公交运力资源优化配置的分析与研究
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摘要
面对城市日益增长的客流需求,最大限度地优化公交运力资源配置势在必行。改善公交运力配置,不权仅是提高公交运营效率的需要,更是提高公交服务水平,实现公交运力资源均衡配置和创建环境友好型社会的内在需要。
要解决公交问题,仅仅依靠增加公交车的数量,或者调整公交线路并不能从根本上解决问题,相反,一味地增加公交车的数量,不权会造成公交资源的浪费,更会加大城市交通的压力。因此,只有充分地利用现有的公交资源,在满足公交企业和乘客多方利益的前提下,对相关资源进行优化配置,才是解决问题的关键所在
本文首先在综合分析公交运力配置的影响囚素的基础上,将公交运力优化配置问题定性为多目标优化问题,并进一步介绍了基于基尼系数的多目标优化模型,在分析原模型的基础上,引进了基尼系数这一经济学指标,通过量化表示将资源配置均衡度作为新的目标函数加入原模型,得到了改进的新模型。并通过实例化数据验证,通过前后的调整,各站点的客流量分担率(基尼系数)由原来的0.29改进为0.21,从客流量分布均衡性的角度说明了新模型比原模型具有较好的现实拟合度。
本文明确了客流量预测在整个公交运力配置流程中的关键作用。客流量的准确预测是公交资源优化配置的依据和前提。本文给出了基于Elman神经网络的公交客流量预测流程。最后,通过数据搜集,利用北京市某条线路近8年的客流量数据进行了实例预测。最后通过与当前较流行的BP静态神经网络比较,发现Elman神经网络整体上比BP神经网络具有较高的预测精度。
With the continuous growth of the city traffic. optimizing bus resources as possible as can is imperative. To improve the public transport capacity configuration, is not only able to help improve public transport operation efficiency, but also improve the bus service level, realize the balance of whole bus transport resource and create environment-friendly society.
To solve the bus problems, only relying on increasing the number of public buses or adjusting the bus lines, can't resolve the problem ultimately, instead, increasing the number of public buses blindly, not only can cause public transport waste of resources, but also will increase the urban traffic pressure. Therefore, how to make full use of the existing public transport resources, and how to meet the interest of bus passengers and State Transit,under the premise of related resources to optimize the allocation, is the key to the problem.
First of all, the factors that relevant factor of public transport capacity are comprehensively analysed so we characterize optimal allocation of public transport capacity as multi-objective optimization problem in this paper. Moreover. multi-objective optimization model is introduced that bases on game theory. By analysis of the original model, we introduce the Gini coefficient in the economic indicators. After quantization, the degree of resource allocation equilibrium is viewed as a new objective function by putting in the original model. A new and improved model is obtained finally. Applying to instance data. by adjustment, the share rate of the passenger volume (Gini coefficient) improved from0.29to0.21.we can conclude that the new model is better than the original model in line with reality from the balance of traffic distribution。
This paper made clear that bus passenger forecast plays the key role in the entire capacity configuration flow. Accurate prediction of the passenger is the basis and premise of bus resources optimization allocation. In this paper.the bus passenger forecasting model based on the liman neural network is given. Finally.the forecast is carried on using the nearly eight years data of Beijing passengers. Finally by comparing with BP static neural network which is more popular currently, we discover that Elman neural network is of high precision than the BP neural network.
要解决公交问题,仅仅依靠增加公交车的数量,或者调整公交线路并不能从根本上解决问题,相反,一味地增加公交车的数量,不权会造成公交资源的浪费,更会加大城市交通的压力。因此,只有充分地利用现有的公交资源,在满足公交企业和乘客多方利益的前提下,对相关资源进行优化配置,才是解决问题的关键所在
本文首先在综合分析公交运力配置的影响囚素的基础上,将公交运力优化配置问题定性为多目标优化问题,并进一步介绍了基于基尼系数的多目标优化模型,在分析原模型的基础上,引进了基尼系数这一经济学指标,通过量化表示将资源配置均衡度作为新的目标函数加入原模型,得到了改进的新模型。并通过实例化数据验证,通过前后的调整,各站点的客流量分担率(基尼系数)由原来的0.29改进为0.21,从客流量分布均衡性的角度说明了新模型比原模型具有较好的现实拟合度。
本文明确了客流量预测在整个公交运力配置流程中的关键作用。客流量的准确预测是公交资源优化配置的依据和前提。本文给出了基于Elman神经网络的公交客流量预测流程。最后,通过数据搜集,利用北京市某条线路近8年的客流量数据进行了实例预测。最后通过与当前较流行的BP静态神经网络比较,发现Elman神经网络整体上比BP神经网络具有较高的预测精度。
With the continuous growth of the city traffic. optimizing bus resources as possible as can is imperative. To improve the public transport capacity configuration, is not only able to help improve public transport operation efficiency, but also improve the bus service level, realize the balance of whole bus transport resource and create environment-friendly society.
To solve the bus problems, only relying on increasing the number of public buses or adjusting the bus lines, can't resolve the problem ultimately, instead, increasing the number of public buses blindly, not only can cause public transport waste of resources, but also will increase the urban traffic pressure. Therefore, how to make full use of the existing public transport resources, and how to meet the interest of bus passengers and State Transit,under the premise of related resources to optimize the allocation, is the key to the problem.
First of all, the factors that relevant factor of public transport capacity are comprehensively analysed so we characterize optimal allocation of public transport capacity as multi-objective optimization problem in this paper. Moreover. multi-objective optimization model is introduced that bases on game theory. By analysis of the original model, we introduce the Gini coefficient in the economic indicators. After quantization, the degree of resource allocation equilibrium is viewed as a new objective function by putting in the original model. A new and improved model is obtained finally. Applying to instance data. by adjustment, the share rate of the passenger volume (Gini coefficient) improved from0.29to0.21.we can conclude that the new model is better than the original model in line with reality from the balance of traffic distribution。
This paper made clear that bus passenger forecast plays the key role in the entire capacity configuration flow. Accurate prediction of the passenger is the basis and premise of bus resources optimization allocation. In this paper.the bus passenger forecasting model based on the liman neural network is given. Finally.the forecast is carried on using the nearly eight years data of Beijing passengers. Finally by comparing with BP static neural network which is more popular currently, we discover that Elman neural network is of high precision than the BP neural network.
引文
[1]朱媛玲.我国城市交通发展现状与优化[J].长春大学学报,2009年3月
[2]胡兴华.公交线路运力配置方法研究[D].西南交通大学硕士学位论文,2()()7
[3]陈志红等.运输组织技术[M].北京：人民交通出版社,2003.8
[4]姚新胜等.公路旅客运输运力配置现状及趋势分析[J].公路与汽运,2010,1(1)
[5]沈志云.交通运输工程学[M].北京：人民交通出版社,1999：52-53.
[6]姜平等.基于Elman型回归神经网络的公交客流预测[J].合肥工业大学学报.2008,3(31)
[7]徐磊.基于遗传算法的多目标优化问题的研究与应用[D].中南大学硕士学位论文,2009.
[8]夏钰.城市公交线路运力合理配置方法研究[D].东南大学硕士学位论文2006：33.
[9]Khaled EI—Raye, Amr Kandil. Time-cost.quality trade-off analsis for highway construction[J]. Journal of Construction Engineering and Management,2005(4): 477-486.
[10]王炜等.城市交通规划理论及其应用[M].南京：东南大学出版社,1998.
[11]高云莉等.不确定条件下工程项目的多目标模糊均衡优化[J].数学的实践与认识.2010,11(40)
[12]包云等.基于基尼系数的铁路运力资源配置均衡性研究[J].物流技术2009,28(3),66-69
[13]王志国.中国铁路运力资源配置研究[D].北京：北京交通大学硕士学位论文,2006.
[14]陆锡明,陈小雁.客运规划与城市发展[M]. 上海：华东理工大学出版社1996.
[15]高红丽.高速公路客运发展的关键问题研究[D].西南交通大学硕士学位论文,2004
[16]SUN Hong, LI Feng, LIQingsong. Research of civilaviation flight direct operating cost[J]. Journal of Transportation Engineering and Information,2007, 5(1)
[17]MelkoteS, Daskin M S. An integrated model of facility location and transportation network design[J]. Transportation Research:Part A,2001,35(6).
[18]F. Hausdorff,Mengenlehre. Set Theory[M]. New York:Chelsea Publishing Company,1957:327-329
[19]杨新苗. 公交线路客流模糊神经网络预测模型[J].公路交通科技.2000,4(17)
[20]杜艳平.智能车流动态预测理论及方法[M].北京：中国铁道出版社,2009.
[21]徐敏.基于博弈思想的优化算法研究[D].中国科学技术大学博士学位论文,2006
[22]Singh S K. Comparing efficiency across state transport undertakings:A production frontier approach [J-]. Journal of Transport Management,2003(27).
[23]李晓忠,汪培庄,罗承忠.模糊神经网络[M].贵阳：贵州科技出版社,1994.
[24]王英杰等.基于GIS平台的地铁客流预测模型构建[J].交通信息与安全.2010,3(28)
[25]张乃尧,阎平凡.神经网络与模糊控制[M].北京：清华大学出版社,1998.
[26]H. w. Kuhn。A. W. Tucker. Nonlinear Programming。in "Proceeding of the Second Berley symposium Off mathematical Statistics and Probabiliyt" California:University of California Press.1951:481-492
[27]胡兴华等.城市公交线路车辆配置模型研究[J].变通运输工程与信息学报,2009,3(7)
[28]Palo Delle Site, Francesco Filippi. Bus service optimization with fuel saving objective and various financial constraints[J]. Transportation Reseach Part A. 32(1998):73:87.
[29]徐薇等.一种基于数据融合和方法融合的时空综合预测算法[J].计算机研究与发展,2005,42(7)
[30]金国.客运专线条件下铁路运输通道运力资源优化配置模型研究[D].北京交通大学硕士学位论文,2009
[31]何杰等.丛于运力均衡与运量转移的公路货运运力结构预测[J].中国公路学报,2006,5(19)
[32]U. S. Department of Transportation. Report of Comprehensive Truck Size and Weight Study[R]. Washington D C:U. S. Department of Transportation,2000.
[33]刘志刚等.基于禁忌搜索的公交区域调度配车模型研究[J].交通运输工程与信息,2007,4(5)
[34]刘逻.遗传算法和模拟退火算法在车辆线路问题上的研究及应用[D].长春理工大学硕士学位论文.2008
[35]夏锰.城市公交线路运力合理配置方法研究[D].东南大学硕士学位文.2006.
[36]Ennio Cascetta, Andrea Papola,A joint mode-transit service choice model incorporating the effect ofregional transport service timetables. Transportation Research 2003
[37]张文尝,运输通道系统分析[J].交通运输系统工程与信息,2001,1(2)：135-137
[38]hangyao Yah, Hao-Lei Chert'A scheduling model and a solution algorithm for in ter-city bus carriers[J]. Transportation Research Part A36,2002:805·825
[39]Ali Haghani, Mohamadreza, Kun—Hung Chiang. A Comparative Analysis of Bus Transit Vehicle Scheduling Modelp]. Transportation Research PanB,2003, 37:301-322.
[40]张重文.道路客运企业车辆运力配置结构均衡问题的研究[D].华南理工大学硕士学位论文,2005
[41]FEKPE E. Evaluating Truck Weight Regulatory Policies[J]. Canadian Journal of Civil Engineering,1995,22(10):1235-1239.
[42]Chela.1. A predictive system for blast furnaces by integraring a neural network with qualitative analysis[J]. Engineering Application of Artificial Intelligence, 2001,14(5):77-85.
[43]Bertossi A. Carraresi P, Gallo G on some matching problems arising in Vehicle schedulingmodels[J], Networks。1987,17:271-281.
[44]ADRANGL B, CHOW G, RAFFIEE K. The effects of market structure and technology on airline fleet composition after deregulation[J]. Review of Industrial Organization,1999,15:77-88.
[45]SUN Hong, ZHAO Liyuan, ZHANG Xiang, et al. A method of evaluating airline fleet composition[C]. Washington D. C.:IEEE Computer Society.2008: 332-336.
[2]胡兴华.公交线路运力配置方法研究[D].西南交通大学硕士学位论文,2()()7
[3]陈志红等.运输组织技术[M].北京：人民交通出版社,2003.8
[4]姚新胜等.公路旅客运输运力配置现状及趋势分析[J].公路与汽运,2010,1(1)
[5]沈志云.交通运输工程学[M].北京：人民交通出版社,1999：52-53.
[6]姜平等.基于Elman型回归神经网络的公交客流预测[J].合肥工业大学学报.2008,3(31)
[7]徐磊.基于遗传算法的多目标优化问题的研究与应用[D].中南大学硕士学位论文,2009.
[8]夏钰.城市公交线路运力合理配置方法研究[D].东南大学硕士学位论文2006：33.
[9]Khaled EI—Raye, Amr Kandil. Time-cost.quality trade-off analsis for highway construction[J]. Journal of Construction Engineering and Management,2005(4): 477-486.
[10]王炜等.城市交通规划理论及其应用[M].南京：东南大学出版社,1998.
[11]高云莉等.不确定条件下工程项目的多目标模糊均衡优化[J].数学的实践与认识.2010,11(40)
[12]包云等.基于基尼系数的铁路运力资源配置均衡性研究[J].物流技术2009,28(3),66-69
[13]王志国.中国铁路运力资源配置研究[D].北京：北京交通大学硕士学位论文,2006.
[14]陆锡明,陈小雁.客运规划与城市发展[M]. 上海：华东理工大学出版社1996.
[15]高红丽.高速公路客运发展的关键问题研究[D].西南交通大学硕士学位论文,2004
[16]SUN Hong, LI Feng, LIQingsong. Research of civilaviation flight direct operating cost[J]. Journal of Transportation Engineering and Information,2007, 5(1)
[17]MelkoteS, Daskin M S. An integrated model of facility location and transportation network design[J]. Transportation Research:Part A,2001,35(6).
[18]F. Hausdorff,Mengenlehre. Set Theory[M]. New York:Chelsea Publishing Company,1957:327-329
[19]杨新苗. 公交线路客流模糊神经网络预测模型[J].公路交通科技.2000,4(17)
[20]杜艳平.智能车流动态预测理论及方法[M].北京：中国铁道出版社,2009.
[21]徐敏.基于博弈思想的优化算法研究[D].中国科学技术大学博士学位论文,2006
[22]Singh S K. Comparing efficiency across state transport undertakings:A production frontier approach [J-]. Journal of Transport Management,2003(27).
[23]李晓忠,汪培庄,罗承忠.模糊神经网络[M].贵阳：贵州科技出版社,1994.
[24]王英杰等.基于GIS平台的地铁客流预测模型构建[J].交通信息与安全.2010,3(28)
[25]张乃尧,阎平凡.神经网络与模糊控制[M].北京：清华大学出版社,1998.
[26]H. w. Kuhn。A. W. Tucker. Nonlinear Programming。in "Proceeding of the Second Berley symposium Off mathematical Statistics and Probabiliyt" California:University of California Press.1951:481-492
[27]胡兴华等.城市公交线路车辆配置模型研究[J].变通运输工程与信息学报,2009,3(7)
[28]Palo Delle Site, Francesco Filippi. Bus service optimization with fuel saving objective and various financial constraints[J]. Transportation Reseach Part A. 32(1998):73:87.
[29]徐薇等.一种基于数据融合和方法融合的时空综合预测算法[J].计算机研究与发展,2005,42(7)
[30]金国.客运专线条件下铁路运输通道运力资源优化配置模型研究[D].北京交通大学硕士学位论文,2009
[31]何杰等.丛于运力均衡与运量转移的公路货运运力结构预测[J].中国公路学报,2006,5(19)
[32]U. S. Department of Transportation. Report of Comprehensive Truck Size and Weight Study[R]. Washington D C:U. S. Department of Transportation,2000.
[33]刘志刚等.基于禁忌搜索的公交区域调度配车模型研究[J].交通运输工程与信息,2007,4(5)
[34]刘逻.遗传算法和模拟退火算法在车辆线路问题上的研究及应用[D].长春理工大学硕士学位论文.2008
[35]夏锰.城市公交线路运力合理配置方法研究[D].东南大学硕士学位文.2006.
[36]Ennio Cascetta, Andrea Papola,A joint mode-transit service choice model incorporating the effect ofregional transport service timetables. Transportation Research 2003
[37]张文尝,运输通道系统分析[J].交通运输系统工程与信息,2001,1(2)：135-137
[38]hangyao Yah, Hao-Lei Chert'A scheduling model and a solution algorithm for in ter-city bus carriers[J]. Transportation Research Part A36,2002:805·825
[39]Ali Haghani, Mohamadreza, Kun—Hung Chiang. A Comparative Analysis of Bus Transit Vehicle Scheduling Modelp]. Transportation Research PanB,2003, 37:301-322.
[40]张重文.道路客运企业车辆运力配置结构均衡问题的研究[D].华南理工大学硕士学位论文,2005
[41]FEKPE E. Evaluating Truck Weight Regulatory Policies[J]. Canadian Journal of Civil Engineering,1995,22(10):1235-1239.
[42]Chela.1. A predictive system for blast furnaces by integraring a neural network with qualitative analysis[J]. Engineering Application of Artificial Intelligence, 2001,14(5):77-85.
[43]Bertossi A. Carraresi P, Gallo G on some matching problems arising in Vehicle schedulingmodels[J], Networks。1987,17:271-281.
[44]ADRANGL B, CHOW G, RAFFIEE K. The effects of market structure and technology on airline fleet composition after deregulation[J]. Review of Industrial Organization,1999,15:77-88.
[45]SUN Hong, ZHAO Liyuan, ZHANG Xiang, et al. A method of evaluating airline fleet composition[C]. Washington D. C.:IEEE Computer Society.2008: 332-336.