交通拥挤的度量方法与基于浮动车的交通拥挤检测
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摘要
随着社会经济的迅速发展，许多城市道路的通行能力已无法满足日益增长的交通需求，交通拥挤堵塞现象日趋严重。智能交通系统的关键功能，就是要能够管理和控制交通流，避免交通堵塞现象的发生；一旦发生交通拥挤和堵塞，能够及时快速地提供有效的解决方案，疏导交通流。拥挤度的检测与评估，是正确审视交通拥挤的前提性工作，具有着重要的现实意义。
本文从讨论交通拥挤的成因入手，结合国内外交通拥挤的评估方法，提出了交通拥挤度的概念，建立了能够连续描述交通拥挤程度，并可以体现人们对交通拥挤感觉的指标。通过分析交通流基本参数与交通拥挤现象之间的关系，本文运用模糊数学的方法对交通参数（交通流量和平均速度）以及拥挤度进行描述，建立了交通拥挤度的模糊推理系统。
为验证交通拥挤度和模糊推理系统的可行性，本文进行了软件仿真实验。利用软件获取的仿真数据，以及人们对仿真实验所呈现的交通拥挤程度的评价数据，文章对模糊推理系统参数进行了辨识。辨识采用了两种方法，即基于最小二乘法的模糊辨识方法和基于自适应神经网络的训练。后者较好地完成了训练任务。通过分析模糊推理系统的推理曲面，验证了模糊推理系统的可行性，它能够较好地支持交通拥挤度这个概念。
然后，本文提出利用浮动车检测系统作为交通参数获取手段的方法，阐述了浮动车检测系统的框架及检测流程，着重介绍了利用GPS数据计算交通参数的方法。针对交通拥挤度，文章提出了利用浮动车检测系统判别常发性和偶发性拥挤类型的方法，并根据上述推理系统框架，给出在非偶发性拥挤下的拥挤度计算步骤。
Traffic congestion becomes more and more serious with the rapid development in modern society. Traffic management and control are aimed to avoid traffic jam and give effective solution of releasing transportation pressure, which is the key function of Intelligent Transportation System (ITS). Therefore, it is important to detect and estimate the traffic congestion. With the evaluation result of traffic congestion in advanced traffic information system, traveler may find the best way against traffic jam, and traffic managers can be benefited by such historical data analysis in layout of city road nets.
In the thesis, with the analyzing of traffic congestion causes, many methods to evaluate traffic jam are reviewed, from which, Level of Congestion (LOC) is defined as an index to describe a continuous traffic processing from free flow to traffic jam. LOC should also be of coherence with human perception on traffic congestion. According to the relationship between characteristics of the traffic flow and traffic congestion, fuzzy logic is adopted to partition the basic parameters (mean velocity and flow volume) into several fuzzy subsets, then fuzzy inference system is established to generate LOC.
To test the feasibility of LOC and fuzzy inference system, a simulation tool is used to collect the data of traffic flow and visually evaluate the traffic congestion by people. Two methodologies are used to obtain the system model. One is fuzzy identification based on the least mean square method and the other is based on adaptive neuro-fuzzy inference system. As a result from fuzzy inference surface analysis, LOC can be supported by the fuzzy inference system and can recover human's general perception on judging congestion.
In the last section, probe vehicle system is discussed as a means of data collection. The framework and detection process of probe vehicle system are depicted. Particularly, GPS data processing and traffic parameter evaluation
based on probe vehicle reports are developed. A method to judge the type of traffic congestion is studied, and according to the previous result, the step to calculate LOC in the type of non-recurrence congestion is finally presented.
本文从讨论交通拥挤的成因入手，结合国内外交通拥挤的评估方法，提出了交通拥挤度的概念，建立了能够连续描述交通拥挤程度，并可以体现人们对交通拥挤感觉的指标。通过分析交通流基本参数与交通拥挤现象之间的关系，本文运用模糊数学的方法对交通参数（交通流量和平均速度）以及拥挤度进行描述，建立了交通拥挤度的模糊推理系统。
为验证交通拥挤度和模糊推理系统的可行性，本文进行了软件仿真实验。利用软件获取的仿真数据，以及人们对仿真实验所呈现的交通拥挤程度的评价数据，文章对模糊推理系统参数进行了辨识。辨识采用了两种方法，即基于最小二乘法的模糊辨识方法和基于自适应神经网络的训练。后者较好地完成了训练任务。通过分析模糊推理系统的推理曲面，验证了模糊推理系统的可行性，它能够较好地支持交通拥挤度这个概念。
然后，本文提出利用浮动车检测系统作为交通参数获取手段的方法，阐述了浮动车检测系统的框架及检测流程，着重介绍了利用GPS数据计算交通参数的方法。针对交通拥挤度，文章提出了利用浮动车检测系统判别常发性和偶发性拥挤类型的方法，并根据上述推理系统框架，给出在非偶发性拥挤下的拥挤度计算步骤。
Traffic congestion becomes more and more serious with the rapid development in modern society. Traffic management and control are aimed to avoid traffic jam and give effective solution of releasing transportation pressure, which is the key function of Intelligent Transportation System (ITS). Therefore, it is important to detect and estimate the traffic congestion. With the evaluation result of traffic congestion in advanced traffic information system, traveler may find the best way against traffic jam, and traffic managers can be benefited by such historical data analysis in layout of city road nets.
In the thesis, with the analyzing of traffic congestion causes, many methods to evaluate traffic jam are reviewed, from which, Level of Congestion (LOC) is defined as an index to describe a continuous traffic processing from free flow to traffic jam. LOC should also be of coherence with human perception on traffic congestion. According to the relationship between characteristics of the traffic flow and traffic congestion, fuzzy logic is adopted to partition the basic parameters (mean velocity and flow volume) into several fuzzy subsets, then fuzzy inference system is established to generate LOC.
To test the feasibility of LOC and fuzzy inference system, a simulation tool is used to collect the data of traffic flow and visually evaluate the traffic congestion by people. Two methodologies are used to obtain the system model. One is fuzzy identification based on the least mean square method and the other is based on adaptive neuro-fuzzy inference system. As a result from fuzzy inference surface analysis, LOC can be supported by the fuzzy inference system and can recover human's general perception on judging congestion.
In the last section, probe vehicle system is discussed as a means of data collection. The framework and detection process of probe vehicle system are depicted. Particularly, GPS data processing and traffic parameter evaluation
based on probe vehicle reports are developed. A method to judge the type of traffic congestion is studied, and according to the previous result, the step to calculate LOC in the type of non-recurrence congestion is finally presented.
引文
[1] 任江涛，张毅，许俊华. 美欧日ITS体系结构比较分析. 公路交通科技， 2001，18(2): 61~65
[2] 陆化普. 智能运输系统. 北京：人民交通出版社，2002
[3] National ITS Architecture, version 3.0
[4] 张智文，魏凤，侯福深. 中国智能运输系统(ITS)的发展. 交通运输系统工程与信息，2001(1): 18~22
[5] 史其信，陆化普. 中国ITS发展战略构想. 公路交通科技，1998，15(3): 13~16
[6] 茅于轼. 北京堵车一年损失60亿,从今往后谁用道路谁付钱？http://www.qianlong.com/3317/2003/09/25/100@1618675.htm
[7] 邓卫，李峻利. 高速公路常发性与偶发性交通拥挤的判别. 东南大学学报，1994，24(2): 60~64
[8] 吕立生. 城市交通阻塞分析. 上海大学学报（自然科学版），1998，4（3）: 317~322
[9] E. D. Arnold, Jr. Congestion on Virginia's Urban Highways，http://ntl.bts.gov/DOCS/arnold.html
[10] David Schrank, Tim Lomax. The 2002 urban mobility report，Texas Transportation Institute，June 2002，http://mobility.tamu.edu
[11] B. S. Kerner. Tracing and forecasting of congested patterns for highway traffic management，2001 IEEE ITSC Proceedings, August 25-29, 2001
[12] David J. Hensing. Some Perspectives on Congestion: The Emerging Mission of Modern Transportation Departments， Semisesquicentennial Transportation Conference，1996，http://www.ctre.iastate.edu/pubs/semisesq/session3/hensing/
[13] 杨兆升. 城市交通流－诱导系统理论与模型. 北京：人民交通出版社
[14] 闻新，周露等. 模糊逻辑工具箱的分析与应用. 北京：科学出版社，2001
[15] 李洪娟. 秦皇岛市海港区拥挤路口交通状况的优化研究，[硕士学位论文]，辽宁，辽宁工程技术大学，2000
[16] 刘小明，王书彬，任福田.公路建设项目实施序列评价指标体系及度量方法研究，北京工业大学
[17] Highway Capacity Manual，USA，1994
[18] PARAMICS, http://www.paramics-online.com
[19] Tong, R.M. Synthesis of fuzzy model for industrial processes. International J. Gen. System，1978，4:143-162.T
[20] Takagi. T.，Sugeno M.，Fuzzy Identification of system and its application to modeling and control. IEEE Trans. SMC，15:116-132
[21] Stephen J.Bahler. Field Test of non-intrusive traffic Detection technologies. Proceedings of the 5th World Congress on Intelligent Transportation Systems. Seoul, Korea, 1998
[22] Travel Time Data Collection Handbook,1998
[23] 新北京，新奥运. http://www.chinautc.com/hot/olympic/015.asp
[24] 张剑飞. 为奥运会提供快捷、高效、安全、舒适的交通支持. 交通运输系统工程与信息，2001，1(3): 186~188
[25] 杨宏业. GPS车载系统的设计与实现. [硕士学位论文]，北京：清华大学自动化系，2002，35~37
[26] Ramya Sarma，A GPS-GIS (Dynamic Segmentation) Approach for Travel Time Studies，Louisiana State University，http://www.lsu.edu/
[27] GPS车辆监控调度系统，http://www.hunangps.com/
[28] ISSUE PAPER: Congestion management，1998，http://www.ewgateway.org/
[2] 陆化普. 智能运输系统. 北京：人民交通出版社，2002
[3] National ITS Architecture, version 3.0
[4] 张智文，魏凤，侯福深. 中国智能运输系统(ITS)的发展. 交通运输系统工程与信息，2001(1): 18~22
[5] 史其信，陆化普. 中国ITS发展战略构想. 公路交通科技，1998，15(3): 13~16
[6] 茅于轼. 北京堵车一年损失60亿,从今往后谁用道路谁付钱？http://www.qianlong.com/3317/2003/09/25/100@1618675.htm
[7] 邓卫，李峻利. 高速公路常发性与偶发性交通拥挤的判别. 东南大学学报，1994，24(2): 60~64
[8] 吕立生. 城市交通阻塞分析. 上海大学学报（自然科学版），1998，4（3）: 317~322
[9] E. D. Arnold, Jr. Congestion on Virginia's Urban Highways，http://ntl.bts.gov/DOCS/arnold.html
[10] David Schrank, Tim Lomax. The 2002 urban mobility report，Texas Transportation Institute，June 2002，http://mobility.tamu.edu
[11] B. S. Kerner. Tracing and forecasting of congested patterns for highway traffic management，2001 IEEE ITSC Proceedings, August 25-29, 2001
[12] David J. Hensing. Some Perspectives on Congestion: The Emerging Mission of Modern Transportation Departments， Semisesquicentennial Transportation Conference，1996，http://www.ctre.iastate.edu/pubs/semisesq/session3/hensing/
[13] 杨兆升. 城市交通流－诱导系统理论与模型. 北京：人民交通出版社
[14] 闻新，周露等. 模糊逻辑工具箱的分析与应用. 北京：科学出版社，2001
[15] 李洪娟. 秦皇岛市海港区拥挤路口交通状况的优化研究，[硕士学位论文]，辽宁，辽宁工程技术大学，2000
[16] 刘小明，王书彬，任福田.公路建设项目实施序列评价指标体系及度量方法研究，北京工业大学
[17] Highway Capacity Manual，USA，1994
[18] PARAMICS, http://www.paramics-online.com
[19] Tong, R.M. Synthesis of fuzzy model for industrial processes. International J. Gen. System，1978，4:143-162.T
[20] Takagi. T.，Sugeno M.，Fuzzy Identification of system and its application to modeling and control. IEEE Trans. SMC，15:116-132
[21] Stephen J.Bahler. Field Test of non-intrusive traffic Detection technologies. Proceedings of the 5th World Congress on Intelligent Transportation Systems. Seoul, Korea, 1998
[22] Travel Time Data Collection Handbook,1998
[23] 新北京，新奥运. http://www.chinautc.com/hot/olympic/015.asp
[24] 张剑飞. 为奥运会提供快捷、高效、安全、舒适的交通支持. 交通运输系统工程与信息，2001，1(3): 186~188
[25] 杨宏业. GPS车载系统的设计与实现. [硕士学位论文]，北京：清华大学自动化系，2002，35~37
[26] Ramya Sarma，A GPS-GIS (Dynamic Segmentation) Approach for Travel Time Studies，Louisiana State University，http://www.lsu.edu/
[27] GPS车辆监控调度系统，http://www.hunangps.com/
[28] ISSUE PAPER: Congestion management，1998，http://www.ewgateway.org/