.In:Proceedings of the 2018 International Conference on Heavy Vehicles.Paris,France:John Wiley and Sons,Inc,2008.19-22
11 Van D H S,Johansson K H,Dimarogonas D V.Coordinating truck platooning by clustering pairwise fuel-optimal plans.In:Proceedings of the 2015 Intelligent Transportation Systems.Las Palmas de Gran Canaria,Spain:IEEE,2015.408-415
12 Boriboonsomsin K,Barth M J,Zhu W.Eco-routing navigation system based on multisource historical and real-time traffic information.IEEE Transactions on Intelligent Transportation Systems,2012,13(4):1694-1704
13 Kaufman L,Rousseeuw P J.Finding Groups in Data:An Introduction to Cluster Analysis[Ph.D.dissertation].Hoboken,NJ,USA:Wiley,2008.
14 Jain A K.Data clustering:50 years beyond k-means.In:Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases.Antwerp,Belgium:Springer-Verlag,2008.3-4
15 Besselink B,Turri V,van de Hoef S H.Cyber-physical control of road freight transport.Proceedings of the IEEE,2016,104(5):1128-1141
16 Sanders P,Schultes D.Engineering fast route planning algorithms.In:Proceedings of the 2007 International Conference on Experimental Algorithms.Rome,Italy:SpringerVerlag,2007.23-36
17 Liang K Y,Martensson J,Johansson K H.Heavy-duty vehicle platoon formation for fuel efficiency.IEEE Transactions on Intelligent Transportation Systems,2016,17(4):1051-1061
2 Papageorgiou M.Overview of road traffic control strategies.Ifac Proceedings Volumes,2004,37(19):29-40
3 Crainic T G,Laporte G.Planning models for freight transportation.European Journal of Operational Research,1997,97(3):409-438
4 Demir E,Bekta T,Laporte G.A review of recent research on green road freight transportation.European Journal of Operational Research,2014,237(3):775-793
5 Karagiannis G.Vehicular networking:a survey and tutorial on requirements,architectures,challenges,standards and solutions.IEEE Communications Surveys and Tutorials,2011,13(4):584-616
6 Varaiya P.Control design of an automated highway system.Proceedings of the IEEE,2000,88(7):913-925
7 Liang K Y,Martensson J,Johansson K H.Heavy-duty vehicle platoon formation for fuel efficiency.IEEE Transactions on Intelligent Transportation Systems,2016,17(4):1051-1061
8 Larson J,Kammer C,Liang K Y.Coordinated route optimization for heavy-duty vehicle platoons.In:Proceedings of the 2013 IEEE Intelligent Transportation Systems.Hague,Netherlands:IEEE,2013.1196-1202
9 Van De Hoef S,Johansson K H,Dimarogonas D V.Fueloptimal centralized coordination of truck platooning based on shortest paths.In:Proceedings of the 2015 American Control Conference.Chicago,America:IEEE,2015.3740-3745
10 Meisen P,Seidl T,Henning K.A data-mining technique for the planning and organization of truck platoons.In:Proceedings of the 2018 International Conference on Heavy Vehicles.Paris,France:John Wiley and Sons,Inc,2008.19-22
11 Van D H S,Johansson K H,Dimarogonas D V.Coordinating truck platooning by clustering pairwise fuel-optimal plans.In:Proceedings of the 2015 Intelligent Transportation Systems.Las Palmas de Gran Canaria,Spain:IEEE,2015.408-415
12 Boriboonsomsin K,Barth M J,Zhu W.Eco-routing navigation system based on multisource historical and real-time traffic information.IEEE Transactions on Intelligent Transportation Systems,2012,13(4):1694-1704
13 Kaufman L,Rousseeuw P J.Finding Groups in Data:An Introduction to Cluster Analysis[Ph.D.dissertation].Hoboken,NJ,USA:Wiley,2008.
14 Jain A K.Data clustering:50 years beyond k-means.In:Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases.Antwerp,Belgium:Springer-Verlag,2008.3-4
15 Besselink B,Turri V,van de Hoef S H.Cyber-physical control of road freight transport.Proceedings of the IEEE,2016,104(5):1128-1141
16 Sanders P,Schultes D.Engineering fast route planning algorithms.In:Proceedings of the 2007 International Conference on Experimental Algorithms.Rome,Italy:SpringerVerlag,2007.23-36
17 Liang K Y,Martensson J,Johansson K H.Heavy-duty vehicle platoon formation for fuel efficiency.IEEE Transactions on Intelligent Transportation Systems,2016,17(4):1051-1061