基于熵和流体力学的城市主干道交通状态判别方法研究
|
摘要
随着汽车保有量的迅速增加,道路交通运行效率受到了严重地影响。因此,研究道路交通状态判别算法,及时准确的判别交通状态,具有重要的理论指导意义与实际应用价值。本文以非平衡态热力学和流体力学为理论基础,从热力学中选取适当的熵模型,并结合道路交通系统自身的特性,在交通流理论中选择关键的、能够与所选取的热力学熵模型参数相对应的参数,建立了道路交通系统熵模型和基于熵模型的状态判别算法。
论文选用非平衡态热力学熵及流体力学原理对道路系统进行关联性分析。对于交通系统这样一个复杂的开放系统,其与热力学系统具有相似之处,具备非平衡态热力学系统的相关特征,可遵循热力学的基本规律,引入热力学熵来描述道路交通系统。利用道路中交通流与流体的相似性,建立交通流与流体概念体系的比照关系,进而运用流体力学中质量守恒定理、牛顿第二定律等原理,针对道路车流进行分析,导出道路交通压力的表达式。
将热力学熵的基本理论引入到交通流研究中,提出道路交通系统熵及耗散结构特征描述方法,道路交通系统熵值越小,意味着系统混乱度越小；熵值越大,系统混乱度越大。在提出流体力学道路交通压力的概念基础上,选取适当的熵模型和关键的交通状态参数,运用非平衡态热力学的理论方法建立道路交通系统熵模型和负熵流模型。由熵模型计算得出道路交通状态熵值和熵产生,用量化的状态熵的数值表示交通拥挤度；建立基于熵模型的有效的状态判别算法,从而实现对道路交通系统状态的判别,而负熵流模型则能确定道路拥堵时应该采取的交通管理量化程度以及最佳管理时段。
论文以哈尔滨市某一城市主干道作为研究对象,采集不同交通状态下的交通流特征参数,运用交通系统熵模型对交通状态进行判别。对路段采集的交通信息不包含偶发性拥挤状态,为了更全面的验证判别算法,选取哈尔滨市某一交叉口模拟偶发性拥挤发生状态,利用计算得到的判别结果与道路实际交通状态进行回归分析,验证判别算法的有效性并提出改进方法。
The road traffic operating efficiency is impacted by the rapid increasing of vehicle in urban very seriously. Therefore, the study of road traffic state discrimination algorithm in order to discriminate the traffic state timely and accurate has an important theoretical and practical significance. This paper is based on non-equilibrium thermodynamics and fluid mechanics theory. This paper selects some appropriate entropy models from thermodynamic. Combining with the characteristics of road traffic system, some key parameters are selected from the traffic flow theory in order to fit the parameters of the thermodynamic entropy model. This paper establishes the road traffic system entropy model and the status discrimination algorithm which is based on entropy model.
This Paper uses non-equilibrium thermodynamics entropy and hydrodynamics theory to analysis road system's association. Transportation system is a complex open system which is similar with the thermodynamic system. It also has the relevant characteristics of the non-equilibrium thermodynamics system and follows the basic discipline of thermodynamic. So, entropy can be used to describe the road transport system. This Paper uses the similarity of traffic flow and fluid to establish the concept system's comparison relationship of traffic flow and hydrodynamics. Conservation of mass, Newton's second Law and other theorems of hydrodynamics are used to analyze road vehicle flow and elicit the expression of road traffic pressure.
This paper takes the basic theory of the thermodynamic entropy into the field of traffic flow and advances the theory of road traffic system entropy and dissipation structure. The value of road traffic system entropy is smaller the degree of confusion is smaller. On the other way, the value of road traffic system entropy is bigger the degree of confusion is bigger. On the basis of hydrodynamic traffic pressure, the paper selects the appropriate entropy model and the key traffic state parameter. The theory of non-equilibrium thermodynamics is used to establish the model of road traffic system entropy and the negative entropy traffic flow model. The value of road traffic state entropy and entropy production are calculated by the entropy model.The degree of traffic crowd is expressed by the value of quantificational state entropy. The effective state discrimination arithmetic is established based on entropy model. So, this paper can discriminate the state of road traffic. The negative entropy flow model can ensure the extent of traffic management and the best management time should be taken when road is crowed.
This Paper takes a section of trunk road which is in the city of Harbin as the object of study. Traffic flow characteristic parameter is collected at different traffic states. Traffic system entropy model is used to discriminate traffic state. Traffic information which is collected on the road does not include occasional crowded state. In order to verify discrimination arithmetic comprehensive, the paper selects an intersection to simulate crowd state what happen once in a while. This paper uses discrimination result and real road traffic state to do regression analysis. The validity of discrimination arithmetic is verified and some advanced methods are lodged.
论文选用非平衡态热力学熵及流体力学原理对道路系统进行关联性分析。对于交通系统这样一个复杂的开放系统,其与热力学系统具有相似之处,具备非平衡态热力学系统的相关特征,可遵循热力学的基本规律,引入热力学熵来描述道路交通系统。利用道路中交通流与流体的相似性,建立交通流与流体概念体系的比照关系,进而运用流体力学中质量守恒定理、牛顿第二定律等原理,针对道路车流进行分析,导出道路交通压力的表达式。
将热力学熵的基本理论引入到交通流研究中,提出道路交通系统熵及耗散结构特征描述方法,道路交通系统熵值越小,意味着系统混乱度越小；熵值越大,系统混乱度越大。在提出流体力学道路交通压力的概念基础上,选取适当的熵模型和关键的交通状态参数,运用非平衡态热力学的理论方法建立道路交通系统熵模型和负熵流模型。由熵模型计算得出道路交通状态熵值和熵产生,用量化的状态熵的数值表示交通拥挤度；建立基于熵模型的有效的状态判别算法,从而实现对道路交通系统状态的判别,而负熵流模型则能确定道路拥堵时应该采取的交通管理量化程度以及最佳管理时段。
论文以哈尔滨市某一城市主干道作为研究对象,采集不同交通状态下的交通流特征参数,运用交通系统熵模型对交通状态进行判别。对路段采集的交通信息不包含偶发性拥挤状态,为了更全面的验证判别算法,选取哈尔滨市某一交叉口模拟偶发性拥挤发生状态,利用计算得到的判别结果与道路实际交通状态进行回归分析,验证判别算法的有效性并提出改进方法。
The road traffic operating efficiency is impacted by the rapid increasing of vehicle in urban very seriously. Therefore, the study of road traffic state discrimination algorithm in order to discriminate the traffic state timely and accurate has an important theoretical and practical significance. This paper is based on non-equilibrium thermodynamics and fluid mechanics theory. This paper selects some appropriate entropy models from thermodynamic. Combining with the characteristics of road traffic system, some key parameters are selected from the traffic flow theory in order to fit the parameters of the thermodynamic entropy model. This paper establishes the road traffic system entropy model and the status discrimination algorithm which is based on entropy model.
This Paper uses non-equilibrium thermodynamics entropy and hydrodynamics theory to analysis road system's association. Transportation system is a complex open system which is similar with the thermodynamic system. It also has the relevant characteristics of the non-equilibrium thermodynamics system and follows the basic discipline of thermodynamic. So, entropy can be used to describe the road transport system. This Paper uses the similarity of traffic flow and fluid to establish the concept system's comparison relationship of traffic flow and hydrodynamics. Conservation of mass, Newton's second Law and other theorems of hydrodynamics are used to analyze road vehicle flow and elicit the expression of road traffic pressure.
This paper takes the basic theory of the thermodynamic entropy into the field of traffic flow and advances the theory of road traffic system entropy and dissipation structure. The value of road traffic system entropy is smaller the degree of confusion is smaller. On the other way, the value of road traffic system entropy is bigger the degree of confusion is bigger. On the basis of hydrodynamic traffic pressure, the paper selects the appropriate entropy model and the key traffic state parameter. The theory of non-equilibrium thermodynamics is used to establish the model of road traffic system entropy and the negative entropy traffic flow model. The value of road traffic state entropy and entropy production are calculated by the entropy model.The degree of traffic crowd is expressed by the value of quantificational state entropy. The effective state discrimination arithmetic is established based on entropy model. So, this paper can discriminate the state of road traffic. The negative entropy flow model can ensure the extent of traffic management and the best management time should be taken when road is crowed.
This Paper takes a section of trunk road which is in the city of Harbin as the object of study. Traffic flow characteristic parameter is collected at different traffic states. Traffic system entropy model is used to discriminate traffic state. Traffic information which is collected on the road does not include occasional crowded state. In order to verify discrimination arithmetic comprehensive, the paper selects an intersection to simulate crowd state what happen once in a while. This paper uses discrimination result and real road traffic state to do regression analysis. The validity of discrimination arithmetic is verified and some advanced methods are lodged.
引文
[1]彭德胜.我国大城市交通问题的探讨[J].上海：同济大学建筑城规学院,1995：27-28
[2]姜桂艳.道路交通状态判别技术与应用[M].北京：人民交通出版社,2004.
[3]Stephen GRitchie Ruey L.Cheu.Neural Network Models for Automated Detection of Non-Recurring Congestion[R].University of California, Irvine,1993:10-16
[4]张秀媛,达庆东,张国伍.公路自动事件检测技术[J].系统工程理论与实践,2001,21(6)：118-124
[5]Justin Black,Indu Sreedevi.Automatic Incident Detection Algorithms[M].2001.
[6]A.Hegyi,D.Girimonte,R.Babuska,B.DeSehutter.A comparison of filter Configurations for freeway traffic state estimation[C].Proceedings of the IEEE ITSC 2006.2006 IEEE Intelligent Transportation Systems Conference.Toronto,Canada,SePtemberl 7-20,2006: 1029-1034
[7]Y.Wang,M.Papageorgiou,A.Messmer.An Adaptive Freeway Traffic State Estimator and Its Real-Data Testing-Part l:Basic Properties.Proceedings of the 8th International [C].IEEE Conference on Intelligent Transportation Systems.Vienna,Austria,SePtemberl3-16,2005: 531-536
[8]Jung-Taek Lee.Incident detection algorithm development on signalized urban arterial streets[D].Michigan State University,1997:52-64
[9]Jiuh-Biing Sheu,Stephen GRitchie.A new methodology for incident detection and characterization on surface streets[J].Transportation Research Part C 6,1998:315-335
[10]John N.Ivan.Neural network representations for arterial street incident detection data fusion[J].Transportation Research 1997,Part C vol.5.No:3/4:245-254
[11]Kim Thomas and Hussein Dia.A neural network model for arterial incident detection using probe vehicle and fixed detector data[J].CAITA 2000:32-42
[12]James and Justice. Maximum Entropy and Bayesian Methods in Applied Statictics.Proceedings of the Fourth Maximum Entropy Workshop[C],University of Calgary,1984:21-27
[13]Gyltopoulosep. Entropies of statistical mechanics and disorder versus the Entropy of the Thermodynamics and order[J].Journal of Energy Resources Technology,2001,123 (2):110-118
[14]Dasnc,Chakrabrticg.Traffic Network and Distriburtion of Cars:Maximum-Entropy Approach[J].Journal of Trans-portation Engineering,2000,126(1):89-92
[15]郭恒明,张鹏飞.基于环形线圈的城市道路段交通异常自动检测方法研究[J].上海公路,2001,No.4(12)：80-84
[16]彭春露,彭国雄.城市道路交通异常事件管理系统设计[J].交通运输系统工程与信息,2002,2(3)：19-23
[17]庄斌,杨晓光,李克平.道路交通拥挤事件判别准则与检测算法[J].中国公路学报,2006,19(3)：82-86
[18]杨兆升,杨庆芳,冯金巧.基于模糊综合推理的道路交通事件识别算法[J].公路交通科技,2003,20(4)：92-94
[19]姜紫峰,刘小坤.基于神经网络的交通事件检测算法[J].西安公路交通大学学报,2000,20(3)：67-69
[20]史新宏,蔡伯根.高速公路自动事件检测算法[J].交通运输系统工程与信息,2001,1(4)：306-310
[21]肖永来.基于SCATS采集数据的城市道路交通状态判别技术研究[J].中国交通信息产业,2005,(6)：39-41
[22]靳文舟,张杰.最大似然思想和最大熵思想在交通状态分析中的一致性[J].公路交通科技,2001,18(4)：66-69
[23]任江涛,欧晓凌,张毅,胡东成.交通状态模式识别研究[J].公路交通科技,2003,20(2).63-67
[24]吴正.关于交通流动力学模型与交通状态指数研究[J].水动力学研究与进展,2003,18(4)：403-407
[25]郭伟,姚丹亚,付毅,胡坚明,刘宁.区域交通流特征提取与交通状态评估方法研究[J].公路交通科技,2005,22(7)：101-104,114
[26]新疆维吾尔自治区科学技术协会.熵与交叉科学[M].北京：气象出版社,1988.
[27]俞礼军,严海,严宝杰.最大熵原理在交通流统计分布模型中的应用[J].交通运输工程学报,2001,1(3)：91-94
[28]达庆东.交通分布与熵[J].公路交通科技,1999,16(1)：37-39
[29]靳文舟,张杰.最大似然思想和最大熵思想在交通状态分析中的一致性[J].公路交通科技,2001,18(4)：66-69
[30]刘勇.道路交通系统熵理论应用研究[D].长安大学硕士学位论文.2003：13-20
[31]张岐山.灰关联熵分析方法[J].系统工程理论与实践,1996：7-11
[32]张学文,马力.大气的热力学总熵[J].大气科学,1992,16(3)：339-344
[33]汪志诚,热力学·统计物理(第二版)[M].北京：高等教育出版社,1993.
[34]刘芸.基于流体力学及熵原理的集装箱港区交通流研究[D].中国海洋大学博士论文.2008：39-68
[35]岑威.基于熵及耗散结构理论的逆向物流系统研究[D].天津师范大学硕士学位论文.2009：11-18
[36]William H.cropper,Rudolf Clausius and the road to Entropy,American Journal of Physics[J].December 1986-Volume 54, Issue 12:1068-1074
[37]欧阳容百.热力学与统计物理[M].北京：科学出版社,2007.
[38]刘勇.基于热力学熵和混沌理论的城市道路交通系统的研究[D].长安大学博士学位论文.2009：64-67
[39]庄钟锐.基于熵理论的作战指挥系统组织结构(OCSOS)描述与评价[D].国防科技大学硕士学位论文.2009：12-15
[40]熊烈强.交通流理论及其在高速公路中的应用研究[D].武汉理工大学博士论文.2003：42-50
[41]Ross P. Traffic dynamics[J].Transp. Res.,1988,22B(6):421-435
[42]Balham O, Middleton A.Self-organization and a Dynamical Transition Traffic Flow Models[J].Physical Review,1992,46A(10):46-54
[43]薛郁.随机计及相对速度的交通流跟驰模型.物理学报,2003.52(11)：2750-2756
[44]Whklam et al. A Stochastic User Equilibrium Assignment Model for Congested Transit networks [J].Transportation Research Part B 33 1999:351-368
[45]Lighthill,M.H.and GB.Whilham.on kinematics waves:A Theory of Traffic Flow on Long Crowded Roads Proceeding[J].Royal Society(London),1955,A229,No.1178:317-345
[46]Payne H.J.Model of freeway traffic and control[J].In:Bekey GA(ed)Mathematical methods of Public System,1971,Vol.Ⅰ,No.1:51-61
[47]程瑶.交通流压缩与粘滞特性研究[D].吉林大学硕士论文.2007：2-10
[48]陈丽文.城市交通噪声分析及其系统研究[D].内蒙古工业大学硕士论文.2006：9-12
[49]Phillips W F. A new continuum traffic model obtained from kinetic theory[J]. IEEE Transp. Autom. Control,1978,AC-23:1032-1036
[50]Daganzo C F. The cell transmission model:A dynamic representation of highway traffic consistent with the hydrodynamic theory[J].Transp. Res.,1994,28B(4):269-287
[51]Payne H J. A critical review of a macroscopic freeway model[J].Proc. Research Directions in Computer Control of Urban Traffic Systems,1979:251-265
[52]李进平.交通流的流体力学模型与数值模拟[D].武汉理工大学硕士论文.2003：12-22
[53]马国旗.城市道路交通流特征参数研究[D].北京工业大学硕士论文.2004：9-12
[54]李江.交通工程学[M].北京：人民交通出版社,2002.
[2]姜桂艳.道路交通状态判别技术与应用[M].北京：人民交通出版社,2004.
[3]Stephen GRitchie Ruey L.Cheu.Neural Network Models for Automated Detection of Non-Recurring Congestion[R].University of California, Irvine,1993:10-16
[4]张秀媛,达庆东,张国伍.公路自动事件检测技术[J].系统工程理论与实践,2001,21(6)：118-124
[5]Justin Black,Indu Sreedevi.Automatic Incident Detection Algorithms[M].2001.
[6]A.Hegyi,D.Girimonte,R.Babuska,B.DeSehutter.A comparison of filter Configurations for freeway traffic state estimation[C].Proceedings of the IEEE ITSC 2006.2006 IEEE Intelligent Transportation Systems Conference.Toronto,Canada,SePtemberl 7-20,2006: 1029-1034
[7]Y.Wang,M.Papageorgiou,A.Messmer.An Adaptive Freeway Traffic State Estimator and Its Real-Data Testing-Part l:Basic Properties.Proceedings of the 8th International [C].IEEE Conference on Intelligent Transportation Systems.Vienna,Austria,SePtemberl3-16,2005: 531-536
[8]Jung-Taek Lee.Incident detection algorithm development on signalized urban arterial streets[D].Michigan State University,1997:52-64
[9]Jiuh-Biing Sheu,Stephen GRitchie.A new methodology for incident detection and characterization on surface streets[J].Transportation Research Part C 6,1998:315-335
[10]John N.Ivan.Neural network representations for arterial street incident detection data fusion[J].Transportation Research 1997,Part C vol.5.No:3/4:245-254
[11]Kim Thomas and Hussein Dia.A neural network model for arterial incident detection using probe vehicle and fixed detector data[J].CAITA 2000:32-42
[12]James and Justice. Maximum Entropy and Bayesian Methods in Applied Statictics.Proceedings of the Fourth Maximum Entropy Workshop[C],University of Calgary,1984:21-27
[13]Gyltopoulosep. Entropies of statistical mechanics and disorder versus the Entropy of the Thermodynamics and order[J].Journal of Energy Resources Technology,2001,123 (2):110-118
[14]Dasnc,Chakrabrticg.Traffic Network and Distriburtion of Cars:Maximum-Entropy Approach[J].Journal of Trans-portation Engineering,2000,126(1):89-92
[15]郭恒明,张鹏飞.基于环形线圈的城市道路段交通异常自动检测方法研究[J].上海公路,2001,No.4(12)：80-84
[16]彭春露,彭国雄.城市道路交通异常事件管理系统设计[J].交通运输系统工程与信息,2002,2(3)：19-23
[17]庄斌,杨晓光,李克平.道路交通拥挤事件判别准则与检测算法[J].中国公路学报,2006,19(3)：82-86
[18]杨兆升,杨庆芳,冯金巧.基于模糊综合推理的道路交通事件识别算法[J].公路交通科技,2003,20(4)：92-94
[19]姜紫峰,刘小坤.基于神经网络的交通事件检测算法[J].西安公路交通大学学报,2000,20(3)：67-69
[20]史新宏,蔡伯根.高速公路自动事件检测算法[J].交通运输系统工程与信息,2001,1(4)：306-310
[21]肖永来.基于SCATS采集数据的城市道路交通状态判别技术研究[J].中国交通信息产业,2005,(6)：39-41
[22]靳文舟,张杰.最大似然思想和最大熵思想在交通状态分析中的一致性[J].公路交通科技,2001,18(4)：66-69
[23]任江涛,欧晓凌,张毅,胡东成.交通状态模式识别研究[J].公路交通科技,2003,20(2).63-67
[24]吴正.关于交通流动力学模型与交通状态指数研究[J].水动力学研究与进展,2003,18(4)：403-407
[25]郭伟,姚丹亚,付毅,胡坚明,刘宁.区域交通流特征提取与交通状态评估方法研究[J].公路交通科技,2005,22(7)：101-104,114
[26]新疆维吾尔自治区科学技术协会.熵与交叉科学[M].北京：气象出版社,1988.
[27]俞礼军,严海,严宝杰.最大熵原理在交通流统计分布模型中的应用[J].交通运输工程学报,2001,1(3)：91-94
[28]达庆东.交通分布与熵[J].公路交通科技,1999,16(1)：37-39
[29]靳文舟,张杰.最大似然思想和最大熵思想在交通状态分析中的一致性[J].公路交通科技,2001,18(4)：66-69
[30]刘勇.道路交通系统熵理论应用研究[D].长安大学硕士学位论文.2003：13-20
[31]张岐山.灰关联熵分析方法[J].系统工程理论与实践,1996：7-11
[32]张学文,马力.大气的热力学总熵[J].大气科学,1992,16(3)：339-344
[33]汪志诚,热力学·统计物理(第二版)[M].北京：高等教育出版社,1993.
[34]刘芸.基于流体力学及熵原理的集装箱港区交通流研究[D].中国海洋大学博士论文.2008：39-68
[35]岑威.基于熵及耗散结构理论的逆向物流系统研究[D].天津师范大学硕士学位论文.2009：11-18
[36]William H.cropper,Rudolf Clausius and the road to Entropy,American Journal of Physics[J].December 1986-Volume 54, Issue 12:1068-1074
[37]欧阳容百.热力学与统计物理[M].北京：科学出版社,2007.
[38]刘勇.基于热力学熵和混沌理论的城市道路交通系统的研究[D].长安大学博士学位论文.2009：64-67
[39]庄钟锐.基于熵理论的作战指挥系统组织结构(OCSOS)描述与评价[D].国防科技大学硕士学位论文.2009：12-15
[40]熊烈强.交通流理论及其在高速公路中的应用研究[D].武汉理工大学博士论文.2003：42-50
[41]Ross P. Traffic dynamics[J].Transp. Res.,1988,22B(6):421-435
[42]Balham O, Middleton A.Self-organization and a Dynamical Transition Traffic Flow Models[J].Physical Review,1992,46A(10):46-54
[43]薛郁.随机计及相对速度的交通流跟驰模型.物理学报,2003.52(11)：2750-2756
[44]Whklam et al. A Stochastic User Equilibrium Assignment Model for Congested Transit networks [J].Transportation Research Part B 33 1999:351-368
[45]Lighthill,M.H.and GB.Whilham.on kinematics waves:A Theory of Traffic Flow on Long Crowded Roads Proceeding[J].Royal Society(London),1955,A229,No.1178:317-345
[46]Payne H.J.Model of freeway traffic and control[J].In:Bekey GA(ed)Mathematical methods of Public System,1971,Vol.Ⅰ,No.1:51-61
[47]程瑶.交通流压缩与粘滞特性研究[D].吉林大学硕士论文.2007：2-10
[48]陈丽文.城市交通噪声分析及其系统研究[D].内蒙古工业大学硕士论文.2006：9-12
[49]Phillips W F. A new continuum traffic model obtained from kinetic theory[J]. IEEE Transp. Autom. Control,1978,AC-23:1032-1036
[50]Daganzo C F. The cell transmission model:A dynamic representation of highway traffic consistent with the hydrodynamic theory[J].Transp. Res.,1994,28B(4):269-287
[51]Payne H J. A critical review of a macroscopic freeway model[J].Proc. Research Directions in Computer Control of Urban Traffic Systems,1979:251-265
[52]李进平.交通流的流体力学模型与数值模拟[D].武汉理工大学硕士论文.2003：12-22
[53]马国旗.城市道路交通流特征参数研究[D].北京工业大学硕士论文.2004：9-12
[54]李江.交通工程学[M].北京：人民交通出版社,2002.