DOI: 10.3724/SP.J.1249.2016.06653

Journal of Shenzhen University Science and Engineering (深圳大学学报理工版) 2016/33:6 PP.653-660

Competitive factors between civil aviation and high-speed rail based on radial basis function and logistic regression

The competitive factors that affect civil aviation transportation and high speed railway are analyzed from two aspects of individual travel demand and traffic supply. Seventeen factors of six dimensions are selected, and radial basis function (RBF) neural network and logistic regression are used to analyze the main competitive factors of the aircraft and the high speed railway. The results show that competitive factors in decending order of influence in turn are the running time, travel distance, fare, reliability, punctuality and arrival time according to the conclusion of RBF. However, the competitive factors from large to small in turn are the running time, travel distance, fare, reliability, arrival time, and punctuality according to the logistic regression analysis. Overall, the greatest factor affecting the competition between civil aviation and high-speed rail is the transport mode property, such as running time, price and punctuality, followed by connection property, such as arrival time. The influence of individual attributes and ticket attributes is relatively little.

Key words:transportationsystems engineering,civil aviation,high-speed rail,radial basis function(RBF) neural network,logistic regression analysis,transportation system planning

ReleaseDate:2016-12-02 18:11:39

[1] Janic M. True multimodalism for mitigating airport congestion: substitution of air passenger transport by high-speed rail[J].Transportation Research Record, 2010, 2177:78-87.

[2] Martín J C ,Román C ,García-Palomares J C, et al. Spatial analysis of the competitiveness of the high-speed train and air transport: the role of access to terminals in the Madrid-Barcelona corridor[J]. Transportation Research Part A: Policy and Practice, 2014, 69: 392-408.

[3] Dobruszkes F. High-speed rail and air transport competition in Western Europe: a supply-oriented perspective[J]. Transport Policy, 2011,18(6): 870-879.

[4] 张 旭,栾维新,赵冰茹.基于非集计模型的武广线高铁与民航竞争研究[J].交通运输系统工程与信息,2012,12(6):17-22. Zhang Xu,Luan Weixin,Zhao Bingru. Competition between Wuhan-Guangzhou high-speed railway and civil aviation based on disaggregate model[J]. Journal of Transportation Systems Engineering and Information Technology,2012,12(6): 17-22.(in Chinese)

[5] 何 韬.我国高速铁路与民航运输竞争关系研究[D].北京:北京交通大学,2012. He Tao. The competitive relationship between high-speed railway and civil aviation in China[D]. Beijing: Beijing Jiaotong University, 2012.(in Chinese)

[6] 芮海田,吴群琪.高铁运输与民航运输选择下的中长距离出行决策行为[J].中国公路学报,2016,29(3):134-141. Rui Haitian, Wu Qunqi. Medium-and-long distance travel mode decision between high speed rail and civil aviation[J]. China Journal of Highway and Transport, 2016, 29(3): 134-141.(in Chinese)

[7] Dobruszkes F, Dehon C, Givoni M. Does European high-speed rail affect the current level of air services? An EU-wide analysis[J]. Transportation Research Part A: Policy and Practice, 2014, 69: 461-475.

[8] Nesset E, Helgesen Ø. Effects of switching costs on customer attitude loyalty to an airport in a multi-airport region[J]. Transportation Research Part A: Policy and Practice, 2014, 67, 240-253.

[9] Pellegrini P, Rodriguez J. Single European sky and single European railway area: a system level analysis of air and rail transportation[J]. Transportation Research Part A: Policy and Practice, 2013, 57: 64-86.

[10] Van Exel N J A, Rietveld P. Could you also have made this trip by another mode? An investigation of perceived travel possibilities of car and train travellers on the main travel corridors to the city of Amsterdam, the Netherlands[J]. Transportation Research Part A: Policy and Practice, 2009, 43(4): 374-385.

[11] 何宇强,毛保华,陈团生,等.高速客运专线客流分担率模型及其应用研究[J].铁道学报,2006,28(3):18-20. He Yuqiang,Mao Baohua,Chen Tuansheng,et al.The mode share model of the high-speed passenger railway line and its application[J].Journal of the China Railway Society, 2006,28(3):18-20.(in Chinese)

[12] 王 爽,赵 鹏.基于Logit模型的客运专线旅客选择行为分析[J].铁道学报,2009,31(3):6-10. Wang Shuang, Zhao Peng. Analysis of passengers' choice behavior for dedicated passenger railway lines based on logit model[J]. Journal of the China Railway Society, 2009,31(3): 6-10.(in Chinese)

[13] 王孝之,赵胜川,闰祯祯.基于Rank Logit模型对城际交通分担率的计算方法研究[J].交通运输系统工程与信息,2012,12(2):137-143. Wang Xiaozhi, Zhao Shengchuan, Yan Zhenzhen. Intercity transport mode spilt calculation method based on Rank Logit model[J]. Journal of Transportation Systems Engineering and Information Technology,2012,12(2):137-143.(in Chinese)

[14] 朱顺应,邓 爽,王 红,等.具有模糊特性变量的出行方式预测logit模型[J].交通运输工程学报,2013,13(3):71-78. Zhu Shunying, Deng Shuang, Wang Hong, et al. Predictive logit model of trip mode with fuzzy attribute variables[J]. Journal of Traffic and Transportation Engineering,2013,13(3):71-78.(in Chinese)

[15] 孙彪瑞,廉飞宇,王 珂,等. 基于径向基神经网络的储粮通风智能决策研究[J].粮食与饲料工业,2014 (9):12-15. Sun Biaorui, Lian Feiyu, Wang Ke, et al.Grain storage ventilation intelligent decision based on radial basis function neural network[J]. Cereal and Feed Insustry, 2014(9): 12-15.(in Chinese)

[16] 阳同光,桂卫华. 基于粒子群优化神经网络观测器感应电机定子电阻辨识[J]. 电机与控制学报,2015,19(2):89-95. Yang Tongguang, Gui Weihua. Stator resistance identification for induction motor based on particle swarm optimization neural network observer[J]. Electric Machines and Control, 2015, 19(2): 89-95.(in Chinese)

[17] 田景文,高美娟.人工神经网络算法研究及应用[M].北京:北京理工大学出版社,2006:41-45. Tian Jingwen, Gao Meijuan. Research and application of artificial neural network algorithm[M]. Beijing: Beijing Institute of Technology University Press,2006:41-45.(in Chinese)

[18] 杜 强,贾丽艳.SPSS统计分析从入门到精通[M].北京:人民邮电出版社,2011. Du Qiang, Jia Liyan. SPSS statistical analysis from entry to master[M]. Beijing: People's Posts and Telecommunications Press, 2011.(in Chinese)

[19] 李春林,刘 淼,胡远满,等.基于增强回归树和 Logistic 回归的城市扩展驱动力分析[J].生态学报,2014,34(3):727-737. Li Chunlin, Liu Miao,Hu Yuanman,et al. Driving forces analysis of urban expansion based on boosted regression trees and Logistic regression[J].Acta Ecologica Sinica, 2014,34(3): 727-737.(in Chinese)