基于神经网络与马尔可夫链的换道次数预测方法
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摘要
为在先验数据有限情况下较精确地预测道路换道次数,提出基于神经网络与马尔可夫链的组合预测模型。采集路段区间平均车速和车流密度,采用BP神经网络训练初步拟合模型;运用马尔可夫链方法,进一步给出换道次数在表示高估、正常、低估的3组区间内的分布及概率,改善BP神经网络误差。运用组合预测模型对西安市某道路的换道次数进行了预测分析,结果表明,实际换道次数均在模型给出的较大概率的预测区间内,表明模型能够根据路段区间平均车速和车流密度较好地预测换道次数。
In order to predict more accurately the frequencies of lane changes with the limited data, a combined forecasting model based on neural network and Markov chain was proposed. The average speed and density of road sections were collected, and the BP neural network model was used to train the preliminary fitting model.By using Markov chain method, the distribution and probability of lane changing frequencies in three groups of intervals representing overestimation, normal and underestimation were given to decrease BP neural network error. The combination forecasting model was adopted to predict and analyze the frequency of lane-changing of a road in Xi'an. The results show that the actual frequencies of lane changing are all within the prediction range of the maximum probability given by the model, which indicates that the model can predict the frequency of lane changing according to the average speed and traffic density of the section.
In order to predict more accurately the frequencies of lane changes with the limited data, a combined forecasting model based on neural network and Markov chain was proposed. The average speed and density of road sections were collected, and the BP neural network model was used to train the preliminary fitting model.By using Markov chain method, the distribution and probability of lane changing frequencies in three groups of intervals representing overestimation, normal and underestimation were given to decrease BP neural network error. The combination forecasting model was adopted to predict and analyze the frequency of lane-changing of a road in Xi'an. The results show that the actual frequencies of lane changing are all within the prediction range of the maximum probability given by the model, which indicates that the model can predict the frequency of lane changing according to the average speed and traffic density of the section.
引文
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[11] JIN W L. A multi-commodity Lighthill–Whitham–Richards model of lane-changing traffic flow[J]. Transportation Research Part B Methodological,2013,57(11):361-377.
[12]李娟,曲大义,刘聪,等.基于元胞自动机的车辆换道行为研究[J].公路交通科技,2016,33(11):140-145.
[13]谢寒,蒋阳升,蒋若曦,等.城市快速路交织区换车道次数与车速、密度的关系实证研究[J].武汉理工大学学报,2012,34(8):75-81.
[14]刘克.实用马尔可夫决策过程[M].北京:清华大学出版社,2004:1-12.
[15] ROSENBLATT F. The perceptron:A probabilistic model for information storage and organization in the brain[J]. Psychological Review,1958,65(6):386-408.
[16] RUMELHART D E,HINTON G E,WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature,1986,323(6088):399-421.
[17]杨励雅,邵春福.基于BP神经网络与马尔可夫链的城市轨道交通周边房地产价格的组合预测方法[J].吉林大学学报:工学版,2008,38(3):514-519.
[2]徐良杰,刘志.基于车道选择的交叉口进口道导向系统改善方法[J].华东交通大学学报,2018(1):14-19.
[3]陆建,李英帅.车辆换道行为建模的回顾与展望[J].交通运输系统工程与信息,2017,17(4):48-55.
[4] GIPPS P G. A model for the structure of lane-changing decisions[J]. Transportation Research Part B,1986,20(5):403-414.
[5] AHMED K I,BEN-AKIVA M,KOUTSOPOULOS H N,et al. Models of freeway lane changing and gap acceptance behavior[J].Transportation and Traffic Theory,1996(13):501-515.
[6] SARA M,MAJID S,GEOFF R,et al. Lane-changing decision model for heavy vehicle drivers[J]. Journal of Intelligent Transportation Systems,2012,16(1):24-35.
[7] HUNT J G,LYONS G D. Modelling dual carriageway lane changing using neural networks[J]. Transportation Research Part C Emerging Technologies,1994,2(4):231-245.
[8] WORRALL R D,BULLEN A G,GUR Y. An elementary stochastic model of lane-changing on a multilane highway[J]. Highway Research Record,1970(308):1-12.
[9] WIEDEMANN R,REITER U. Microscopic traffic simulation:the simulation system mission,background and actual state[J]. Project ICARUS(V1052)Final Report,1992,2:1-53.
[10] HAMDAR S H. Modeling driver behavior as a stochastic hazard-based risk-taking process[D]. Evanston:Northwestern University,2008:158-168.
[11] JIN W L. A multi-commodity Lighthill–Whitham–Richards model of lane-changing traffic flow[J]. Transportation Research Part B Methodological,2013,57(11):361-377.
[12]李娟,曲大义,刘聪,等.基于元胞自动机的车辆换道行为研究[J].公路交通科技,2016,33(11):140-145.
[13]谢寒,蒋阳升,蒋若曦,等.城市快速路交织区换车道次数与车速、密度的关系实证研究[J].武汉理工大学学报,2012,34(8):75-81.
[14]刘克.实用马尔可夫决策过程[M].北京:清华大学出版社,2004:1-12.
[15] ROSENBLATT F. The perceptron:A probabilistic model for information storage and organization in the brain[J]. Psychological Review,1958,65(6):386-408.
[16] RUMELHART D E,HINTON G E,WILLIAMS R J. Learning representations by back-propagating errors[J]. Nature,1986,323(6088):399-421.
[17]杨励雅,邵春福.基于BP神经网络与马尔可夫链的城市轨道交通周边房地产价格的组合预测方法[J].吉林大学学报:工学版,2008,38(3):514-519.