城市轨道交通接驳方式选择及客流吸引范围研究
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摘要
大城市的干线交通朝着以快速轨道交通为主体的多层次综合客运体系发展已成为必然趋势。为实现轨道交通的干线运输功能,必须充分考虑轨道交通衔接问题,然而不同的站点其客流来源不同,接驳方式构成也不同,需要分类研究。本文在对站点进行分类的基础上,针对中心商务区较强的客流吸引特征,选择了中心商务区站点,尝试着建立城市轨道交通接驳方式选择模型和客流吸引范围模型,探讨各种接驳方式的分担率以及基于不同接驳方式的客流吸引范围。围绕这一目标,论文开展了以下几个方面的工作。
首先,在分析常规轨道交通站点分类方法的基础上,综合轨道交通站点的节点特性和场所特性,归纳出站点分类的三级基础参数,并对参数进行说明和量化,提出了基于聚类技术的站点分类方法。结合哈尔滨市地铁1号线,运用该方法对其18个站点进行聚类分析。
其次,结合站点聚类分析结果,综合轨道交通客流来源,将站点归纳为三种类型:中心商务区站点、换乘枢纽型站点和外围区一般站点。针对三种不同类型站点,开展轨道交通接驳方式RP和SP调查、数据处理和数据统计分析,为建立接驳方式选择模型提供数据支撑。
再次,针对中心商务区较强的客流吸引特征,运用Nested Logit模型分层理论,建立中心商务区站点接驳方式选择的2层NL模型,包括下层接驳方式选择模型和上层出行方式选择模型。并通过引入融合参数,实现对轨道交通出行端和目的端接驳方式调查数据的融合。结合案例,运用建立的模型分析各种接驳方式的分担率。
最后,将舒适度成本引入广义出行费用中,通过比较轨道交通和常规公交的广义出行费用,建立起基于中心区可达性的客流吸引范围模型。结合哈尔滨市地铁1号线,运用Logit模型求解方法标定出轨道交通出行与常规公交出行的效用函数,以此计算出轨道交通站点的客流吸引范围。
Urban rail transit is the shaft of the traffic system of city, arterial traffic of big city that is moving to rapid rail transit as the main part with multi-level development of comprehensive passenger transport system has already been the inevitable trend. In order to realize arterial transport function of urban rail transit, rail transit connection problems must be fully considerated. However, passenger flow source is different from different rail stations, and the composition of access mode is also different, which need to be classificated, so rail transit station should be classified first. This paper attempts to build urban rail transit access mode selection model and passenger flow attracting range model, in order to study the sharing rate of different access mode and passenger flow attracting range based on attraction of central business district. For this aim, the paper has carried out the following work.
Firstly, by the analysis of common rail transit station classification method, cluster analysis method to classfy rail transit station is proposed combined with characteristics of station and station area. This chapter categories and quantifies the classification parameters, then taking Harbin subway line 1 as example, 18 rail transit stations of which are clustered.
Secondly, based on results of station cluster analysis, together with passenger flow source of rail transit, the station is divided into three categories: central business district station, transfer hub station, and marginal area station. Then carrying on RP and SP survey of access mode, data processing and data statistical analysis, which support for the establishment of model.
Thirdly, in comparing the characteristics of disctete choice model, 2 level of Nested Logit model has been chosen to study access mode selection of central business district station, including the lower access mode selection model and the upper travel mode selection model. By introducing the fusion parameter, the lower access mode selection model is established based on data fusion of arrival access mode and departure access mode of rail transit. Then using the model to analyze the case.
Finally, by introducing the cost of traffic comfort to generalized trip costs, and comparing generalized trip costs of rail transit and public traffic, urban rail transit passenger flow attracting range model is established based on attraction of urban central district. Taking Harbin subway line 1 as example, rail and public transport travel utility fuction is calibrated by the Logit model, then theoretical value of passenger flow attracting range is calibrated.
首先,在分析常规轨道交通站点分类方法的基础上,综合轨道交通站点的节点特性和场所特性,归纳出站点分类的三级基础参数,并对参数进行说明和量化,提出了基于聚类技术的站点分类方法。结合哈尔滨市地铁1号线,运用该方法对其18个站点进行聚类分析。
其次,结合站点聚类分析结果,综合轨道交通客流来源,将站点归纳为三种类型:中心商务区站点、换乘枢纽型站点和外围区一般站点。针对三种不同类型站点,开展轨道交通接驳方式RP和SP调查、数据处理和数据统计分析,为建立接驳方式选择模型提供数据支撑。
再次,针对中心商务区较强的客流吸引特征,运用Nested Logit模型分层理论,建立中心商务区站点接驳方式选择的2层NL模型,包括下层接驳方式选择模型和上层出行方式选择模型。并通过引入融合参数,实现对轨道交通出行端和目的端接驳方式调查数据的融合。结合案例,运用建立的模型分析各种接驳方式的分担率。
最后,将舒适度成本引入广义出行费用中,通过比较轨道交通和常规公交的广义出行费用,建立起基于中心区可达性的客流吸引范围模型。结合哈尔滨市地铁1号线,运用Logit模型求解方法标定出轨道交通出行与常规公交出行的效用函数,以此计算出轨道交通站点的客流吸引范围。
Urban rail transit is the shaft of the traffic system of city, arterial traffic of big city that is moving to rapid rail transit as the main part with multi-level development of comprehensive passenger transport system has already been the inevitable trend. In order to realize arterial transport function of urban rail transit, rail transit connection problems must be fully considerated. However, passenger flow source is different from different rail stations, and the composition of access mode is also different, which need to be classificated, so rail transit station should be classified first. This paper attempts to build urban rail transit access mode selection model and passenger flow attracting range model, in order to study the sharing rate of different access mode and passenger flow attracting range based on attraction of central business district. For this aim, the paper has carried out the following work.
Firstly, by the analysis of common rail transit station classification method, cluster analysis method to classfy rail transit station is proposed combined with characteristics of station and station area. This chapter categories and quantifies the classification parameters, then taking Harbin subway line 1 as example, 18 rail transit stations of which are clustered.
Secondly, based on results of station cluster analysis, together with passenger flow source of rail transit, the station is divided into three categories: central business district station, transfer hub station, and marginal area station. Then carrying on RP and SP survey of access mode, data processing and data statistical analysis, which support for the establishment of model.
Thirdly, in comparing the characteristics of disctete choice model, 2 level of Nested Logit model has been chosen to study access mode selection of central business district station, including the lower access mode selection model and the upper travel mode selection model. By introducing the fusion parameter, the lower access mode selection model is established based on data fusion of arrival access mode and departure access mode of rail transit. Then using the model to analyze the case.
Finally, by introducing the cost of traffic comfort to generalized trip costs, and comparing generalized trip costs of rail transit and public traffic, urban rail transit passenger flow attracting range model is established based on attraction of urban central district. Taking Harbin subway line 1 as example, rail and public transport travel utility fuction is calibrated by the Logit model, then theoretical value of passenger flow attracting range is calibrated.
引文
1欧阳志坚,马小毅.广州市轨道交通客流规模预测分析[J].城市轨道交通研究.2004(3): 63~65
2李得伟,鲁放,韩宇.城市轨道交通客流补偿及引导措施研究[J].都市快轨交通.2008.21(2):19~20
3 Yadin. M. Development of a model for probabilistic discrete decisions: Department of Statistics. University of California, 1985:40~45
4 Gumbel E J. Distribution a plausier variable dontles marges sont donnees. Paris: Comptes Rendus De Academie Des Science.1985:2717~2720
5 Sheffi Y. On the estimation of the multinomial probit model splitting model. Transportation Research.1982, 16A:174~179
6 Denis Bolduc. A practical technique to estimate multinomial probit models in transportation. Transportation Research Part B33.1999:63~79
7 Jofre Swait. Choice set generation within the generalized extreme value family of discrete choice models. Transportation Research Part B35.2001:643~666
8 Abdel,A.M.A.,Kitamura,R.,Jovani. Exploring route choice behavior using geographic information system-based alternative routes and hypothetical travel time information input. Transportation Research record 1493, 1995:74~80
9 Fujii,S., Iida,Y.,Uchida.T Dynamic traffics emulation to evaluate Vehicle navigation systems. In: Vehicle Navigation and Information Systems Conference Proceedings, 1994:239~244
10 Transportation Research Board (National Research Council).Traffic Data Collection and Analysis: Methods and Procedures.1986.12:55~56
11 Chandra R Bhat. Incorporating observed and unobserved heterogeneity in urban work travel mode choice modeling. Transportation Science.2000, 34(2):228~238
12 McFadden D,Reid F A. Aggregate travel demand forecasting from Disaggregate Behavioral Models.T.R.R.1975:24~37
13 Frank S.Koppelman, Chieh-Hua Wen. The paired combinatorial logit model: properties, estimation and application, Transportation Research.2000, 34B(2): 75~89
14 Daganzo,C.F.Multinomial Probit: The Theory and its Applications to DemandForecasting. Academic Press.1979:17~18
15 Domencich,T.A.,Kraft,Geta l. Estimation of urban passenger travel behavior: an economic demand model.H.R.R.1978:64~78
16 By ChristineM. Benedek and Laurence R.Rilet. Equitable Traffic Assignment with Environmental Cost Functions. Journal of Transportation Engineering.1998, 124(1):16~22
17 Korf J.L, M. J. Demetskv. Analysis of Rapid Transit Access Mode Choice. TRR.817, 1981
18 Bates E. G. A study of Passenger Transfer Facilities. TRB. 662.1988
19 Ian S. J. Dlckins. Park and Ride Facilities on Light Rail Transit Systems. Transportation Research.18:23~36, 1991
20马小毅.土地利用性质调整对轨道交通客运量的影响[J].都市快轨交通. 2008. 21(3):2~3
21杜彩军,蒋玉琨.城市轨道交通与其他交通方式衔接规律的探讨[J].都市快轨交通. 2005. 18(3):45~49
22黄杉,关宏志.城市轨道站点衔接客流比例标准确定研究—以北京市为例[J].交通标准化. 2008. 186(2):15~18
23傅搏峰,吴娇蓉,陈小鸿.郊区轨道站点分类方法研究[J].铁道学报. 2008. 20(6):19~23
24吴娇蓉,毕艳祥,傅博峰.基于郊区轨道交通站点分类的客流特征和换乘系统优先级分析[J].城市轨道交通研究. 2007.11:23~28
25岳芳,毛保华,陈团生.城市轨道交通接驳方式的选择[J].都市快轨交通. 2007.20(4):36~39
26殷远飞.城市轨道接驳方式选择研究[D].北京工业大学硕士毕业论文2005
27王文红,关宏志,王山川. Nested-Logit模型在轨道交通衔接方式选择中的应用[J].城市轨道交通研究.2008.7:25~29
28黄杉,关宏志,严海.轨道交通衔接方式选择行为研究—以北京市轨道交通为例[J].土木工程学报. 2009.42(7):126~130
29马小毅,金安,周志华.一体化程度对轨道交通客运量的影响[J].都市快轨交通. 2009. 22(2):43~46
30王树盛,黄卫,陆振波,俞先江.都市圈轨道交通客流预测方法研究[J].城市轨道交通研究. 2004. 1:40~43
31轩宏伟,王花兰,颜月霞.常规公交影响下的轨道交通吸引范围研究[J].铁道运输与经济. 2007. 29(7):61~63
32王秋平,李峰.城市其他客运交通换乘轨道交通协调探讨[J].西安建筑科技大学学报(自然科学版). 2003.35(2):32~36
33刘伟华,刘惠敏,晏启鹏.轨道交通环线站点环内吸引区域研究[J].长沙铁道学院学报.2002.20(4):94~98
34姚丽亚,关宏志.轨道交通线网服务范围研究[J].北京工业大学学报. 2008.34(7):738~742
35黄丽彬.大城市轨道交通站点对地区发展的影响评价研究—以上海徐家汇轨道枢纽为例[D].同济大学硕士学位论文.2006:25~30
36郑文含.不同类型轨道交通站点地区开发强度探讨[J].《2008城市发展与规划国际论坛论文集》2008:93~95
37曹玫.城市轨道交通与常规公交客流一体化研究[D].东南大学硕士学位论文.2005:12~15
38 Klaus Backhaus, Bernd Erichson, Wulff Plinke, Xuyi Wang Rolf Weiber. Multivariate statistical analysis[M](中文版)
39 MOSHE BEN-AKIVA. STEVEN R LERMAN. Discrete Choice Analysis:Theory and Application to Travel Demand[M]. London: The MIT Press Carbridge, 1985
40 Hensher D A, Bradley M. Using stated response data to enrich revealed preference discrete choice models[J]. Marketing Letters. 1993,4(2):139
41 AO En-jian, Morikawa T. A study on nested logit mode choice model for intercity high-speed rail system with combined RP/SP data[J].Traffic and Transportation Studies Proceedings of ICTTS,2002:612~619
42金安. LOGIT模型参数估计方法研究[J].交通运输系统工程与信息. 2004.4(1): 72~74
43覃裔.轨道交通枢纽规划与设计理论研究[D].同济大学硕士论文.2002: 66~67
44郭丽娜.城市轨道交通与土地利用协调发展的研究[D].北京交通大学硕士学位论文. 2006: 33~38
45 Shama S C, Tayebali A, Wemer A.乡村道路的费用效益分析及用户评判闭.运输研究报告第1009号,TRB,华盛顿,D.C. 1985
46李林波,吴兵.交通方式选择中心理因素影响分析[J].山东交通学院学报,2003.11(3): 27~31
2李得伟,鲁放,韩宇.城市轨道交通客流补偿及引导措施研究[J].都市快轨交通.2008.21(2):19~20
3 Yadin. M. Development of a model for probabilistic discrete decisions: Department of Statistics. University of California, 1985:40~45
4 Gumbel E J. Distribution a plausier variable dontles marges sont donnees. Paris: Comptes Rendus De Academie Des Science.1985:2717~2720
5 Sheffi Y. On the estimation of the multinomial probit model splitting model. Transportation Research.1982, 16A:174~179
6 Denis Bolduc. A practical technique to estimate multinomial probit models in transportation. Transportation Research Part B33.1999:63~79
7 Jofre Swait. Choice set generation within the generalized extreme value family of discrete choice models. Transportation Research Part B35.2001:643~666
8 Abdel,A.M.A.,Kitamura,R.,Jovani. Exploring route choice behavior using geographic information system-based alternative routes and hypothetical travel time information input. Transportation Research record 1493, 1995:74~80
9 Fujii,S., Iida,Y.,Uchida.T Dynamic traffics emulation to evaluate Vehicle navigation systems. In: Vehicle Navigation and Information Systems Conference Proceedings, 1994:239~244
10 Transportation Research Board (National Research Council).Traffic Data Collection and Analysis: Methods and Procedures.1986.12:55~56
11 Chandra R Bhat. Incorporating observed and unobserved heterogeneity in urban work travel mode choice modeling. Transportation Science.2000, 34(2):228~238
12 McFadden D,Reid F A. Aggregate travel demand forecasting from Disaggregate Behavioral Models.T.R.R.1975:24~37
13 Frank S.Koppelman, Chieh-Hua Wen. The paired combinatorial logit model: properties, estimation and application, Transportation Research.2000, 34B(2): 75~89
14 Daganzo,C.F.Multinomial Probit: The Theory and its Applications to DemandForecasting. Academic Press.1979:17~18
15 Domencich,T.A.,Kraft,Geta l. Estimation of urban passenger travel behavior: an economic demand model.H.R.R.1978:64~78
16 By ChristineM. Benedek and Laurence R.Rilet. Equitable Traffic Assignment with Environmental Cost Functions. Journal of Transportation Engineering.1998, 124(1):16~22
17 Korf J.L, M. J. Demetskv. Analysis of Rapid Transit Access Mode Choice. TRR.817, 1981
18 Bates E. G. A study of Passenger Transfer Facilities. TRB. 662.1988
19 Ian S. J. Dlckins. Park and Ride Facilities on Light Rail Transit Systems. Transportation Research.18:23~36, 1991
20马小毅.土地利用性质调整对轨道交通客运量的影响[J].都市快轨交通. 2008. 21(3):2~3
21杜彩军,蒋玉琨.城市轨道交通与其他交通方式衔接规律的探讨[J].都市快轨交通. 2005. 18(3):45~49
22黄杉,关宏志.城市轨道站点衔接客流比例标准确定研究—以北京市为例[J].交通标准化. 2008. 186(2):15~18
23傅搏峰,吴娇蓉,陈小鸿.郊区轨道站点分类方法研究[J].铁道学报. 2008. 20(6):19~23
24吴娇蓉,毕艳祥,傅博峰.基于郊区轨道交通站点分类的客流特征和换乘系统优先级分析[J].城市轨道交通研究. 2007.11:23~28
25岳芳,毛保华,陈团生.城市轨道交通接驳方式的选择[J].都市快轨交通. 2007.20(4):36~39
26殷远飞.城市轨道接驳方式选择研究[D].北京工业大学硕士毕业论文2005
27王文红,关宏志,王山川. Nested-Logit模型在轨道交通衔接方式选择中的应用[J].城市轨道交通研究.2008.7:25~29
28黄杉,关宏志,严海.轨道交通衔接方式选择行为研究—以北京市轨道交通为例[J].土木工程学报. 2009.42(7):126~130
29马小毅,金安,周志华.一体化程度对轨道交通客运量的影响[J].都市快轨交通. 2009. 22(2):43~46
30王树盛,黄卫,陆振波,俞先江.都市圈轨道交通客流预测方法研究[J].城市轨道交通研究. 2004. 1:40~43
31轩宏伟,王花兰,颜月霞.常规公交影响下的轨道交通吸引范围研究[J].铁道运输与经济. 2007. 29(7):61~63
32王秋平,李峰.城市其他客运交通换乘轨道交通协调探讨[J].西安建筑科技大学学报(自然科学版). 2003.35(2):32~36
33刘伟华,刘惠敏,晏启鹏.轨道交通环线站点环内吸引区域研究[J].长沙铁道学院学报.2002.20(4):94~98
34姚丽亚,关宏志.轨道交通线网服务范围研究[J].北京工业大学学报. 2008.34(7):738~742
35黄丽彬.大城市轨道交通站点对地区发展的影响评价研究—以上海徐家汇轨道枢纽为例[D].同济大学硕士学位论文.2006:25~30
36郑文含.不同类型轨道交通站点地区开发强度探讨[J].《2008城市发展与规划国际论坛论文集》2008:93~95
37曹玫.城市轨道交通与常规公交客流一体化研究[D].东南大学硕士学位论文.2005:12~15
38 Klaus Backhaus, Bernd Erichson, Wulff Plinke, Xuyi Wang Rolf Weiber. Multivariate statistical analysis[M](中文版)
39 MOSHE BEN-AKIVA. STEVEN R LERMAN. Discrete Choice Analysis:Theory and Application to Travel Demand[M]. London: The MIT Press Carbridge, 1985
40 Hensher D A, Bradley M. Using stated response data to enrich revealed preference discrete choice models[J]. Marketing Letters. 1993,4(2):139
41 AO En-jian, Morikawa T. A study on nested logit mode choice model for intercity high-speed rail system with combined RP/SP data[J].Traffic and Transportation Studies Proceedings of ICTTS,2002:612~619
42金安. LOGIT模型参数估计方法研究[J].交通运输系统工程与信息. 2004.4(1): 72~74
43覃裔.轨道交通枢纽规划与设计理论研究[D].同济大学硕士论文.2002: 66~67
44郭丽娜.城市轨道交通与土地利用协调发展的研究[D].北京交通大学硕士学位论文. 2006: 33~38
45 Shama S C, Tayebali A, Wemer A.乡村道路的费用效益分析及用户评判闭.运输研究报告第1009号,TRB,华盛顿,D.C. 1985
46李林波,吴兵.交通方式选择中心理因素影响分析[J].山东交通学院学报,2003.11(3): 27~31