CN109978224A - A method of analysis obtains the Trip Generation Rate of heterogeneity building - Google Patents

A method of analysis obtains the Trip Generation Rate of heterogeneity building Download PDF

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CN109978224A
CN109978224A CN201910031098.XA CN201910031098A CN109978224A CN 109978224 A CN109978224 A CN 109978224A CN 201910031098 A CN201910031098 A CN 201910031098A CN 109978224 A CN109978224 A CN 109978224A
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石飞
朱乐
陆振波
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Abstract

本发明公开了一种分析获取不同性质建筑的交通出行率的方法,包括步骤:首先,处理手机信令数据获取出行量,统计研究区内的土地调查数据,并将出行量与不同性质建筑的建筑总面积保存为列表形式。其次,根据研究目的确定研究时间、不同出行目的、不同建筑类型的交通出行率。然后,具体通过统计不同交通小区不同性质建筑的建筑总面积和基于手机信令数据获得的交通出行量,根据通勤人口密度与交通可达性将不同的交通小区进行聚类分析,最终针对每一类交通小区进行多元统计回归分析,确定不同特征、不同建筑类型的交通出行率。本发明实现了不同性质建筑的交通出行率获取,并以此预测出规划年的交通出行情况。

The invention discloses a method for analyzing and obtaining the traffic travel rate of buildings of different natures, comprising the steps of: first, processing mobile phone signaling data to obtain travel volume, making statistics of land survey data in a research area, and comparing the travel volume with the traffic travel rate of buildings of different natures. The gross floor area is saved as a list. Secondly, according to the research purpose, determine the research time, different travel purposes, and the traffic travel rate of different building types. Then, by calculating the total building area of buildings of different natures in different traffic areas and the traffic trip volume obtained based on mobile phone signaling data, the different traffic areas are clustered according to the commuter population density and traffic accessibility, and finally for each traffic area. A multivariate statistical regression analysis was carried out on the traffic-like districts to determine the traffic travel rate of different characteristics and different building types. The invention realizes the acquisition of the traffic travel rate of buildings with different properties, and predicts the traffic travel situation in the planning year based on this.

Description

一种分析获取不同性质建筑的交通出行率的方法A method to analyze and obtain the traffic travel rate of buildings with different properties

技术领域technical field

本发明涉及交通规划出行调查技术领域,特别是一种交通出行率的获取方法。The invention relates to the technical field of traffic planning travel survey, in particular to a method for obtaining a traffic travel rate.

背景技术Background technique

交通规划不能脱离城市的土地利用规划,这就要求在交通规划过程中有科学的理论体系作依据。以土地利用形态作为出行预测的基础,使规划满足未来的交通需求。城市交通与城市土地利用间的互动关系决定了不同土地利用布局形态和强度会产生不同类型和强度的社会活动,从而决定不同区域的交通集散量和分布状况。相应地,交通系统功能效率的高低也直接影响周边地价、地租和人气,影响周边土地功能的实现充分与否。因此,在进行交通规划中需要深入研究城市土地利用与交通的相互关系,交通出行率是直观反映这种相互关系的重要指标之一。Traffic planning cannot be separated from urban land use planning, which requires a scientific theoretical system as a basis in the process of traffic planning. Using land use patterns as the basis for travel forecasting enables planning to meet future traffic demands. The interactive relationship between urban traffic and urban land use determines that different types and intensities of land use layouts will produce different types and intensities of social activities, thus determining the volume and distribution of traffic in different regions. Correspondingly, the functional efficiency of the transportation system also directly affects the surrounding land price, land rent and popularity, and affects whether the realization of the surrounding land functions is sufficient or not. Therefore, it is necessary to deeply study the relationship between urban land use and traffic in traffic planning, and the traffic trip rate is one of the important indicators to directly reflect this relationship.

交通小区称为OD区(OD节点),通过交通小区划分,将交通流划分为若干OD点之间的交通流。OD节点的划分.实际上是一个模糊聚类的过程,需要采用模糊聚类的方法,将交通流按照一定的隶属度,划分到具体的交通小区中。但目前在实际工作中,由于统计资料收集困难等多方面原因,一般采用省(自治区)、市、县(乡、区)交通枢纽中心等行政区为单位或以高速公路进出口作为交通小区,对于具体公路建设项目,根据可行性研究的深度等要求,确定具体的交通小区划分。如一条国道,可以将县、市作为交通小区,一条省级公路,可以将市、县、乡(镇)作为交通小区;一条县级公路,可以将县、乡(镇)、村作为交通小区。同时,在划分交通小区时,还要将经济技术开发区、新技术产业区、旅游区、重要矿山或大型单位所在地、重要口岸或中转集散点等考虑在内。The traffic cells are called OD zones (OD nodes), and the traffic flow is divided into traffic flows between several OD points through the division of the traffic cells. The division of OD nodes is actually a process of fuzzy clustering. It is necessary to adopt the method of fuzzy clustering to divide the traffic flow into specific traffic cells according to a certain degree of membership. However, in actual work, due to various reasons such as the difficulty in collecting statistical data, administrative districts such as provincial (autonomous region), city, county (township, district) transportation hub centers are generally used as units, or highway entrances and exits are used as transportation districts. For specific highway construction projects, according to the requirements of the depth of the feasibility study, determine the specific traffic area division. For example, for a national highway, counties and cities can be used as traffic areas; for a provincial highway, cities, counties, and townships (towns) can be used as traffic areas; for a county-level road, counties, townships (towns) and villages can be used as traffic areas. . At the same time, when dividing traffic areas, economic and technological development zones, new technology industrial zones, tourist areas, the location of important mines or large units, and important ports or transit distribution points should also be taken into account.

出行率模型是描述每一种土地利用出行生成量变化的决定指标和其出行生成量之间的关系,描述研究对象自身属性与交通生成量之间的量化规律。同时,随着信息化时代的到来,上班和上学时间制度的改变,出行生成量还会发生变化。这就要求交通规划者们应紧密结合社会发展,密切关注影响出行产生的相关制度因素,及时修正生成预测模型,使其更加贴近于实际。所以,解决城市交通问题,离不开对生活在城市中的人的交通行为即居民出行特征的研究。大数据已经在各个领域均有一定的发展,那么对于传统交通规划中的出行率预测也提出了新的机遇与挑战。The trip rate model is to describe the relationship between the determinant index of the change of the trip generation amount of each land use and its trip generation amount, and to describe the quantitative law between the research object's own attributes and the traffic generation amount. At the same time, with the advent of the information age, the time system for work and school changes, and the amount of travel generation will also change. This requires that traffic planners should closely integrate social development, pay close attention to the relevant institutional factors that affect travel, and timely revise the generated prediction model to make it closer to reality. Therefore, to solve the urban traffic problem, it is inseparable from the research on the traffic behavior of the people living in the city, that is, the travel characteristics of the residents. Big data has developed in various fields, so it also brings new opportunities and challenges to the prediction of travel rate in traditional transportation planning.

现有方法中通过居民调查数据来进行出行量预测,由于居民出行调查抽样率低、成本高等特点使得出行数据样本量小、研究精度低,通过居民出行调查数据很难回归出精度高的出行率,同时由于不同用地类型的出行率不同,调查数据很难大量覆盖到不同用地,因此传统的出行率调查方法有其明显的局限性。例如,商业建筑的出行率在调查抽样中仅能覆盖城市中极少的商业建筑,因此得出的出行率不能体现实际情况,对于未来年的出行量预测精度有着明显的限制。简而言之,通过居民调查数据来进行出行量估计精度不高。In the existing method, the travel volume is predicted through the data of the residents' travel survey. Due to the low sampling rate and high cost of the residents' travel survey, the sample size of the travel data is small and the research accuracy is low. It is difficult to return the travel rate with high accuracy through the residents' travel survey data At the same time, due to the different travel rates of different land types, it is difficult for the survey data to cover a large number of different land uses, so the traditional travel rate survey method has its obvious limitations. For example, the travel rate of commercial buildings can only cover very few commercial buildings in the city in the survey sampling, so the obtained travel rate cannot reflect the actual situation, which has obvious restrictions on the accuracy of the travel volume forecast in the future years. In short, the accuracy of travel estimates based on resident survey data is not high.

发明内容SUMMARY OF THE INVENTION

本发明所要解决的技术问题是克服现有技术的不足,而提供一种基于手机信令数据和土地调查数据来获取不同性质建筑的交通出行率的方法,本发明能够比较精确地计算不同用地的交通出行率,并规划年的土地调查数据进行出行量预测。The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art, and to provide a method for obtaining the traffic travel rate of buildings of different properties based on mobile phone signaling data and land survey data. Traffic trip rates and planning year land survey data for trip volume forecasts.

本发明为解决上述技术问题采用以下技术方案:The present invention adopts the following technical solutions for solving the above-mentioned technical problems:

本发明提出一种分析获取不同性质建筑的交通出行率的方法,包括以下步骤:The present invention proposes a method for analyzing and obtaining the traffic travel rate of buildings of different properties, comprising the following steps:

步骤1),统计不同交通小区不同用地类型的建筑总面积;Step 1), count the total building area of different land use types in different traffic districts;

步骤2),基于手机信令数据获取每个交通小区的交通出行量;Step 2), obtain the traffic travel volume of each traffic cell based on mobile phone signaling data;

步骤3),根据通勤人口密度与交通可达性将不同的交通小区进行聚类分析;Step 3), according to the commuter population density and traffic accessibility, perform cluster analysis on different traffic communities;

步骤4),根据步骤3)的分析结果,针对每一类交通小区进行多元统计回归分析,确定不同特征、不同居住建筑类型的交通出行率。Step 4), according to the analysis result of step 3), perform multivariate statistical regression analysis for each type of traffic community, and determine the traffic travel rate of different characteristics and different residential building types.

进一步的,本发明所提出的分析获取不同性质建筑的交通出行率的方法,所述步骤1)中统计不同交通小区不同用地类型的建筑总面积,具体如下:Further, in the method for analyzing and obtaining the traffic travel rate of buildings of different natures proposed by the present invention, in the step 1), the total building area of different traffic districts and different land types is counted, and the details are as follows:

步骤1.1),确定建筑分类标准;Step 1.1), determine the building classification standard;

步骤1.2),根据建筑分类标准,将研究区范围内用地按交通小区的边界进行划分,得到各交通小区的用地图层;Step 1.2), according to the building classification standard, divide the land in the study area according to the boundary of the traffic area, and obtain the map layer of each traffic area;

步骤1.3),打开步骤1.2)中的用地图层属性表,依次添加文本字段,命名编号,输入对应交通小区编号,将所有交通小区图层合并为一个图层;Step 1.3), open the attribute table of the map layer in step 1.2), add text fields in turn, name the number, enter the corresponding traffic area number, and combine all traffic area layers into one layer;

步骤1.4),以交通小区边界为统计单元获取各类建筑总面积。In step 1.4), the total area of various buildings is obtained by taking the boundary of the traffic area as the statistical unit.

进一步的,本发明所提出的分析获取不同性质建筑的交通出行率的方法,所述步骤2)中基于手机信令数据获得交通出行量,具体如下:Further, in the method for analyzing and obtaining the traffic travel rate of buildings of different properties proposed by the present invention, in the step 2), the traffic travel amount is obtained based on the mobile phone signaling data, and the details are as follows:

步骤2.1),在ARCGIS中依据基站的服务范围创建泰森多边形,将基站的出行数据赋予泰森多边形,打开泰森多边形的属性列表,根据空间关系进行关联;Step 2.1), create a Thiessen polygon in ARCGIS according to the service range of the base station, assign the trip data of the base station to the Thiessen polygon, open the attribute list of the Thiessen polygon, and associate according to the spatial relationship;

步骤2.2),获得出行人口数据后,用每个泰森多边形的出行人口除以每个泰森多边形的面积,得出出行人口密度;Step 2.2), after obtaining the travel population data, divide the travel population of each Thiessen polygon by the area of each Thiessen polygon to obtain the travel population density;

步骤2.3),将交通小区与基站的泰森多边形进行叠置分析INTERSECT,并将生成的叠置的结果进行计算,打开属性表,添加双精度字段,使用字段计算器计算出行人数;Step 2.3), carry out the superposition analysis INTERSECT of the Thiessen polygon of the traffic cell and the base station, and calculate the result of the superposition generated, open the attribute table, add a double field, and use the field calculator to calculate the number of travelers;

步骤2.4),最后进行空间统计,得到每个交通小区的出行量。Step 2.4), and finally perform spatial statistics to obtain the travel volume of each traffic area.

进一步的,本发明所提出的分析获取不同性质建筑的交通出行率的方法,所述步骤3)中根据通勤人口密度与交通可达性将不同的交通小区进行聚类分析,具体如下:Further, in the method for analyzing and obtaining the traffic travel rate of buildings with different properties proposed by the present invention, in the step 3), different traffic cells are clustered according to the commuter population density and traffic accessibility, and the details are as follows:

步骤3.1),根据研究目的选取分类因子:将城区各个交通小区的交通可达性、通勤人口密度确定为分类因子;Step 3.1), select the classification factor according to the research purpose: determine the traffic accessibility and commuter population density of each traffic district in the urban area as the classification factor;

步骤3.2),确定聚类方法:根据步骤3.1)确定的分类因子进行K-Means聚类,最终确定分类为二,即交通可达性高且通勤密度高、交通可达性差且通勤密度差的交通小区。Step 3.2), determine the clustering method: perform K-Means clustering according to the classification factor determined in step 3.1), and finally determine the classification into two, that is, those with high traffic accessibility and high commuting density, and poor traffic accessibility and poor commuting density. traffic area.

进一步的,本发明所提出的分析获取不同性质建筑的交通出行率的方法,所述步骤4)具体如下:Further, the method for analyzing and obtaining the traffic travel rate of buildings with different properties proposed by the present invention, the step 4) is as follows:

根据聚类分析的结果,将交通小区划分为几类,分别根据统计结果进行多元统计回归得出交通生成率;具体为:According to the results of cluster analysis, the traffic districts are divided into several categories, and the traffic generation rate is obtained by performing multivariate statistical regression according to the statistical results respectively; the details are as follows:

(1)针对每一个交通小区构建一个多元线性方程,形成多个方程进行联立求解,(1) Construct a multivariate linear equation for each traffic area, and form multiple equations to solve simultaneously,

Y1=β1x12x23x3+…+βnxn+b1 Y 11 x 12 x 23 x 3 +…+β n x n +b 1

Y2=β1x12x23x3+…+βnxn+b2 Y 21 x 12 x 23 x 3 +…+β n x n +b 2

……...

Yn=β1x12x23x3+…+βnxn+bn Y n1 x 12 x 23 x 3 +…+β n x n +b n

式中,Yn:每个交通小区的交通出行量;βn:每种用地类型的交通出行率;Xn:每种用地类型的建筑总面积;bn:常数项;n代表交通小区的数量;In the formula, Y n : the traffic trip volume of each traffic area; β n : the traffic trip rate of each land use type; X n : the total building area of each land use type; b n : constant term; n represents the traffic area of the traffic area. quantity;

(2)根据控规单元对于交通小区进行重分组,对每个控规单元进行出行量的统计,具体计算公式如下:(2) Regroup the traffic cells according to the control and regulation unit, and count the travel volume of each control and regulation unit. The specific calculation formula is as follows:

式中:where:

Bi:控规单元i的出行量;dik:控规单元i的第k类建筑面积;wik:控规单元i每个第k类建筑的出行率;B i : the trip volume of the control unit i; d ik : the building area of the kth class of the control unit i; w ik : the trip rate of each k-th building of the control unit i;

(3)最终得出每种用地类型的交通出行率。(3) Finally, the traffic travel rate of each land use type is obtained.

本发明采用以上技术方案与现有技术相比,具有以下技术效果:Compared with the prior art, the present invention adopts the above technical scheme, and has the following technical effects:

(1)首先,本发明基于手机基站服务范围与交通小区范围统计出行量。通过该方法,可以反映出每个交通小区每天各个时段的出行量,进而分析出各个交通小区的出行特征,为交通规划、城市规划编制等工作提供有效的决策支撑,由于可以对全国各个城市进行分析,因此本发明方法的适用性更强。(1) First, the present invention counts the travel volume based on the service range of the mobile phone base station and the traffic cell range. Through this method, the travel volume of each traffic area at each time of day can be reflected, and then the travel characteristics of each traffic area can be analyzed, which can provide effective decision support for traffic planning, urban planning and other work. Therefore, the applicability of the method of the present invention is stronger.

(2)其次,本发明可以按照不同交通小区特征进行分类出行率计算的方法,并判断出不同交通小区的具体特征,这为长期的交通出行预测提供了基础,也有利于快速计算出各种交通小区出行量的变化情况;同时,此方案获取不同交通小区的出行率,提高了交通出行率的精度,对于准确预测出行量提供了技术保障。(2) Secondly, the present invention can classify the travel rate calculation method according to the characteristics of different traffic cells, and determine the specific characteristics of different traffic cells, which provides a basis for long-term traffic travel prediction, and is also conducive to quickly calculating various At the same time, this scheme obtains the travel rate of different traffic areas, improves the accuracy of the traffic travel rate, and provides a technical guarantee for accurately predicting the travel amount.

(3)再者,本发明对于城市交通中的高峰时段可以进行精确处理。由于手机数据的时间属性的精度高,可以精确到秒,因此运用大数据判断城市出行特征后,具体针对每座城市进行出行高峰的判断,然后对高峰时段下的出行率进行精确计算,对于城市拥堵提供了有效的判断依据。(3) Furthermore, the present invention can accurately handle the peak hours in urban traffic. Due to the high precision of the time attribute of mobile phone data, it can be accurate to the second. Therefore, after using big data to determine the characteristics of urban travel, we can determine the travel peak for each city, and then accurately calculate the travel rate during peak hours. Congestion provides an effective basis for judgment.

(4)最后,本发明样本量巨大,较传统基于个体的居民调查数据获取小样本来说,可以较为全面的反映城市居民的出行特征,同时可以获取基于不同建筑类型的交通出行率,对于城市规划的用地布局与交通规划的流量预测均有着重要作用。(4) Finally, the sample size of the present invention is huge. Compared with the small sample obtained from the traditional individual-based resident survey data, it can more comprehensively reflect the travel characteristics of urban residents, and at the same time, it can obtain the traffic travel rate based on different building types, which is very useful for urban planning. Both the land use layout and the traffic planning and traffic forecasting play an important role.

附图说明Description of drawings

图1是本发明所提出的多元统计回归统计方法示意图。FIG. 1 is a schematic diagram of the multivariate statistical regression statistical method proposed by the present invention.

图2是本发明的整体流程示意图。FIG. 2 is a schematic diagram of the overall flow of the present invention.

图3是统计交通小区不同用地类型的建筑总面积示意图。Figure 3 is a schematic diagram of the total building area of different land use types of the traffic area.

图4基于手机信令数据获取出行量示意图。FIG. 4 is a schematic diagram of obtaining travel volume based on mobile phone signaling data.

图5是交通小区出行量含义示意图。Figure 5 is a schematic diagram of the meaning of the trip volume in a traffic area.

具体实施方式Detailed ways

下面结合附图对本发明的技术方案做进一步的详细说明:Below in conjunction with accompanying drawing, the technical scheme of the present invention is described in further detail:

由于居民调查数据样本量少,但基于手机信令数据的出行数据样本量大,可以机器学习进行数据清洗,并最终得出出行量的空间分布。因此,用手机信令数据与土地调查数据进行不同用地的出行率计算将是一种切实可行的方法。本发明重点就是通过手机信令数据和土地调查数据建立多元统计回归模型,来进行相关的分析。Due to the small sample size of the resident survey data, but the large sample size of travel data based on mobile phone signaling data, machine learning can be used to clean the data, and finally the spatial distribution of the travel amount can be obtained. Therefore, it will be a practical method to use the mobile phone signaling data and land survey data to calculate the travel rate of different land use. The key point of the present invention is to establish a multivariate statistical regression model through mobile phone signaling data and land survey data to carry out relevant analysis.

通过手机服务商公司获取手机信令数据。经过仔细分析并对手机服务商进行调研咨询,得出手机信令数据具有以下三个特点:Obtain mobile phone signaling data through the mobile phone service provider company. After careful analysis and research and consultation of mobile phone service providers, it is concluded that mobile phone signaling data has the following three characteristics:

①被动式、覆盖广、非随机。手机信令数据是运营商记录的手机用户在网络活动时的位置信息,属于非自愿被动式采集数据,当手机发生开机、关机、主叫、被叫、收发短信、切换基站、移动交换中心或位置更新时,手机识别号、信令时间、当时所处的小区基站编号均保存在手机信令数据中。①Passive, wide coverage, non-random. Mobile phone signaling data is the location information of mobile phone users during network activities recorded by operators. It belongs to involuntary passive collection of data. When updating, the mobile phone identification number, signaling time, and cell base station number at that time are all stored in the mobile phone signaling data.

②时效性、动态性、连续性。手机信令数据记录了每一个用户的日常行为和对城市空间的使用方式,可反映用户时空行为活动规律的特征,实现实时动态连续追踪与可视化表达,为描述城市居住、就业、游憩、交通等活动的时空动态特征提供了新的途径。②Timeliness, dynamics and continuity. The mobile phone signaling data records the daily behavior of each user and the way they use the urban space, which can reflect the characteristics of the user's time and space behavior and activities, and realize real-time dynamic continuous tracking and visual expression. The spatiotemporal dynamics of activities provide new avenues.

③反映城市内外的功能联系。在区域层面,从跨城市出行的居民时空轨迹数据中可以识别出常住地、出行目的地,通过测算城市之间的人流联系来反映城市之间的功能联系。在城市内部层面,从个体的时空轨迹中可以识别居住地、工作地、游憩地之间的联系。③ Reflect the functional connection inside and outside the city. At the regional level, resident places and travel destinations can be identified from the spatiotemporal trajectory data of residents traveling across cities, and the functional connections between cities can be reflected by measuring the flow of people between cities. At the inner city level, the connections between places of residence, places of work, and places of recreation can be identified from the temporal and spatial trajectories of individuals.

从手机信令数据的第②个特点,可以发现:确定每个人的出行特征是一个关键点。同时,只有人们切换服务的基站才会被记录一次出行。因此,根据手机信令数据的特点,首先需要从海量数据中筛选出实际的交通出行量,再查每个人的具体出行特征。From the second characteristic of mobile phone signaling data, it can be found that determining the travel characteristics of each person is a key point. At the same time, only the base station where people switch services will be recorded as a trip. Therefore, according to the characteristics of mobile phone signaling data, it is first necessary to screen out the actual traffic travel volume from the massive data, and then check the specific travel characteristics of each person.

接下来,建立基于不同建筑类型的交通出行量模型来研究具体的出行特征:参见附图1,图中:A:公共管理与公共服务建筑面积,B:商业服务业设施建筑面积,G:绿地与广场建筑面积,M:工业建筑面积,R:居住建筑面积,S:道路与交通设施建筑面积,U:公用设施建筑面积,W:物流仓储建筑面积,H:城乡建设建筑面积,将交通出行率用βn表示,即每种用地的出行率。计算不同用地出行率的算法核心是:基于手机信令数据的第②和③特点,一次有效的出行会有两次记录,可以查询到两次出行时间,分别记录每个人全天的出行特征。那么,确定不同建筑类型的出行率规律,需要结合不同类型建筑的建筑面积Xn进行分析,不同类型建筑的建筑面积通过规划局土地调查获得,以交通出行率βn、不同类型建筑的建筑面积Xn、不同统计单元的出行量Yn进行多元统计回归。Next, establish a traffic travel volume model based on different building types to study specific travel characteristics: see Figure 1, in the figure: A: building area of public management and public services, B: building area of commercial service facilities, G: green space and square construction area, M: industrial construction area, R: residential construction area, S: construction area of roads and transportation facilities, U: construction area of public facilities, W: construction area of logistics and storage, H: construction area of urban and rural construction, traffic travel The rate is denoted by β n , which is the travel rate of each land use. The core of the algorithm for calculating the travel rate of different land use is: based on the characteristics of the second and third characteristics of the mobile phone signaling data, an effective trip will be recorded twice, and the travel time of the two trips can be queried, and the travel characteristics of each person throughout the day can be recorded separately. Then, to determine the travel rate rules of different building types, it is necessary to analyze the building area X n of different types of buildings. The building areas of different types of buildings are obtained through the land survey of the Planning Bureau. Multivariate statistical regression is performed on X n , and the travel volume Y n of different statistical units.

需要指出的是:上述交通出行率βn在不同城市、不同时段、不同建筑类型中均会有不同,需要具体针对每座城市进行具体分析,并通过长期数据观察推断出的趋势性结论,且选取的观察时间段具有一般性,即排除了节假日等因素对客流出行的干扰。由于这是一种趋势性判断,所得的某些结论具有概率判断的特点,尽管这不是一种绝对判断,但可以为相关研究者提供思路参考与决策深入研究,这也与大数据直接结论一般以趋势性和表象结论为主的特点相一致。It should be pointed out that: the above-mentioned traffic travel rate β n will be different in different cities, different time periods, and different building types. It is necessary to carry out specific analysis for each city, and to infer the trend conclusions through long-term data observation, and The selected observation time period is general, that is, the interference of factors such as holidays and other factors on passenger flow is excluded. Since this is a trend judgment, some conclusions obtained have the characteristics of probabilistic judgment. Although this is not an absolute judgment, it can provide relevant researchers with reference for ideas and in-depth research on decision-making, which is also similar to the direct conclusions of big data. The characteristics of trend and appearance conclusion are consistent.

综上所述,给出交通出行率βn具体含义:若βn值越大则单位面积交通出行量就越大。To sum up, the specific meaning of the traffic trip rate β n is given: the larger the value of β n , the greater the traffic trip volume per unit area.

参考图2所示,本发明的方法流程具体如下:Referring to Fig. 2, the method flow of the present invention is as follows:

步骤1)、参见附图3,统计不同交通小区不同用地类型的建筑总面积,例如选择昆山中心城区作为研究范围,具体统计每种建筑类型的建筑面积。Step 1), referring to Figure 3, count the total building area of different land use types in different traffic districts, for example, select the central urban area of Kunshan as the research scope, and specifically count the building area of each building type.

其中,统计不同交通小区不同用地类型的建筑总面积,具体如下:Among them, the total building area of different land use types in different traffic districts is calculated as follows:

步骤1.1),确定建筑分类标准:行政办公建筑、商业建筑、居住建筑、物流仓储建筑、工业建筑、交通设施建筑、公用设施建筑、绿地广场配套建筑。Step 1.1), determine the building classification standards: administrative office buildings, commercial buildings, residential buildings, logistics and storage buildings, industrial buildings, transportation facilities buildings, public facilities buildings, green space square supporting buildings.

步骤1.2),查询在步骤1.1)将研究区范围内用地按交通小区的边界进行划分SPLIT(分割),选取昆山市中心城区为研究范围,确定交通小区边界后进行分割。Step 1.2), query In step 1.1), the land in the study area is divided into SPLIT (segment) according to the boundary of the traffic area, and the central urban area of Kunshan is selected as the research area, and the boundary of the traffic area is determined before segmentation.

步骤1.3),对步骤1.2)中打开属性表,添加文本字段,命名编号,输入对应交通小区编号,将所有交通小区图层合并为一个图层MERGE(合并)。对昆山市中心城区的交通小区从1开始进行编号。Step 1.3), open the attribute table in step 1.2), add a text field, name the number, enter the corresponding traffic area number, and merge all traffic area layers into one layer MERGE (merge). The traffic districts in the central urban area of Kunshan are numbered from 1.

步骤1.4),以交通小区边界为统计单元获取各类建筑总面积。具体的到昆山市中心城区每个交通小区的各类建筑的建筑面积。In step 1.4), the total area of various buildings is obtained by taking the boundary of the traffic area as the statistical unit. Specifically, the construction area of various buildings in each traffic district in the central urban area of Kunshan.

步骤2)、参见附图4,基于手机信令数据获得的交通出行量,获取昆山2017年月6日的全天手机信令数据,进行数据处理,获取实际的出行量。Step 2), referring to FIG. 4, based on the traffic trip volume obtained from the mobile phone signaling data, obtain the all-day mobile phone signaling data of Kunshan on June 6, 2017, perform data processing, and obtain the actual trip volume.

其中,基于手机信令数据获得的交通出行量,具体如下:Among them, the traffic travel volume obtained based on the mobile phone signaling data is as follows:

步骤2.1),在ARCGIS中依据基站的服务范围创建泰森多边形,将基站的出行数据赋予泰森多边形,打开泰森多边形的属性列表,根据空间关系进行关联;将昆山市2017年6月6号的出行数据进行统计,后得出各个基站的出行量统计表,后和昆山市中心城区基站进行空间关联,得到每个基站的出行量。Step 2.1), create a Thiessen polygon in ARCGIS according to the service range of the base station, assign the trip data of the base station to the Thiessen polygon, open the attribute list of the Thiessen polygon, and associate according to the spatial relationship; The travel data of each base station is calculated, and then the travel volume statistics table of each base station is obtained, and the spatial correlation with the base station in the central urban area of Kunshan is carried out to obtain the travel volume of each base station.

步骤2.2),获得出行人口数据后,用每个泰森多边形的出行人口除以每个泰森多边形的面积,得出出行人口密度;用昆山市中心城区每个基站的出行量除以每个基站服务范围后,得到每个基站的出行人口密度。Step 2.2), after obtaining the travel population data, divide the travel population of each Thiessen polygon by the area of each Thiessen polygon to obtain the travel population density; divide the travel volume of each base station in the central urban area of Kunshan by each After the service range of the base station, the travel population density of each base station is obtained.

步骤2.3),然后将交通小区与基站的泰森多边形进行叠置分析INTERSECT(相交),并将生成的叠置的结果进行计算,打开属性表,添加双精度字段,使用字段计算器计算出行人数;将昆山市的交通小区边界与基站服务范围边界进行相交,对基站进行重新编号。Step 2.3), then superimpose the traffic cell and the Thiessen polygon of the base station to analyze the INTERSECT (intersection), and calculate the generated superimposed results, open the attribute table, add a double field, and use the field calculator to calculate the number of passengers ; Intersect the boundary of the traffic cell in Kunshan with the boundary of the service area of the base station, and renumber the base station.

步骤2.4),最后进行空间统计。基于昆山市中心城区基站重编号进行空间统计,得到每个交通小区的出行量。图5是交通小区出行量含义示意图。Step 2.4), and finally perform spatial statistics. Based on the renumbering of base stations in the central urban area of Kunshan, spatial statistics are performed to obtain the travel volume of each traffic cell. Figure 5 is a schematic diagram of the meaning of the trip volume in a traffic area.

步骤3)、根据通勤人口密度与交通可达性将不同的交通小区进行聚类分析,Step 3), according to the commuter population density and traffic accessibility, cluster analysis of different traffic districts,

其中,根据通勤人口密度与交通可达性将不同的交通小区进行聚类分析,具体如下:Among them, according to the commuter population density and traffic accessibility, different traffic districts are clustered and analyzed, as follows:

步骤3.1),选取分类因子,具体根据研究目的进行确定;选取昆山市中心城区各个交通小区的可达性、通勤密度为分类因子。Step 3.1), select the classification factor, which is determined according to the research purpose; select the accessibility and commuting density of each traffic area in the central urban area of Kunshan as the classification factor.

步骤3.2),确定聚类方法。在SPSS中对昆山市中心城区各个交通小区的可达性、通勤密度为分类因子进行K-Means(K均值)聚类,最确定分类为二,即交通可达性高且通勤密度高、交通可达性差且通勤密度差的交通小区。Step 3.2), determine the clustering method. In SPSS, the accessibility and commuting density of each traffic area in the central city of Kunshan are used as classification factors to perform K-Means (K-means) clustering. Traffic districts with poor accessibility and poor commuting density.

步骤4)、根据步骤3)最终针对每一类交通小区进行多元统计回归分析,确定不同特征、不同居住建筑类型的交通出行。In step 4), according to step 3), multivariate statistical regression analysis is finally carried out for each type of traffic community to determine traffic travel with different characteristics and different residential building types.

其中,将交通小区划分为几类,分别根据统计结果进行多元统计回归得出交通生成率。针对昆山市中心城区的两类交通小区进行多元统计回归,得出具体交通出行量的值。Among them, the traffic districts are divided into several categories, and the traffic generation rate is obtained by performing multivariate statistical regression according to the statistical results. Multivariate statistical regression was carried out for the two types of traffic districts in the central urban area of Kunshan, and the value of specific traffic trips was obtained.

以下是昆山市中心城区出行率回归结果示意图;其中,表1为中心城区不同用地出行率回归情况,表2为外围城区不同用地出行率回归情况。The following is a schematic diagram of the regression results of the travel rate in the central urban area of Kunshan. Among them, Table 1 is the regression of the travel rate of different land use in the central urban area, and Table 2 is the regression of the travel rate of different land use in the peripheral urban area.

表1Table 1

用地类型land type 早高峰出发Morning peak departure 早高峰到达Arrival in the morning peak 全天出发Departure all day 全天到达Arrival all day A公共管理与公共服务用地A public administration and public service land 1.1341.134 3.80753.8075 7.903<sup>**</sup>7.903<sup>**</sup> 8.075<sup>**</sup>8.075<sup>**</sup> B商业服务业设施用地B commercial service industry facility land 1.0571.057 3.76853.7685 16.472<sup>**</sup>16.472<sup>**</sup> 17.315<sup>**</sup>17.315<sup>**</sup> G绿地与广场用地G green space and square land 5.78055.7805 2.052.05 8.5518.551 2.3032.303 H城乡建设用地H urban and rural construction land 3.86153.8615 0.87450.8745 9.9059.905 9.6179.617 M工业用地M industrial land 2.7885<sup>**</sup>2.7885<sup>**</sup> 4.5315<sup>**</sup>4.5315<sup>**</sup> 4.977<sup>**</sup>4.977<sup>**</sup> 5.211<sup>**</sup>5.211 <sup>**</sup> R居住用地R residence 1.986<sup>**</sup>1.986<sup>**</sup> 0.7015<sup>**</sup>0.7015<sup>**</sup> 4.932<sup>**</sup>4.932<sup>**</sup> 4.630<sup>**</sup>4.630<sup>**</sup> S道路与交通设施用地S road and transportation facilities land 14.419514.4195 12.203512.2035 47.872<sup>*</sup>47.872<sup>*</sup> 52.070<sup>*</sup>52.070<sup>*</sup> U公用设施用地U utility land 3.2673.267 13.1813.18 9.8489.848 15.11215.112 W物流仓储用地W logistics warehousing land 1.6341.634 1.4681.468 3.1213.121 3.5963.596

注:**表示p值小于0.01,*表示p值小于0.05,其余p值介于0.05-0.15之间。Note: ** means p-value is less than 0.01, * means p-value is less than 0.05, and the rest p-values are between 0.05-0.15.

表2Table 2

用地类型land type 早高峰出发Morning peak departure 早高峰到达Arrival in the morning peak 全天出发Departure all day 全天到达Arrival all day A公共管理与公共服务用地A public administration and public service land 2.019<sup>*</sup>2.019<sup>*</sup> 1.060<sup>*</sup>1.060<sup>*</sup> 5.243<sup>*</sup>5.243 <sup>*</sup> 5.099<sup>*</sup>5.099<sup>*</sup> B商业服务业设施用地B commercial service industry facility land 1.049<sup>**</sup>1.049<sup>**</sup> 0.285<sup>**</sup>0.285<sup>**</sup> 1.488<sup>*</sup>1.488<sup>*</sup> 1.349<sup>*</sup>1.349 <sup>*</sup> G绿地与广场用地G green space and square land 1.1861.186 0.6240.624 2.4562.456 2.1082.108 H城乡建设用地H urban and rural construction land 0.1810.181 0.1400.140 0.6050.605 0.5380.538 M工业用地M industrial land 0.588<sup>**</sup>0.588<sup>**</sup> 1.075<sup>**</sup>1.075<sup>**</sup> 1.448<sup>**</sup>1.448<sup>**</sup> 1.552<sup>**</sup>1.552<sup>**</sup> R居住用地R residence 2.439<sup>**</sup>2.439<sup>**</sup> 1.129<sup>**</sup>1.129 <sup>**</sup> 4.461<sup>**</sup>4.461<sup>**</sup> 4.232<sup>**</sup>4.232 <sup>**</sup> S道路与交通设施用地S road and transportation facilities land 1.2161.216 1.4051.405 5.2455.245 5.5955.595 U公用设施用地U utility land 1.5151.515 0.7160.716 8.8608.860 8.2758.275 W物流仓储用地W logistics warehousing land 0.5570.557 0.0360.036 3.2943.294 2.9162.916

注:**表示p值小于0.01,*表示p值小于0.05,其余p值介于0.05-0.15之间。Note: ** means p-value is less than 0.01, * means p-value is less than 0.05, and the rest p-values are between 0.05-0.15.

以上内容是结合具体的优选实施方式对本发明所作的进一步详细说明,不能认定本发明的具体实施只局限于这些说明。对于本发明所属技术领域的普通技术人员来说,在不脱离本发明构思的前提下,还可以做出若干简单推演或替代,都应当视为属于本发明的保护范围。The above content is a further detailed description of the present invention in combination with specific preferred embodiments, and it cannot be considered that the specific implementation of the present invention is limited to these descriptions. For those of ordinary skill in the technical field of the present invention, without departing from the concept of the present invention, some simple deductions or substitutions can be made, which should be regarded as belonging to the protection scope of the present invention.

Claims (5)

1.一种分析获取不同性质建筑的交通出行率的方法,其特征在于,包括以下步骤:1. a method for analyzing and obtaining the traffic travel rate of buildings of different natures, is characterized in that, comprises the following steps: 步骤1),统计不同交通小区不同用地类型的建筑总面积;Step 1), count the total building area of different land use types in different traffic districts; 步骤2),基于手机信令数据获取每个交通小区的交通出行量;Step 2), obtain the traffic travel volume of each traffic cell based on mobile phone signaling data; 步骤3),根据通勤人口密度与交通可达性将不同的交通小区进行聚类分析;Step 3), according to the commuter population density and traffic accessibility, perform cluster analysis on different traffic communities; 步骤4),根据步骤3)的分析结果,针对每一类交通小区进行多元统计回归分析,确定不同特征、不同居住建筑类型的交通出行率。Step 4), according to the analysis result of step 3), perform multivariate statistical regression analysis for each type of traffic community, and determine the traffic travel rate of different characteristics and different residential building types. 2.根据权利要求1所述的一种分析获取不同性质建筑的交通出行率的方法,其特征在于,所述步骤1)中统计不同交通小区不同用地类型的建筑总面积,具体如下:2. a kind of method according to claim 1 that analyzes and obtains the traffic trip rate of buildings of different natures, it is characterized in that, in described step 1), the total building area of different traffic districts and different land use types is counted, and is specifically as follows: 步骤1.1),确定建筑分类标准;Step 1.1), determine the building classification standard; 步骤1.2),根据建筑分类标准,将研究区范围内用地按交通小区的边界进行划分,得到各交通小区的用地图层;Step 1.2), according to the building classification standard, divide the land in the study area according to the boundary of the traffic area, and obtain the map layer of each traffic area; 步骤1.3),打开步骤1.2)中的用地图层的属性表,依次添加文本字段,命名编号,输入对应交通小区编号,将所有交通小区图层合并为一个图层;Step 1.3), open the attribute table of the map layer in step 1.2), add text fields in turn, name the number, enter the corresponding traffic area number, and combine all traffic area layers into one layer; 步骤1.4),以交通小区边界为统计单元获取各类建筑总面积。In step 1.4), the total area of various buildings is obtained with the boundary of the traffic area as the statistical unit. 3.根据权利要求2所述的一种分析获取不同性质建筑的交通出行率的方法,其特征在于,所述步骤2)中基于手机信令数据获得交通出行量,具体如下:3. a kind of method that analyzes and obtains the traffic trip rate of buildings of different properties according to claim 2, it is characterized in that, in described step 2), obtain traffic trip amount based on mobile phone signaling data, be specific as follows: 步骤2.1),在ARCGIS中依据基站的服务范围创建泰森多边形,将基站的出行数据赋予泰森多边形,打开泰森多边形的属性列表,根据空间关系进行关联;Step 2.1), create a Thiessen polygon in ARCGIS according to the service range of the base station, assign the trip data of the base station to the Thiessen polygon, open the attribute list of the Thiessen polygon, and associate according to the spatial relationship; 步骤2.2),获得出行人口数据后,用每个泰森多边形的出行人口除以每个泰森多边形的面积,得出出行人口密度;Step 2.2), after obtaining the travel population data, divide the travel population of each Thiessen polygon by the area of each Thiessen polygon to obtain the travel population density; 步骤2.3),将交通小区与基站的泰森多边形进行叠置分析INTERSECT,并将生成的叠置的结果进行计算,打开属性表,添加双精度字段,使用字段计算器计算出行人数;Step 2.3), carry out the superposition analysis INTERSECT of the Thiessen polygon of the traffic cell and the base station, and calculate the result of the superposition generated, open the attribute table, add a double field, and use the field calculator to calculate the number of travelers; 步骤2.4),最后进行空间统计,得到每个交通小区的出行量。Step 2.4), and finally perform spatial statistics to obtain the travel volume of each traffic area. 4.根据权利要求3所述的一种分析获取不同性质建筑的交通出行率的方法,其特征在于,所述步骤3)中根据通勤人口密度与交通可达性将不同的交通小区进行聚类分析,具体如下:4. The method according to claim 3, wherein in the step 3), different traffic cells are clustered according to commuter population density and traffic accessibility. The analysis is as follows: 步骤3.1),根据研究目的选取分类因子:将城区各个交通小区的交通可达性、通勤人口密度确定为分类因子;Step 3.1), select the classification factor according to the research purpose: determine the traffic accessibility and commuter population density of each traffic district in the urban area as the classification factor; 步骤3.2),确定聚类方法:根据步骤3.1)确定的分类因子进行K-Means聚类,最终确定分类为二,即交通可达性高且通勤密度高、交通可达性差且通勤密度差的交通小区。Step 3.2), determine the clustering method: perform K-Means clustering according to the classification factor determined in step 3.1), and finally determine the classification into two, that is, those with high traffic accessibility and high commuting density, and poor traffic accessibility and poor commuting density. traffic area. 5.根据权利要求4所述的一种分析获取不同性质建筑的交通出行率的方法,其特征在于,所述步骤4)具体如下:5. a kind of method according to claim 4, it is characterized in that, described step 4) is specifically as follows: 根据聚类分析的结果,将交通小区划分为几类,分别根据统计结果进行多元统计回归得出交通生成率;具体为:According to the results of cluster analysis, the traffic districts are divided into several categories, and the traffic generation rate is obtained by performing multivariate statistical regression according to the statistical results respectively; the details are as follows: (1)针对每一个交通小区构建一个多元线性方程,形成多个方程进行联立求解,(1) Construct a multivariate linear equation for each traffic area, and form multiple equations to solve simultaneously, Y1=β1x12x23x3+…+βnxn+b1 Y 11 x 12 x 23 x 3 +…+β n x n +b 1 Y2=β1x12x23x3+…+βnxn+b2 Y 21 x 12 x 23 x 3 +…+β n x n +b 2 ……... Yn=β1x12x23x3+…+βnxn+bn Y n1 x 12 x 23 x 3 +…+β n x n +b n 式中,Yn:每个交通小区的交通出行量;βn:每种用地类型的交通出行率;Xn:每种用地类型的建筑总面积;bn:常数项;n代表交通小区的数量;In the formula, Y n : the traffic trip volume of each traffic area; β n : the traffic trip rate of each land use type; X n : the total building area of each land use type; b n : constant term; n represents the traffic area of the traffic area. quantity; (2)根据控规单元对于交通小区进行重分组,对每个控规单元进行出行量的统计,具体计算公式如下:(2) Regroup the traffic cells according to the control and regulation unit, and count the travel volume of each control and regulation unit. The specific calculation formula is as follows: 式中:where: Bi:控规单元i的出行量;dik:控规单元i的第k类建筑面积;wik:控规单元i每个第k类建筑的出行率;B i : the trip volume of the control unit i; d ik : the building area of the kth class of the control unit i; w ik : the trip rate of each k-th building of the control unit i; (3)最终得出每种用地类型的交通出行率。(3) Finally, the traffic travel rate of each land use type is obtained.
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