短时公交客流的弦不变量预测方法
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摘要
短时公交客流预测是智能公交系统动态调度的基础.文中根据短时公交客流数据特性,提出基于弦理论的短时公交客流预测方法,模拟弦结构建立弦不变量客流预测模型(SI-PFPM),并采用遗传算法优化SI-PFPM中各参数.提出基于动态时间弯曲距离的仿射传播(AP)聚类算法,对短时公交客流时间序列进行聚类分析.利用SI-PFPM预测聚类子集数据,并分析预测残差,验证SI-PFPM可以预测短时公交客流的假设成立.最后将SI-PFPM的预测性能与现有方法进行对比分析,验证SI-PFPM对短时公交客流预测的有效性.
The prediction of short-term bus passenger flow is the basis of the dynamic scheduling of the intelligent bus system. Therefore,according to the data characteristics of short-term passenger flow of bus,a short-term bus passenger flow prediction method based on string theory is proposed. A string invariants passenger flow prediction model( SI-PFPM) is constructed by simulating the string structure and the genetic algorithm is adopted to optimize the parameters of SI-PFPM. An AP clustering algorithm( DTW-AP) based on dynamic time warping( DTW) distance is proposed to perform clustering analysis on the passenger flow time series of short-time buses. SI-PFPM is employed to predict the short-term passenger flow data,and the predicted residual error is analyzed. The result shows that SI-PFPM is effective for the prediction of short-term bus passenger flow. Finally,the prediction performance of SIPFPM is compared with the existing methods,and the effectiveness of SI-PFPM in short-term bus passenger flow prediction is verified.
The prediction of short-term bus passenger flow is the basis of the dynamic scheduling of the intelligent bus system. Therefore,according to the data characteristics of short-term passenger flow of bus,a short-term bus passenger flow prediction method based on string theory is proposed. A string invariants passenger flow prediction model( SI-PFPM) is constructed by simulating the string structure and the genetic algorithm is adopted to optimize the parameters of SI-PFPM. An AP clustering algorithm( DTW-AP) based on dynamic time warping( DTW) distance is proposed to perform clustering analysis on the passenger flow time series of short-time buses. SI-PFPM is employed to predict the short-term passenger flow data,and the predicted residual error is analyzed. The result shows that SI-PFPM is effective for the prediction of short-term bus passenger flow. Finally,the prediction performance of SIPFPM is compared with the existing methods,and the effectiveness of SI-PFPM in short-term bus passenger flow prediction is verified.
引文
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[12] ZWIEBACH B. A First Course in String Theory. Cambridge,USA:Cambridge University Press,2009.
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[14]PINCAK R,BARTOS‘E. With String Model to Time Series Forecasting. Physica A(Statistical Mechanics and ITS Applications),2015,436:135-146.
[15]DEB K,PRATAP A,AGARWAL S,et al. A Fast and Elitist Multiobjective Genetic Algorithm:NSGA-II. IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
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[17]贺锋,秦晓丽,方勇纯.一种基于遗传算法的移动机器人自定位方法.模式识别与人工智能,2009,22(1):142-147.(HE F,QIN X L,FANG Y C. A Genetic Algorithm Based Autonomous Localization Strategy for Mobile Robots. Pattern Recognition and Artificial Intelligence,2009,22(1):142-147.)
[18]陈希孺.概率论与数理统计.合肥:中国科学技术大学出版社,2009.(CHEN X R. Probability Theory and Mathematics Statistics. Hefei,China:Press of University of Science and Technology of China,2009.)
[19]李航.统计学习方法.北京:清华大学出版社,2012.(LI H. Statistical Learning Method. Beijing,China:Tsinghua University Press,2012.)
[20]吴景龙,杨淑霞,刘承水.基于遗传算法优化参数的支持向量机短期负荷预测方法.中南大学学报(自然科学版),2009,40(1):180-184.(WU J L,YANG S X,LIU C S. Parameter Selection for Support Vector Machines Based on Genetic Algorithms to Short-Term Power Load Forecasting. Journal of Central South University(Science and Technology),2009,40(1):180-184.)
[21]SUN Y X,LENG B. GUAN W. A Novel Wavelet-SVM Short-Time Passenger Flow Prediction in Beijing Subway System. Neurocomputing,2015,166:109-121.
[22] XIAO Z,YE S J,ZHONG B,et al. BP Neural Network with Rough Set for Short Term Load Forecasting. Expert Systems with Applications,2009,36(1):273-279.
[2]滕靖,王何斐,杨新征,等.基于乘客主观感知的公交客流拥塞量化表征模型.中国公路学报,2018,31(6):299-307.(TENG J,WANG H F,YANG X Z,et al. Subjective Perception Based Passenger Congestion Quantification for Bus Operation. China Journal of Highway and Transport,2018,31(6):299-307.)
[3]高坤,涂辉招,李浩,等.出行者对不同交通方式行程时间可靠度和车内拥挤度的感知差异性.中国公路学报,2017,30(7):126-133.(GAO K,TU H Z,LI H,et al. Travelers'Perception Differences in Travel Time Reliability and Crowding of Mode-Choice in Multimodal Networks. China Journal of Highway and Transport,2017,30(7):126-133.)
[4]LUKE H,MARTIN K. The Distribution of Crowding Costs in Public Transport:New Evidence from Paris. Transportation Research Part A(Policy and Practice),2015,77:182-201.
[5]张春辉,宋瑞,孙杨.基于卡尔曼滤波的公交站点短时客流预测.交通运输系统工程与信息,2011,11(4):154-159.(ZHANG C H,SONG R,SUN Y. Kalman Filter-Based Short Term Passenger Flow Forecasting on Bus Stop. Journal of Transportation Systems Engineering and Information Technology,2011,11(4):154-159.)
[6]刘凯,李文权,赵锦焕.短时公交客流小波预测方法研究.交通运输工程与信息学报,2010,8(2):111-117.(LIU K,LI W Q,ZHAO J H. Study on Wavelet Forecast Method for Short-Term Passenger Flow. Journal of Transportation Engineering and Information,2010,8(2):111-117.)
[7]CHEN Q,LI W Q,ZHAO J H. The Use of LS-SVM for Short-Term Passenger Flow Prediction. Transport,2011,26(1):5-10.
[8]郭士永,李文权,白薇,等.基于最小二乘向量机的公交站点短时客流预测.武汉理工大学学报(交通科学与工程版),2013,37(3):603-607.(GUO S Y,LI W Q,BAI W,et al. Prediction of Short-Term Passenger Flow on a Bus Stop Based on LSSVM. Journal of Wuhan University of Technology(Transportation Science&Engineering),2013,37(3):603-607.)
[9] LIU L J,CHEN R C. A Novel Passenger Flow Prediction Model Using Deep Learning Methods. Transportation Research Part C(Emerging Technologies),2017,84:74-91.
[10]杨信丰,刘兰芬.基于AP聚类的支持向量机公交站点短时客流预测.武汉理工大学学报(交通科学与工程版),2016,40(1):36-40.(YANG X F,LIU L F. Short-Term Passenger Flow Forecasting on Bus Station Based on Affinity Propagation and Support Vector Machine. Journal of Wuhan University of Technology(Transportation Science&Engineering),2016,40(1):36-40.)
[11]PINCAK R. The String Prediction Models as Invariants of Time Series in the Forex Market. Physica A(Statistical Mechanics And ITS Applications),2013,392(24):6414-6426.
[12] ZWIEBACH B. A First Course in String Theory. Cambridge,USA:Cambridge University Press,2009.
[13]HORVATH D,PINCAK R. From the Currency Rate Quotations onto Strings and Brane World Scenarios. Physica A(Statistical Mechanics and ITS Applications),2012,391(21):5172-5188.
[14]PINCAK R,BARTOS‘E. With String Model to Time Series Forecasting. Physica A(Statistical Mechanics and ITS Applications),2015,436:135-146.
[15]DEB K,PRATAP A,AGARWAL S,et al. A Fast and Elitist Multiobjective Genetic Algorithm:NSGA-II. IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[16]GABRIEL L Q,ENRIQUE A. Parallel Genetic Algorithms:Theory and Real World Applications. Berlin,Germany:Springer,2011.
[17]贺锋,秦晓丽,方勇纯.一种基于遗传算法的移动机器人自定位方法.模式识别与人工智能,2009,22(1):142-147.(HE F,QIN X L,FANG Y C. A Genetic Algorithm Based Autonomous Localization Strategy for Mobile Robots. Pattern Recognition and Artificial Intelligence,2009,22(1):142-147.)
[18]陈希孺.概率论与数理统计.合肥:中国科学技术大学出版社,2009.(CHEN X R. Probability Theory and Mathematics Statistics. Hefei,China:Press of University of Science and Technology of China,2009.)
[19]李航.统计学习方法.北京:清华大学出版社,2012.(LI H. Statistical Learning Method. Beijing,China:Tsinghua University Press,2012.)
[20]吴景龙,杨淑霞,刘承水.基于遗传算法优化参数的支持向量机短期负荷预测方法.中南大学学报(自然科学版),2009,40(1):180-184.(WU J L,YANG S X,LIU C S. Parameter Selection for Support Vector Machines Based on Genetic Algorithms to Short-Term Power Load Forecasting. Journal of Central South University(Science and Technology),2009,40(1):180-184.)
[21]SUN Y X,LENG B. GUAN W. A Novel Wavelet-SVM Short-Time Passenger Flow Prediction in Beijing Subway System. Neurocomputing,2015,166:109-121.
[22] XIAO Z,YE S J,ZHONG B,et al. BP Neural Network with Rough Set for Short Term Load Forecasting. Expert Systems with Applications,2009,36(1):273-279.