摘要Highway passenger travel demand forecasting provides an important basis for travel planning. Considering the relationships among travel modes (highway, railway, and aviation) is necessary to accurately predict travel demand. On the basis of such relationships, influencing factors, including population, gross regional product, highway kilometers, high-speed rail construction, and aviation facility accessibility, are selected to construct a dummy variable model for forecasting highway passenger travel demand. On the basis of the chosen dummy variables (high-speed rail construction and aviation facility accessibility), model formation includes the following four conditions:no high-speed rail construction and low aviation facility accessibility; high-speed rail construction and low aviation facility accessibility; no high-speed rail construction and high aviation facility accessibility; and high-speed rail construction and high aviation facility accessibility. We measure these conditions using panel data at the city and provincial levels of China for the years 2000-2014. The error analysis between the forecasting results and accurate data collection at the National Bureau of Statistics of China verify that the model formation is correct. Results show the travel demand differences under various conditions. The elasticity coefficients among the influencing factors of travel demand explain the co-opetition relationships among travel modes. These results provide valuable guidance to the sustainable development of a passenger transportation system.
Abstract:Highway passenger travel demand forecasting provides an important basis for travel planning. Considering the relationships among travel modes (highway, railway, and aviation) is necessary to accurately predict travel demand. On the basis of such relationships, influencing factors, including population, gross regional product, highway kilometers, high-speed rail construction, and aviation facility accessibility, are selected to construct a dummy variable model for forecasting highway passenger travel demand. On the basis of the chosen dummy variables (high-speed rail construction and aviation facility accessibility), model formation includes the following four conditions:no high-speed rail construction and low aviation facility accessibility; high-speed rail construction and low aviation facility accessibility; no high-speed rail construction and high aviation facility accessibility; and high-speed rail construction and high aviation facility accessibility. We measure these conditions using panel data at the city and provincial levels of China for the years 2000-2014. The error analysis between the forecasting results and accurate data collection at the National Bureau of Statistics of China verify that the model formation is correct. Results show the travel demand differences under various conditions. The elasticity coefficients among the influencing factors of travel demand explain the co-opetition relationships among travel modes. These results provide valuable guidance to the sustainable development of a passenger transportation system.
基金资助:Supported by the Doctoral Research Foundation of Liaoning Province (No.201601257)
通讯作者:
HE Nan
E-mail: honny_he@hotmail.com
引用本文:
何南, 李季涛. 考虑运输方式间影响关系的公路客运交通需求预测[J]. Journal of Highway and Transportation Research and Development, 2018, 12(3): 90-96.
HE Nan, LI Ji-tao. Highway Passenger Travel Demand Forecasting Incorporating Relationships Among Travel Modes. Journal of Highway and Transportation Research and Development, 2018, 12(3): 90-96.
[1] HYMEL K M, SMALL K A, DENDER K V. Induced Demand and Rebound Effects in Road Transport[J]. Transportation Research Part B:Methodological, 2010, 44(10):1220-1241.
[2] HANSEN M,HUANG Y. Road Supply and Traffic in California Urban Areas[J]. Transportation Research, Part A; Policy and Practice, 1997, 31(3):205-218.
[3] BARR L C. Testing for the Significance of Induced Highway Travel Demand in Metropolitan Areas[J]. Journal of the Transportation Research Board, 2000, 1706(1):1-8.
[4] NOLAND R B. Relationships between Highway Capacity and Induced Vehicle Travel[J]. Transportation Research Part A:Policy and Practice, 2001, 35(1):47-72.
[5] HE N, ZHAO S C. Induced Traffic in China:Elasticity Models with Panel Data[J]. ASCE's Journal of Urban Planning and Development, 2015, 141(4):04014046.
[6] WANG Sheng-chang, BAI Shao-Bo, ZHANG Hui. Prediction Methods of Highway Passenger Volume[J]. Journal of Chang'an University:Natural Science Edition, 2005, 25(5):83-85.(in Chinese)
[7] YANG Wen-qi. Prediction of Highway Traffic Volume Based on Genetic Algorithms and BP Neural Network[D]. Chengdu:Southwest Jiaotong University, 2015.(in Chinese)
[8] ZHAO Yue-feng, ZHANG Sheng-rui, WANG Ruo-ya. Forecast of Transfer Traffic Volume during Freeway Reconstruction and Expansion[J]. Journal of Highway and Transportation Research and Development, 2013, 30(10):129-133.(in Chinese)
[9] KLEIN O, CLAISSE G, POCHET P. Le tgv-atlantique:entre récession et concurrence[J]. Post-Print, 1997.
[10] PARK Y, HA H K. Analysis of the Impact of High-speed Railroad Service on Air Transport Demand[J]. Transportation Research Part E Logistics & Transportation Review, 2006, 42(2):95-104.
[11] JIMÉNEZ J L, BETANCOR O. When Trains Go Faster than Planes:the Strategic Reaction of Airlines in Spain[J]. Transport Policy, 2012, 23(9):34-41.
[12] YANG H, ZHANG A. Effects of High-Speed Rail and Air Transport Competition on Prices, Profits and Welfare[J]. Transportation Research Part B Methodological, 2012, 46(46):1322-1333.
[13] RODRIGUE J P. The Economics and Politics of High-Speed Rail:Lessons from Experiences Abroad[J]. High Speed Rail, 2016, 4(1):17-18.
[14] GLEAVE S D. Air and Rail Competition and Complementarity, Final Report for DG TR[R]. London:Commission for the European Communities. 2006.
[15] ALBALATE D, BEL G, FAGEDA X. Competition and Cooperation Between High-speed Rail and Air Transportation Services in Europe[J]. Journal of Transport Geography, 2014, 42:166-174.
[16] ZHAO Sheng-chuan, HE Nan. Elasticity-based Model Applies in the Forecasting of Highway Induced Traffic[J]. Journal of Transportation Systems Engineering and Information Technology, 2011, 11(3):1-7.(in Chinese)
[17] National Bureau of Statistics of China. China Statistical Year book(2000-2014).[DB/OL](2016-10-10). http://www.stats.gov.cn/.(in Chinese)
[18] MEYER M D, MILLER E J. Urban Transportation Planning[M]. New York McGraw-Hill Higher Education. USA, 2001.
[1]
李高盛, 彭玲, 李祥, 吴同. 基于LSTM的城市公交车站短时客流量预测研究[J]. Journal of Highway and Transportation Research and Development, 2019, 13(2): 65-72.
[2]
胡宝雨, 赵琥, 孙祥龙, 王弟鑫, 刘宁. 城市公交与农村客运同步换乘模型研究[J]. Journal of Highway and Transportation Research and Development, 2019, 13(2): 73-79.
[3]
郭建科, 邱煜焜, 白家圆, 王利. 基于城市公共交通可达性的医疗服务空间分异及均等化研究——以大连市为例[J]. Journal of Highway and Transportation Research and Development, 2019, 13(2): 80-89.
[4]
赵妮娜, 赵晓华, 林展州, 葛书芳. 主线分流互通立交指路标志版面形式研究[J]. Journal of Highway and Transportation Research and Development, 2019, 13(2): 90-102.
[5]
姜明, 陈艳艳, 冯移冬, 周瑞. 路侧示警桩设置关键指标研究[J]. Journal of Highway and Transportation Research and Development, 2019, 13(1): 79-87.
[6]
蔡静, 刘莹, 张明辉. 京津冀货物运输结构调整策略研究[J]. Journal of Highway and Transportation Research and Development, 2019, 13(1): 88-93.
[7]
常云涛, 王奕彤. 连续流交叉口信号配时优化模型[J]. Journal of Highway and Transportation Research and Development, 2018, 12(4): 66-74.
[8]
林丽, 冯辉, 朱泳旭. 基于Ring-Barrier相位的干线公交协调控制[J]. Journal of Highway and Transportation Research and Development, 2018, 12(4): 85-91.
[9]
胡祖平, 何建佳. 基于网络可靠性的街区开放适宜度研究[J]. Journal of Highway and Transportation Research and Development, 2018, 12(4): 51-58.
[10]
陈红, 马晓彤, 赵丹婷. 基于元胞自动机的破损路面车辆换道仿真研究[J]. Journal of Highway and Transportation Research and Development, 2018, 12(4): 75-84.
[11]
李新, 毛剑楠, 骆晨, 刘澜. 基于MFD的路网可扩展边界控制方法研究[J]. Journal of Highway and Transportation Research and Development, 2018, 12(4): 59-65.
[12]
郝丽, 胡大伟, 李晨. T-JIT环境下企业供应链中采购管理供应商选择和订单分配研究[J]. Journal of Highway and Transportation Research and Development, 2018, 12(3): 80-89.
[13]
姚佼, 徐洁琼, 倪屹聆. 城市干道多时段协调控制优化研究[J]. Journal of Highway and Transportation Research and Development, 2018, 12(3): 60-70.
[14]
潘兵宏, 余英杰, 武生权, 严考权. 基于UC-win/Road仿真的高速公路出口预告标志前置距离研究[J]. Journal of Highway and Transportation Research and Development, 2018, 12(3): 71-79.
[15]
胡鹏, 帅斌, 吴贞瑶, 李浩歌. 城市危险品道路运输网络的设计和分析[J]. Journal of Highway and Transportation Research and Development, 2018, 12(3): 97-104.