基于ACP方法的平行手机信令数据分析系统
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摘要
随着交通拥堵和公共安全问题的日趋严重,传统方案在道路监测和区域监测方面不仅成本高,准确性和可靠性也无法保证,因此无法给用户提供一整套综合全面的出行路线规划及旅游目的地选择等方面的相关指导.本文提出基于ACP方法的平行手机信令数据分析系统,将解决上述问题.本文主要基于ACP方法,包括人工社会、计算实验和平行执行,构建基于手机信令的人工监控场景和实际监控场景.实际监控场景和人工监控场景平行执行,人工监控场景用来模拟和实验复杂的实际监控场景,通过大量计算实验,进行各种模型的训练与评估,通过平行执行不断地更新和优化,实时指导实际监控场景;同时实际监控场景将结果反馈给人工监控场景,对人工监控场景模型进行修正.通过实际监控场景和人工监控场景之间的不断优化,可有效提高手机信令系统的实时性、准确性和可靠性,并最终满足不断增长的实时用户需求,保证用户出行的舒适性及安全性.
The issue of traffic congestion and public security is becoming more and more important. Traditional solutions are not only high cost in terms of road monitoring and regional monitoring, but also the accuracy and reliability can not be guaranteed. Thus, the traditional solutions can not provide users comprehensive guidance about the travel route planning and travel destination selection and other related guidance. This paper proposes a mobile phone signaling data analysis system based on the ACP approach to solve the aforementioned problems. The ACP approach includes artificial society, computational experiments and parallel execution which build artificial monitoring scene and actual monitoring scene based on mobile phone signaling. The actual monitoring scene and artificial monitoring scene are executed in parallel. Artificial monitoring scene is used to simulate and test the complex actual monitoring scene. Through a large number of computational experiments, various models are trained and evaluated; Artificial monitoring scene constantly updates, optimizes and guides the actual monitoring scene through parallel execution; The actual monitoring scene will feedback the results to the artificial monitoring scene, thus artificial monitoring scene model is continuously amended.The continuous optimization between the actual monitoring scene and artificial monitoring scene can effectively improves the real-time efficiency, accuracy and reliability of the mobile phone signaling system. The proposed system would meet the requirements of ever-increasing real-time, and ensure the comfort and safety for the travel of the users.
The issue of traffic congestion and public security is becoming more and more important. Traditional solutions are not only high cost in terms of road monitoring and regional monitoring, but also the accuracy and reliability can not be guaranteed. Thus, the traditional solutions can not provide users comprehensive guidance about the travel route planning and travel destination selection and other related guidance. This paper proposes a mobile phone signaling data analysis system based on the ACP approach to solve the aforementioned problems. The ACP approach includes artificial society, computational experiments and parallel execution which build artificial monitoring scene and actual monitoring scene based on mobile phone signaling. The actual monitoring scene and artificial monitoring scene are executed in parallel. Artificial monitoring scene is used to simulate and test the complex actual monitoring scene. Through a large number of computational experiments, various models are trained and evaluated; Artificial monitoring scene constantly updates, optimizes and guides the actual monitoring scene through parallel execution; The actual monitoring scene will feedback the results to the artificial monitoring scene, thus artificial monitoring scene model is continuously amended.The continuous optimization between the actual monitoring scene and artificial monitoring scene can effectively improves the real-time efficiency, accuracy and reliability of the mobile phone signaling system. The proposed system would meet the requirements of ever-increasing real-time, and ensure the comfort and safety for the travel of the users.
引文
1 Yang Zhao-Sheng. Basic Traffic Information Fusion Technology and Its Application. Beijing:China Railway Publishing House, 2005.(杨兆升.基础交通信息融合技术及其应用.北京:中国铁道出版社,2005.)
2 Xia M, Chen Y H, Huang H C, Wang X Y, Chen Y F.Environment-adaptive road traffic measurement with single wireless geomagnetic sensor node. In:Proceedings of the International Conference on Wireless Communication and Sensor Network(WCSN 2015). Changsha, China:World Scientific, 2016. 720-727
3 He Zhao-Cheng, Lu Rui-Qi, Nie Pei-Lin. Estimation on freeway segment average speed based on probe vehicle data.Journal of Highway and Transportation Research and Development, 2011, 28(6):128-135(何兆成,卢瑞琪,聂佩林.基于浮动车定位数据的高速公路区间平均速度估计.公路交通科技, 2011, 28(6):128-135)
4 Liu Qiao. Studies on the condition of urban road networks based on floating car technology[Master dissertation],Wuhan University of Technology, China, 2013.(刘俏.基于浮动车技术的城市道路网状况研究[硕士学位论文],武汉理工大学,中国, 2013.)
5 Cao Yi-Hua. The large-scale crowd analysis in video surveillance[Master dissertation], Shanghai Jiao Tong University,China, 2013.(曹艺华.面向视频监控的大尺度人群行为分析[硕士学位论文],上海交通大学,中国, 2013.)
6 Xiao Tan, Yang Xu, Li Huang-Huang. Railway passenger station crowd density estimation algorithm based video surveillance. Railway Signalling&Communication, 2010,46(8):80-82(肖坦,杨栩,李黄煌.基于视频监控的铁路客运站人群密度分析算法.铁道通信信号, 2010, 46(8):80-82)
7 Casas O, Lopez M, Qu?lez M, Martinez-Farre X, Hornero G, Rovira C, Pinilla M R, RamosP M, Borges B, Marques H, Gir?ao S. Wireless sensor network for smart composting monitoring and control. Measurement, 2014, 47:483-495
8 Zheng Jian-Hu, Wang Ming-Hua. Comparative analysis of collection technologies for dynamic traffic information.Transport Standardization, 2009,(4):42-47(郑建湖,王明华.动态交通信息采集技术比较分析.交通标准化,2009,(4):42-47)
9 Li Zu-Fen, Yu Lei, Gao Yong, Wu Yi-Zheng, Gong Da-Peng,Song Guo-Hua. Extraction method of temporal and spatial characteristics of residents trips based on cellular signaling data. Transportation Standardization, 2016, 2(1):51-57(李祖芬,于雷,高永,吴亦政,龚大鹏,宋国华.基于手机信令定位数据的居民出行时空分布特征提取方法.交通运输研究, 2016, 2(1):51-57)
10 Tang Xiao-Yong, Zhou Tao, Lu Bai-Chuan. Analysis of wide range population flow analysis based on mobile phone signaling. Journal of Chongqing Jiaotong University(Natural Science), 2017, 36(1):82-87, 109(唐小勇,周涛,陆百川.基于手机信令的大范围人流移动分析.重庆交通大学学报(自然科学版), 2017, 36(1):82-87, 109)
11 Wu Song, Luo Jiang-Tao, Zhou Yun-Feng, Lin Ju-Ting, Shu Zhong-Ling. Method of real-time traffic statistics using mobile network signaling. Application Research of Computers,2014, 31(3):776-779(吴松,雒江涛,周云峰,林举厅,舒忠玲.基于移动网络信令数据的实时人流量统计方法.计算机应用研究, 2014, 31(3):776-779)
12 Hellinga B, Fu L P, Takada H. Obtaining traveller information via mobile phone location referencing-challenges and opportunities. In:Proceedings of the Annual Conference of the Transportation Association of Canada. St. John s,Canada:National Academy of Sciences, 2003. 21-24
13 White J, Quick J, Philippou P. The use of mobile phone location data for traffic information. In:Proceedings of the12 th IEE International Conference on Road Transport Information and Control. London, UK:IEEE, 2004. 321-325
14 Rose G. Mobile phones as traffic probes:practices, prospects and issues. Transport Reviews, 2006, 26(3):275-291
15 Qiu Z J, Cheng P. State of the art and practice:cellular probe technology applied in advanced traveler information system. In:Transportation Research Board 86th Annual Meeting(TRB2007). Washington DC, USA:Transportation Research Board. 2007.
16 Caceres N, Wideberg J P, Benitez F G. Review of traffic data estimations extracted from cellular networks. IET Intelligent Transport Systems, 2008, 2(3):179-192
17 Valerio D. Road Traffic Information from Cellular Network Signaling, Technical Report FTW-TR-2009-003, Telecommunications Research Center Vienna, Austria, 2009.
18 Guo Xiao-Ni. Traffic flow monitoring and travel information service system based on mobile phone signaling. Telecom World, 2017,(14):157(郭晓妮.基于手机信令的交通流监测与出行信息服务系统.通讯世界, 2017,(14):157)
19 Chen Huan. Survey of pedestrian travel characteristics based on mobile phone signaling data. Traffic&Transportation, 2017,(S1):92-94, 98(陈欢.基于手机信令数据的人员出行特征跟踪调查.交通与运输,2017,(S1):92-94, 98)
20 Wang F Y. Parallel control and management for intelligent transportation systems:concepts, architectures, and applications. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(3):630-638
21 Wang Fei-Yue. Parallel system methods for management and control of complex systems. Control and Decision, 2004,19(5):485-489, 514(王飞跃.平行系统方法与复杂系统的管理和控制.控制与决策,2004, 19(5):485-489, 514)
22 Wang Fei-Yue. Artificial societies, computational experiments, and parallel systems:a discussion on computational theory of complex social-economic systems. Complex Systems and Complexity Science, 2004, 1(4):25-35(王飞跃.人工社会、计算实验、平行系统—关于复杂社会经济系统计算研究的讨论.复杂系统与复杂性科学, 2004, 1(4):25-35)
23 Wang Fei-Yue. Computational experiments for behavior analysis and decision evaluation of complex systems. Journal of System Simulation, 2004, 16(5):893-897(王飞跃.计算实验方法与复杂系统行为分析和决策评估.系统仿真学报, 2004, 16(5):893-897)
24 Wang Fei-Yue. Computational theory and method on complex system. China Basic Science, 2004, 6(5):3-10(王飞跃.关于复杂系统研究的计算理论与方法.中国基础科学,2004, 6(5):3-10)
25 Zhang N, Wang F Y, Zhu F H, Zhao D B, Tang S M. DynaCAS:computational experiments and decision support for ITS. IEEE Intelligent Systems, 2008, 23(6):19-23
26 Wang Fei-Yue, Liu De-Rong, Xiong Gong, Cheng ChangJian, Zhao Dong-Bin. Parallel control theory of complex systems and applications. Complex Systems and Complexity Science, 2012, 9(3):1-12(王飞跃,刘德荣,熊刚,程长建,赵冬斌.复杂系统的平行控制理论及应用.复杂系统与复杂性科学, 2012, 9(3):1-12)
27 Wang F Y, Wang X, Li L X, Li L. Steps toward parallel intelligence. IEEE/CAA Journal of Automatica Sinica, 2016,3(4):345-348
28 Wang F Y, Yang L Q, Cheng X, Han S S, Yang J. Network softwarization and parallel networks:beyond softwaredefined networks. IEEE Network, 2016, 30(4):60-65
29 Wang Fei-Yue, Yang Liu-Qing, Hu Xiao-Ya, Cheng Xiang,Han Shuang-Shuang, Yang Jian. Parallel networks and network softwarization:a novel network architecture. Scientia Sinica(Informationis), 2017, 47(7):811-831(王飞跃,杨柳青,胡晓娅,程翔,韩双双,杨坚.平行网络与网络软件化:一种新颖的网络架构.中国科学:信息科学, 2017, 47(7):811-831)
30 Li Li, Lin Yi-Lun, Cao Dong-Pu, Zheng Nan-Ning, Wang Fei-Yue. Parallel learning—a new framework for machine learning. Acta Automatica Sinica, 2017, 43(1):1-8(李力,林懿伦,曹东璞,郑南宁,王飞跃.平行学习—机器学习的一个新型理论框架.自动化学报, 2017, 43(1):1-8)
31 Lv Y S, Duan Y J, Kang W W, Li Z X, Wang F Y. Traffic flow prediction with big data:a deep learning approach.IEEE Transactions on Intelligent Transportation Systems,2015, 16(2):865-873
32 Wen J, Zhao J L, Luo S W, Han Z. The improvements of BP neural network learning algorithm. In:Proceedings of the 5 th International Conference on Signal Processing. Beijing,China:IEEE, 2000, 3:1647-1649
2 Xia M, Chen Y H, Huang H C, Wang X Y, Chen Y F.Environment-adaptive road traffic measurement with single wireless geomagnetic sensor node. In:Proceedings of the International Conference on Wireless Communication and Sensor Network(WCSN 2015). Changsha, China:World Scientific, 2016. 720-727
3 He Zhao-Cheng, Lu Rui-Qi, Nie Pei-Lin. Estimation on freeway segment average speed based on probe vehicle data.Journal of Highway and Transportation Research and Development, 2011, 28(6):128-135(何兆成,卢瑞琪,聂佩林.基于浮动车定位数据的高速公路区间平均速度估计.公路交通科技, 2011, 28(6):128-135)
4 Liu Qiao. Studies on the condition of urban road networks based on floating car technology[Master dissertation],Wuhan University of Technology, China, 2013.(刘俏.基于浮动车技术的城市道路网状况研究[硕士学位论文],武汉理工大学,中国, 2013.)
5 Cao Yi-Hua. The large-scale crowd analysis in video surveillance[Master dissertation], Shanghai Jiao Tong University,China, 2013.(曹艺华.面向视频监控的大尺度人群行为分析[硕士学位论文],上海交通大学,中国, 2013.)
6 Xiao Tan, Yang Xu, Li Huang-Huang. Railway passenger station crowd density estimation algorithm based video surveillance. Railway Signalling&Communication, 2010,46(8):80-82(肖坦,杨栩,李黄煌.基于视频监控的铁路客运站人群密度分析算法.铁道通信信号, 2010, 46(8):80-82)
7 Casas O, Lopez M, Qu?lez M, Martinez-Farre X, Hornero G, Rovira C, Pinilla M R, RamosP M, Borges B, Marques H, Gir?ao S. Wireless sensor network for smart composting monitoring and control. Measurement, 2014, 47:483-495
8 Zheng Jian-Hu, Wang Ming-Hua. Comparative analysis of collection technologies for dynamic traffic information.Transport Standardization, 2009,(4):42-47(郑建湖,王明华.动态交通信息采集技术比较分析.交通标准化,2009,(4):42-47)
9 Li Zu-Fen, Yu Lei, Gao Yong, Wu Yi-Zheng, Gong Da-Peng,Song Guo-Hua. Extraction method of temporal and spatial characteristics of residents trips based on cellular signaling data. Transportation Standardization, 2016, 2(1):51-57(李祖芬,于雷,高永,吴亦政,龚大鹏,宋国华.基于手机信令定位数据的居民出行时空分布特征提取方法.交通运输研究, 2016, 2(1):51-57)
10 Tang Xiao-Yong, Zhou Tao, Lu Bai-Chuan. Analysis of wide range population flow analysis based on mobile phone signaling. Journal of Chongqing Jiaotong University(Natural Science), 2017, 36(1):82-87, 109(唐小勇,周涛,陆百川.基于手机信令的大范围人流移动分析.重庆交通大学学报(自然科学版), 2017, 36(1):82-87, 109)
11 Wu Song, Luo Jiang-Tao, Zhou Yun-Feng, Lin Ju-Ting, Shu Zhong-Ling. Method of real-time traffic statistics using mobile network signaling. Application Research of Computers,2014, 31(3):776-779(吴松,雒江涛,周云峰,林举厅,舒忠玲.基于移动网络信令数据的实时人流量统计方法.计算机应用研究, 2014, 31(3):776-779)
12 Hellinga B, Fu L P, Takada H. Obtaining traveller information via mobile phone location referencing-challenges and opportunities. In:Proceedings of the Annual Conference of the Transportation Association of Canada. St. John s,Canada:National Academy of Sciences, 2003. 21-24
13 White J, Quick J, Philippou P. The use of mobile phone location data for traffic information. In:Proceedings of the12 th IEE International Conference on Road Transport Information and Control. London, UK:IEEE, 2004. 321-325
14 Rose G. Mobile phones as traffic probes:practices, prospects and issues. Transport Reviews, 2006, 26(3):275-291
15 Qiu Z J, Cheng P. State of the art and practice:cellular probe technology applied in advanced traveler information system. In:Transportation Research Board 86th Annual Meeting(TRB2007). Washington DC, USA:Transportation Research Board. 2007.
16 Caceres N, Wideberg J P, Benitez F G. Review of traffic data estimations extracted from cellular networks. IET Intelligent Transport Systems, 2008, 2(3):179-192
17 Valerio D. Road Traffic Information from Cellular Network Signaling, Technical Report FTW-TR-2009-003, Telecommunications Research Center Vienna, Austria, 2009.
18 Guo Xiao-Ni. Traffic flow monitoring and travel information service system based on mobile phone signaling. Telecom World, 2017,(14):157(郭晓妮.基于手机信令的交通流监测与出行信息服务系统.通讯世界, 2017,(14):157)
19 Chen Huan. Survey of pedestrian travel characteristics based on mobile phone signaling data. Traffic&Transportation, 2017,(S1):92-94, 98(陈欢.基于手机信令数据的人员出行特征跟踪调查.交通与运输,2017,(S1):92-94, 98)
20 Wang F Y. Parallel control and management for intelligent transportation systems:concepts, architectures, and applications. IEEE Transactions on Intelligent Transportation Systems, 2010, 11(3):630-638
21 Wang Fei-Yue. Parallel system methods for management and control of complex systems. Control and Decision, 2004,19(5):485-489, 514(王飞跃.平行系统方法与复杂系统的管理和控制.控制与决策,2004, 19(5):485-489, 514)
22 Wang Fei-Yue. Artificial societies, computational experiments, and parallel systems:a discussion on computational theory of complex social-economic systems. Complex Systems and Complexity Science, 2004, 1(4):25-35(王飞跃.人工社会、计算实验、平行系统—关于复杂社会经济系统计算研究的讨论.复杂系统与复杂性科学, 2004, 1(4):25-35)
23 Wang Fei-Yue. Computational experiments for behavior analysis and decision evaluation of complex systems. Journal of System Simulation, 2004, 16(5):893-897(王飞跃.计算实验方法与复杂系统行为分析和决策评估.系统仿真学报, 2004, 16(5):893-897)
24 Wang Fei-Yue. Computational theory and method on complex system. China Basic Science, 2004, 6(5):3-10(王飞跃.关于复杂系统研究的计算理论与方法.中国基础科学,2004, 6(5):3-10)
25 Zhang N, Wang F Y, Zhu F H, Zhao D B, Tang S M. DynaCAS:computational experiments and decision support for ITS. IEEE Intelligent Systems, 2008, 23(6):19-23
26 Wang Fei-Yue, Liu De-Rong, Xiong Gong, Cheng ChangJian, Zhao Dong-Bin. Parallel control theory of complex systems and applications. Complex Systems and Complexity Science, 2012, 9(3):1-12(王飞跃,刘德荣,熊刚,程长建,赵冬斌.复杂系统的平行控制理论及应用.复杂系统与复杂性科学, 2012, 9(3):1-12)
27 Wang F Y, Wang X, Li L X, Li L. Steps toward parallel intelligence. IEEE/CAA Journal of Automatica Sinica, 2016,3(4):345-348
28 Wang F Y, Yang L Q, Cheng X, Han S S, Yang J. Network softwarization and parallel networks:beyond softwaredefined networks. IEEE Network, 2016, 30(4):60-65
29 Wang Fei-Yue, Yang Liu-Qing, Hu Xiao-Ya, Cheng Xiang,Han Shuang-Shuang, Yang Jian. Parallel networks and network softwarization:a novel network architecture. Scientia Sinica(Informationis), 2017, 47(7):811-831(王飞跃,杨柳青,胡晓娅,程翔,韩双双,杨坚.平行网络与网络软件化:一种新颖的网络架构.中国科学:信息科学, 2017, 47(7):811-831)
30 Li Li, Lin Yi-Lun, Cao Dong-Pu, Zheng Nan-Ning, Wang Fei-Yue. Parallel learning—a new framework for machine learning. Acta Automatica Sinica, 2017, 43(1):1-8(李力,林懿伦,曹东璞,郑南宁,王飞跃.平行学习—机器学习的一个新型理论框架.自动化学报, 2017, 43(1):1-8)
31 Lv Y S, Duan Y J, Kang W W, Li Z X, Wang F Y. Traffic flow prediction with big data:a deep learning approach.IEEE Transactions on Intelligent Transportation Systems,2015, 16(2):865-873
32 Wen J, Zhao J L, Luo S W, Han Z. The improvements of BP neural network learning algorithm. In:Proceedings of the 5 th International Conference on Signal Processing. Beijing,China:IEEE, 2000, 3:1647-1649
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