基于Lowry模型改进的居民出行产生预测方法研究
|
摘要
居民出行产生作为交通需求的前提和基础,其结果直接影响到以下各阶段预测结果的优劣,进而影响到交通设施规划的合理性,在需求预测中占有举足轻重的作用。而居民出行产生量与区域人口息息相关,在没有历年人口调查数据的基础上如何准确预测人口显得尤为重要。另外,随着人们生活水平的提高,居民出行中的弹性出行比重会越来越大,而影响弹性出行的因素很多,如何在诸多因素中提取主要因素并准确预测弹性出行产生量倍受学者关注。
基于上述观点,本文对Lowry模型进行改进,预测交通小区的人口数和岗位数,并将结果应用于居民出行产生量的预测。
引入人口位势和岗位位势的概念,正确表述Lowry模型中人口和岗位分布函数的实际意义。在应用遗传算法对人口位势和岗位位势标定后,通过多元逐步回归分析确定了各类土地利用与人口位势和岗位位势之间的关系,进而确定了影响人口位势和岗位位势的主要土地利用类型。此时,即根据规划年土地利用便可计算规划年的人口位势和岗位位势,并应用Lowry改进模型计算交通小区的人口数和岗位数。
在着重分析居民出行的目的特性的基础上,对各类出行目的进行了汇总和分类,将出行按目的划分为上班、上学和弹性出行三类。针对弹性出行,引入效用函数概念,用以说明周边小区对本小区弹性出行的影响,最终建立分目的的居民出行产生预测模型。
将Lowry改进模型与居民出行产生预测相结合,进行案例分析。将Lowry改进模型计算得到的各小区人口及岗位数应用于分目的的居民出行产生预测模型,进而得到各交通小区分目的居民出行产生量。
As the precondition and foundation for transportation demand, the result of trip generation affects the following stage and rationality of transportation planning, so trip generation is very important in transportation demand. The amount of trip generation is closely bound up region population, so it is important to forecast region population with out investigated data. On the side, elasticity trip will be more and more along with economy development. There are many factors affect the elasticity trip, how to forecast elasticity trip accurately is attended by many scholar.
Base on the above standpoints, this article forecasts population and employments of trip zones by improving the Lowry model. The result of Lowry model applies to trip generation forecast.
This paper introduces population potential and the employment potential concept to improve the population and the employment distributation function of the Lowry model. Using the genetic algorithm to calibrate the population potential and the employment potential, then, through multi-dimensional gradually regression to analysis the relationship between each kind of land utilization and population position potential and employment potential. In this way to determine the main land utilization type which influence population potential and employment potential. So, according to year land utilization, we can compute population potential and employment potential, and apply them to the Lowry model.
Then, based on emphatically analyzes in the goal characteristic of inhabitant trip, compiles to each kind of trip characteristics are three kinds, namely goes to work, goes to school and elastic trip. Introduction utility function concept, with showed the peripheral elasticity trip influence, finally, and establishes the subtitle inhabitant trip generation forecast model.
With the help of case to unify the Lowry model and the trip generation forecast. Trip zones population and employments obtains from the Lowry model apply in the trip generation forecast model, obtained the quantity of trip generation of different items at last.
基于上述观点,本文对Lowry模型进行改进,预测交通小区的人口数和岗位数,并将结果应用于居民出行产生量的预测。
引入人口位势和岗位位势的概念,正确表述Lowry模型中人口和岗位分布函数的实际意义。在应用遗传算法对人口位势和岗位位势标定后,通过多元逐步回归分析确定了各类土地利用与人口位势和岗位位势之间的关系,进而确定了影响人口位势和岗位位势的主要土地利用类型。此时,即根据规划年土地利用便可计算规划年的人口位势和岗位位势,并应用Lowry改进模型计算交通小区的人口数和岗位数。
在着重分析居民出行的目的特性的基础上,对各类出行目的进行了汇总和分类,将出行按目的划分为上班、上学和弹性出行三类。针对弹性出行,引入效用函数概念,用以说明周边小区对本小区弹性出行的影响,最终建立分目的的居民出行产生预测模型。
将Lowry改进模型与居民出行产生预测相结合,进行案例分析。将Lowry改进模型计算得到的各小区人口及岗位数应用于分目的的居民出行产生预测模型,进而得到各交通小区分目的居民出行产生量。
As the precondition and foundation for transportation demand, the result of trip generation affects the following stage and rationality of transportation planning, so trip generation is very important in transportation demand. The amount of trip generation is closely bound up region population, so it is important to forecast region population with out investigated data. On the side, elasticity trip will be more and more along with economy development. There are many factors affect the elasticity trip, how to forecast elasticity trip accurately is attended by many scholar.
Base on the above standpoints, this article forecasts population and employments of trip zones by improving the Lowry model. The result of Lowry model applies to trip generation forecast.
This paper introduces population potential and the employment potential concept to improve the population and the employment distributation function of the Lowry model. Using the genetic algorithm to calibrate the population potential and the employment potential, then, through multi-dimensional gradually regression to analysis the relationship between each kind of land utilization and population position potential and employment potential. In this way to determine the main land utilization type which influence population potential and employment potential. So, according to year land utilization, we can compute population potential and employment potential, and apply them to the Lowry model.
Then, based on emphatically analyzes in the goal characteristic of inhabitant trip, compiles to each kind of trip characteristics are three kinds, namely goes to work, goes to school and elastic trip. Introduction utility function concept, with showed the peripheral elasticity trip influence, finally, and establishes the subtitle inhabitant trip generation forecast model.
With the help of case to unify the Lowry model and the trip generation forecast. Trip zones population and employments obtains from the Lowry model apply in the trip generation forecast model, obtained the quantity of trip generation of different items at last.
引文
1刘晓明.城市交通出行生成模型的研究.北京工业大学硕士学位论文. 2000:2~5
2温欣明.就业岗位与经济发展关系的实证研究.统计与决策. 2006:85~87
3单连成,高自友.具有弹性需求的城市出行分布和居住土地使用布局的组合模型及求解算法.公路交通科技. 1999.16(4):61~65
4钱林波等.土地利用混合程度与居民出行空间分布.华中科技大学学报. 2000,19(3):23~25
5杨敏,陈学武,王炜等.基于人口和土地利用的城市新区交通生成预测模型.东南大学学报. 2005,35(5):815~819
6 George R.Parsons. A Demand Theory for Numbers of Trips in a Random Utility Model of Recreation. Journal of environmental economics and management 2001:357~367
7 Kwaku Agyemang-Duah. Spatial Transferability of An ordered Response Model of Trip Generation. Transportation Research-A. 2000,31:65~68
8 Michael D. Anderson and Justin P. Olander. Evaluation of Two Trip Genetation Techniques for Small Area Travel Models. Journal of Urban Planning and Develipment. 2002,128(2):77~88
9 Sumeeta Srinivasan. Linking land use and transportation in a rapidly urbanizing context: A study in Delhi, India. Transportation. 2005, (32): 87~104
10 Marlon G. Boarnet and Sharon Sarmiento. Can Land-use Policy Really Affect Travel Behavior? A Study of the Link between Nom-work Travel and Land-use Characteristics. Urban Studies. 1998, 35(7):1155~1169
11 Tae Youn Jang. Count Data Models for Trip Generation. Journal of Transportation Engineering, 2005,131(6):165~169
12 John S. Miller, P.E.; Lester A, Hoel, P.E.; Arkopal K. Goswami. Borrowing Residential Trip Generation Rates. Journal of Transportation Engineering. 2002, 132(2):43~46
13 Thirayoot Limanond and Debbie A. Niemeier. Effect of land use on decisions of shopping tour generation. Transportation. 2004,( 31):153~181,
14 Jonas Eliasson , Lars-G oran Mattsson. A model for integrated analysis of household location and travel choices. Transportation Research Part A. 2000, (34):375~394
15 Thomas F. Golob. A simultaneous model of household activity participation and trip chain generation. Transportation Research Part B. 2000 (34):389~397
16葛亮,王炜,陈学武.结合土地利用再谈城市交通可持续发展.华中科技大学学报. 2002, 19(3):33~36
17石飞,江薇等.基于土地利用形态的交通生成预测理论方法研究.土木工程学报. 2005, (3):115~124
18杨明,曲大义等.城市土地利用与交通需求相关关系模型研究.公路交通科技. 2002, (2):72~75
19王丽,刘小明,任福田.城市土地开发交通生成预测方法的探讨.北京工业大学学报. 2004, (3):97~100
20刘安等.出行生成预测模型的研究.同济大学学报. 1998, 26(3):290~293
21刘灿齐.现代交通规划学.人民交通出版社. 2001:63~66
22陆化普,王建伟,袁虹.基于交通效率的大城市合理土地利用形态研究.公路交通科技. 2005,18(7):65~68
23郭士梅等.城市规划中人口规模预测方法评析.人口统计. 2005,(1):6~9
24周瑞平. GM(1,1)模型灰色预测法预测城市人口规模.内蒙古师范大学学报. 2005,34(1):81~83
25杨希锐,王殿海,张瑞林.居民居住选址和出行生成模型.吉林工业大学学报. 1998:12~15
26丁成日.城市规划与空间结构:城市可持续发展战略.建筑工业出版社. 2005:135~146
27于勇,姜兴渭.基于遗传算法的组合预测方法研究.高科技通讯. 2001:75~77
28周明.遗传算法原理及应用.国防工业出版社. 1999:18~31
29高峰,荣冈.遗传算法参数规则及其在模型参数辨识中的应用分析.机电工程. 2003,20(3):31~33
30黎钧棋,石国桢.遗传算法交叉率与变异率关系的研究.武汉理工大学学报. 2003,27(1):97~99
31王圆圆.西安市出行生成问题研究.长安大学硕士学位论文. 2004:35~38
32 FHA. Nationwide Personal Transportantion Study. US Department of Transportation. 1972:39~40
33 CHP. 1977 Nationwide Personal Transportation Study. US Department of Transportation. 1977:72~73
34 CDT. California Travel Study. California Department of Transportation. 1991:19~20
35 FHA. 2001 Nationwide Personal Transportantion Study. US Department of Transportation. 2003:35~36
36 George R.Parsons. A Demand Theory for Numbers of Trips in a Random Utility Model of Recreation. Journal of environmental economics and management 2001:357~367
37 BYUNC-DO KIM, KYUNCDO PARK. Studying Patterns of Consumer’s Grocery Shopping Trip. Journal of Retailing. 2002, 73(4):501~517
38周鹤龙.美国大都市区交通规划及其启示.国外城市规划. 2002,(5):50~52
39 Daniel A. Badoe and Chin-cheng chen. Unit of analysis in conventional trip generation modeling: an investigation. NRC Research. 2004,(3):272~280
40 L.James French; Ronald W.Eck; Menber. Trip Generation Rates of Correctional Facilities. Journal of Urban Planning and Development. 2000, 126(1):354~361
41李民.基于活动链的居民出行行为分析.吉林大学硕士学位论文. 2004:32~38
42周素红,杨利军.城市开发强度影响下的城市交通.城市规划学刊. 2005,(2):75~80
43何刚.城市交通规划交通需求预测模型的研究.合肥工业大学硕士学位论文. 2003:29~34
44 Marlon Boarnet and Randall Crane. The influence of land use on travel behavior:specification and estimation strategies. Transportation Research Part A. 2005 (35):823~845
45 Sriram Krishnamurthy and Kara Maria Kockelman. Propagation of Uncertainty in Transportation-Land Use Models: An Investigation of DRAM-EMPAL and UTPP Predictions in Austin, Texas. TRB . 2003:657~662
46毛海虓.中国城市居民出行特征研究.北京工业大学博士学位论文. 2005:17~24
47 F. Tillema, K.M. van Zuilekom and M.F.A.M van Maarseveen. Trip generation: comparison of neural networks and regression models. Urban Transport X, 2004:82~86
48 Michael J. Clay and Robert A. Johnston. Multivariate uncertainty analysis of an integrated land use and transportation model: MEPLAN. Transportation Research Part D 11. 2006:191~203
49 BYUNC-DO KIM, KYUNCDO PARK. Studying Patterns of Consumer’s Grocery Shopping Trip. Journal of Retailing. 2001,73(4):501~517
50张尧庭,陈慧玉.效用函数及优化.科学出版社. 2000:38~62
2温欣明.就业岗位与经济发展关系的实证研究.统计与决策. 2006:85~87
3单连成,高自友.具有弹性需求的城市出行分布和居住土地使用布局的组合模型及求解算法.公路交通科技. 1999.16(4):61~65
4钱林波等.土地利用混合程度与居民出行空间分布.华中科技大学学报. 2000,19(3):23~25
5杨敏,陈学武,王炜等.基于人口和土地利用的城市新区交通生成预测模型.东南大学学报. 2005,35(5):815~819
6 George R.Parsons. A Demand Theory for Numbers of Trips in a Random Utility Model of Recreation. Journal of environmental economics and management 2001:357~367
7 Kwaku Agyemang-Duah. Spatial Transferability of An ordered Response Model of Trip Generation. Transportation Research-A. 2000,31:65~68
8 Michael D. Anderson and Justin P. Olander. Evaluation of Two Trip Genetation Techniques for Small Area Travel Models. Journal of Urban Planning and Develipment. 2002,128(2):77~88
9 Sumeeta Srinivasan. Linking land use and transportation in a rapidly urbanizing context: A study in Delhi, India. Transportation. 2005, (32): 87~104
10 Marlon G. Boarnet and Sharon Sarmiento. Can Land-use Policy Really Affect Travel Behavior? A Study of the Link between Nom-work Travel and Land-use Characteristics. Urban Studies. 1998, 35(7):1155~1169
11 Tae Youn Jang. Count Data Models for Trip Generation. Journal of Transportation Engineering, 2005,131(6):165~169
12 John S. Miller, P.E.; Lester A, Hoel, P.E.; Arkopal K. Goswami. Borrowing Residential Trip Generation Rates. Journal of Transportation Engineering. 2002, 132(2):43~46
13 Thirayoot Limanond and Debbie A. Niemeier. Effect of land use on decisions of shopping tour generation. Transportation. 2004,( 31):153~181,
14 Jonas Eliasson , Lars-G oran Mattsson. A model for integrated analysis of household location and travel choices. Transportation Research Part A. 2000, (34):375~394
15 Thomas F. Golob. A simultaneous model of household activity participation and trip chain generation. Transportation Research Part B. 2000 (34):389~397
16葛亮,王炜,陈学武.结合土地利用再谈城市交通可持续发展.华中科技大学学报. 2002, 19(3):33~36
17石飞,江薇等.基于土地利用形态的交通生成预测理论方法研究.土木工程学报. 2005, (3):115~124
18杨明,曲大义等.城市土地利用与交通需求相关关系模型研究.公路交通科技. 2002, (2):72~75
19王丽,刘小明,任福田.城市土地开发交通生成预测方法的探讨.北京工业大学学报. 2004, (3):97~100
20刘安等.出行生成预测模型的研究.同济大学学报. 1998, 26(3):290~293
21刘灿齐.现代交通规划学.人民交通出版社. 2001:63~66
22陆化普,王建伟,袁虹.基于交通效率的大城市合理土地利用形态研究.公路交通科技. 2005,18(7):65~68
23郭士梅等.城市规划中人口规模预测方法评析.人口统计. 2005,(1):6~9
24周瑞平. GM(1,1)模型灰色预测法预测城市人口规模.内蒙古师范大学学报. 2005,34(1):81~83
25杨希锐,王殿海,张瑞林.居民居住选址和出行生成模型.吉林工业大学学报. 1998:12~15
26丁成日.城市规划与空间结构:城市可持续发展战略.建筑工业出版社. 2005:135~146
27于勇,姜兴渭.基于遗传算法的组合预测方法研究.高科技通讯. 2001:75~77
28周明.遗传算法原理及应用.国防工业出版社. 1999:18~31
29高峰,荣冈.遗传算法参数规则及其在模型参数辨识中的应用分析.机电工程. 2003,20(3):31~33
30黎钧棋,石国桢.遗传算法交叉率与变异率关系的研究.武汉理工大学学报. 2003,27(1):97~99
31王圆圆.西安市出行生成问题研究.长安大学硕士学位论文. 2004:35~38
32 FHA. Nationwide Personal Transportantion Study. US Department of Transportation. 1972:39~40
33 CHP. 1977 Nationwide Personal Transportation Study. US Department of Transportation. 1977:72~73
34 CDT. California Travel Study. California Department of Transportation. 1991:19~20
35 FHA. 2001 Nationwide Personal Transportantion Study. US Department of Transportation. 2003:35~36
36 George R.Parsons. A Demand Theory for Numbers of Trips in a Random Utility Model of Recreation. Journal of environmental economics and management 2001:357~367
37 BYUNC-DO KIM, KYUNCDO PARK. Studying Patterns of Consumer’s Grocery Shopping Trip. Journal of Retailing. 2002, 73(4):501~517
38周鹤龙.美国大都市区交通规划及其启示.国外城市规划. 2002,(5):50~52
39 Daniel A. Badoe and Chin-cheng chen. Unit of analysis in conventional trip generation modeling: an investigation. NRC Research. 2004,(3):272~280
40 L.James French; Ronald W.Eck; Menber. Trip Generation Rates of Correctional Facilities. Journal of Urban Planning and Development. 2000, 126(1):354~361
41李民.基于活动链的居民出行行为分析.吉林大学硕士学位论文. 2004:32~38
42周素红,杨利军.城市开发强度影响下的城市交通.城市规划学刊. 2005,(2):75~80
43何刚.城市交通规划交通需求预测模型的研究.合肥工业大学硕士学位论文. 2003:29~34
44 Marlon Boarnet and Randall Crane. The influence of land use on travel behavior:specification and estimation strategies. Transportation Research Part A. 2005 (35):823~845
45 Sriram Krishnamurthy and Kara Maria Kockelman. Propagation of Uncertainty in Transportation-Land Use Models: An Investigation of DRAM-EMPAL and UTPP Predictions in Austin, Texas. TRB . 2003:657~662
46毛海虓.中国城市居民出行特征研究.北京工业大学博士学位论文. 2005:17~24
47 F. Tillema, K.M. van Zuilekom and M.F.A.M van Maarseveen. Trip generation: comparison of neural networks and regression models. Urban Transport X, 2004:82~86
48 Michael J. Clay and Robert A. Johnston. Multivariate uncertainty analysis of an integrated land use and transportation model: MEPLAN. Transportation Research Part D 11. 2006:191~203
49 BYUNC-DO KIM, KYUNCDO PARK. Studying Patterns of Consumer’s Grocery Shopping Trip. Journal of Retailing. 2001,73(4):501~517
50张尧庭,陈慧玉.效用函数及优化.科学出版社. 2000:38~62