数据驱动的偶发拥堵时空建模及传播分析
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摘要
为研究实际的道路交通路网中偶发拥堵的传播和演化特性,充分发挥海量交通流数据的潜在价值,克服现有基于模拟仿真的拥堵分析方法因理论假设和参数设置所导致的"失真"问题,本文在交通流实测数据的基础上,建立改进的PLS-STAR模型对偶发拥堵的时空传播结构进行描述,并提出偶发拥堵的直接和间接时空传播效应两种概念对拥堵的时空传播影响进行刻画,从而构造了一种数据驱动的偶发拥堵时空传播效应评估方法.通过北京路网的案例研究发现,路网服务水平的降低,更大程度来源于拥堵传播的间接影响而非直接取决于突发的交通量增加,因此,通过控制拥堵传播来提升城市路网的服务水平仍具有巨大潜力.
For further analyzing the propagation and evolution characteristics of sudden congestion in actual road network, eliminating the distortion problem commonly existing in traditional simulation-based method because of superfluous assumptions or parameters, and taking advantage of the oceans of traffic flow data, an improved PLSSTAR model is developed to characterize the complex space-time propagation structure of sudden congestion on the basis of analyzing and identifying the sudden disruption using real traffic flow data. Then a congestion propagation effect evaluation method based on PLS-STAR is proposed based on the direct and indirect effects of space-time congestion propagation. By case studies on the road network in Beijing, it can be concluded that the reduction of road network's service level is more attributed to the indirect effect of traffic congestion propagation,rather than the sudden increase of traffic volume. Therefore, there is still great potential to improve the service level of the urban road network by controlling congestion propagation effect.
For further analyzing the propagation and evolution characteristics of sudden congestion in actual road network, eliminating the distortion problem commonly existing in traditional simulation-based method because of superfluous assumptions or parameters, and taking advantage of the oceans of traffic flow data, an improved PLSSTAR model is developed to characterize the complex space-time propagation structure of sudden congestion on the basis of analyzing and identifying the sudden disruption using real traffic flow data. Then a congestion propagation effect evaluation method based on PLS-STAR is proposed based on the direct and indirect effects of space-time congestion propagation. By case studies on the road network in Beijing, it can be concluded that the reduction of road network's service level is more attributed to the indirect effect of traffic congestion propagation,rather than the sudden increase of traffic volume. Therefore, there is still great potential to improve the service level of the urban road network by controlling congestion propagation effect.
引文
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[14]韦伟,毛保华,陈绍宽,等.基于当期事件识别的拥堵传播特征研究[J].交通运输系统工程与信息, 2016, 16(4):165-170.[WEI W, MAO B H, CHEN S K, et al.Spatial propagating study of urban traffic congestion based on current event recognition[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(4):165-170.]
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[16] KERNER B S. Three-phase traffic theory and highway capacity[J]. Physica A Statistical Mechanics&Its Applications, 2002, 333(1):379-440.
[2]杨泳.城市快速路交通拥堵形成、传播规律及消散控制策略研究[D].成都:西南交通大学, 2015.[YANG Y. Study on congestion forming, propagation properties and dissipation control strategies of urban expressway[D]. Chengdu:Southwest Jiaotong University, 2015.]
[3]谈晓洁,周晶,盛昭瀚.城市交通拥挤特征及疏导决策分析[J].管理工程学报, 2003, 17(1):56-60.[TAN X J,ZHOU J, SHENG Z H. Analysis of characteristic and decision making for management of urban traffic congestion[J]. Journal of Industrial Engineering and Engineering Management, 2003, 17(1):56-60.]
[4]高自友,龙建成,李新刚.城市交通拥堵传播规律与消散控制策略研究[J].上海理工大学学报, 2011, 33(6):701-708.[GAO Z Y, LONG J C, LI X G. Congestion propagation law and dissipation control strategies for urban traffic[J]. Journal of University of Shanghai for Science and Technology, 2011, 33(6):701-708.]
[5] YAO Y, HONG Y, WU D, et al. Estimating the effects of“ community opening” policy on alleviating traffic congestion in large Chinese cities by integrating ant colony optimization and complex network analyses[J]. Computers Environment&Urban Systems,2018(7):163-174.
[6]李树彬,吴建军,高自友,等.基于复杂网络的交通拥堵与传播动力学分析[J].物理学报, 2011, 60(5):146-154.[LI S B, WU J J, GAO Z Y, et al. The analysis of traffic congestion and dynamic propagation properties based on complex network[J]. Acta Phys. Sin., 2011, 60(5):146-154.]
[7] ZHANG Z, WOLSHON B, DIXIT V V. Integration of a cell transmission model and macroscopic fundamental diagram:Network aggregation for dynamic traffic models[J]. Transportation Research Part C, 2015(55):298-309.
[8] LAVAL J A, DAGANZO C F. Lane-changing in traffic stream[J]. Transportation Research Part B, 2006, 40(3):251-264.
[9] ZHOU M, QU X, LI X. A recurrent neural network based microscopic car following model to predict traffic oscillation[J]. Transportation Research Part C, 2017(84):245-264.
[10] SUN D, CHEN D, ZHAO M, et al. Linear stability and nonlinear analyses of traffic waves for the general nonlinear car-following model with multi-time delays[J]. Physica A Statistical Mechanics&Its Applications, 2018(501):293-307.
[11]陈绍宽,韦伟,毛保华,等.基于改进时空Moran's I指数的道路交通状态特征分析[J].物理学报, 2013, 62(14):527-533.[CHEN S K, WEI W, MAO B H, et al.Analysis of urban traffic status based on spatiotemporal Moran's I[J]. Acta Phys. Sin., 2013, 62(14):527-533.]
[12] CHOWDHURY D, SANTEN L, SCHADSCHNEIDER A.Statistical physics of vehicular traffic and some related systems[Z]. Physics Reports, 2000, 329(4):199-329.
[13]邱敦国,杨红雨.一种基于双周期时间序列的短时交通流预测算法[J].四川大学学报(工程科学版), 2013,45(5):64-68.[QIU D G, YANG H Y. A short-term traffic flow forecast algorithm based on double seasonal time series[J]. Journal of Sichuan University(Engineering Science Edition), 2013, 45(5):64-68.]
[14]韦伟,毛保华,陈绍宽,等.基于当期事件识别的拥堵传播特征研究[J].交通运输系统工程与信息, 2016, 16(4):165-170.[WEI W, MAO B H, CHEN S K, et al.Spatial propagating study of urban traffic congestion based on current event recognition[J]. Journal of Transportation Systems Engineering and Information Technology, 2016, 16(4):165-170.]
[15]曲亚鑫.基于历史数据的偏最小二乘建模方法研究与应用[D].北京:华北电力大学, 2012.[QU Y X.Research and application of partial least squares modeling methods based on historical data[D]. Beijing:North China Electric Power University, 2012.]
[16] KERNER B S. Three-phase traffic theory and highway capacity[J]. Physica A Statistical Mechanics&Its Applications, 2002, 333(1):379-440.