CN108596381B - Urban parking demand prediction method based on OD data - Google Patents
Urban parking demand prediction method based on OD data Download PDFInfo
- Publication number
- CN108596381B CN108596381B CN201810348875.9A CN201810348875A CN108596381B CN 108596381 B CN108596381 B CN 108596381B CN 201810348875 A CN201810348875 A CN 201810348875A CN 108596381 B CN108596381 B CN 108596381B
- Authority
- CN
- China
- Prior art keywords
- parking
- points
- data
- area
- peak
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 21
- 238000000611 regression analysis Methods 0.000 claims abstract description 9
- 230000032823 cell division Effects 0.000 claims description 7
- 238000010219 correlation analysis Methods 0.000 claims description 6
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 238000013277 forecasting method Methods 0.000 claims description 2
- 238000007781 pre-processing Methods 0.000 claims description 2
- 239000000463 material Substances 0.000 abstract description 4
- 238000013468 resource allocation Methods 0.000 abstract description 3
- 238000011835 investigation Methods 0.000 abstract 1
- 238000005192 partition Methods 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 10
- 238000011497 Univariate linear regression Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000008929 regeneration Effects 0.000 description 1
- 238000011069 regeneration method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- Development Economics (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Educational Administration (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- General Health & Medical Sciences (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
Description
技术领域technical field
本发明涉及城市交通和停车规划领域,尤其涉及一种基于OD(origin,destination,起点,终点)数据的城市停车需求预测方法。The invention relates to the field of urban traffic and parking planning, in particular to an urban parking demand prediction method based on OD (origin, destination, starting point, ending point) data.
背景技术Background technique
近年来,随着机动车保有量的快速增长,在许多城市,停车位供不应求,停车难已然成为大部分城市“成长的烦恼”。停车体验,也成了关系人们出行幸福感的一大关键指标。In recent years, with the rapid growth of the number of motor vehicles, in many cities, the supply of parking spaces is in short supply, and the difficulty of parking has become a "growing trouble" in most cities. Parking experience has also become a key indicator of people's travel happiness.
准确地进行停车需求预测是城市停车设施规划建设的前提和基础,停车需求的预测量过大会造成土地与资金浪费,但预测过小会导致无法满足城市停车需求,制约社会经济发展,造成严重的交通问题。Accurate forecasting of parking demand is the premise and foundation of the planning and construction of urban parking facilities. Excessive forecasting of parking demand will result in waste of land and capital, but too small forecast will lead to inability to meet urban parking demand, restrict social and economic development, and cause serious problems. Traffic problems.
目前,现有技术中的计算停车需求的方法需要依据较为完整的城市土地报告数据,需要投入大量人力、物力、财力进行城市交通出行调查,调查耗费的时间也较长。At present, the method for calculating parking demand in the prior art needs to be based on relatively complete urban land report data, requires a lot of manpower, material resources and financial resources to conduct urban traffic travel survey, and the survey takes a long time.
因此,如何快捷准确地进行城市停车需求的预测是一个亟待解决的问题。Therefore, how to quickly and accurately predict the urban parking demand is an urgent problem to be solved.
发明内容SUMMARY OF THE INVENTION
本发明的实施例提供了一种基于OD数据的城市停车需求预测方法,以解决现有技术的问题。Embodiments of the present invention provide a method for predicting urban parking demand based on OD data, so as to solve the problems of the prior art.
为了实现上述目的,本发明采取了如下技术方案。In order to achieve the above objects, the present invention adopts the following technical solutions.
一种基于OD数据的停车需求预测方法,包括:A parking demand forecasting method based on OD data, comprising:
获取研究区域的OD数据和停车数据,根据所述OD数据和停车数据对所述研究区域进行停车小区划分;Obtain the OD data and parking data of the research area, and divide the research area into parking cells according to the OD data and the parking data;
基于所述研究区域的OD数据、停车数据和停车小区划分方案进行回归分析,构建停车需求预测模型;Based on the OD data, parking data and parking area division scheme of the research area, regression analysis is performed to build a parking demand forecasting model;
根据所述停车需求预测模型对目标停车区域进行停车需求预测。According to the parking demand prediction model, the parking demand prediction is performed on the target parking area.
进一步地,所述的获取研究区域的OD数据和停车数据,包括:Further, the described acquisition of OD data and parking data of the research area includes:
获取研究区域中的汽车的OD数据,该OD数据包括:编号、出行起点O点经纬度、出行终点D点经纬度、开始时间、结束时间和行程距离;Obtain the OD data of the car in the study area, the OD data includes: serial number, longitude and latitude of travel starting point O point, travel end point D point longitude and latitude, start time, end time and travel distance;
获取研究区域中的汽车的停车数据,该停车数据包括停车场ID、高峰时刻、高峰停车数。Obtain parking data of cars in the study area, including parking lot IDs, peak hours, and peak parking counts.
进一步地,所述的方法还包括:Further, the method also includes:
对所述OD数据和停车数据中误差及异常值进行预处理,筛除字段取值为空的数据,并根据研究区域的经纬度坐标范围删除O点或D点不在该研究区域内的数据,删除行程距离小于500米的数据,删除行程时间tj≤0min的数据;删除行程速度Vj≥100km/h的数据。Perform preprocessing on the errors and abnormal values in the OD data and parking data, filter out the data whose field value is empty, and delete the data whose points O or D are not in the research area according to the latitude and longitude coordinate range of the research area. For data whose travel distance is less than 500 meters, delete data with travel time t j ≤ 0min; delete data with travel speed V j ≥ 100 km/h.
进一步地,所述的根据所述OD数据和停车数据对所述研究区域进行停车小区划分,包括:Further, the described research area is divided into parking cells according to the OD data and parking data, including:
将研究区域划分为多个格子,格子边长从设定数值依次递增,分别统计不同格子边长条件下每个格子内的高峰停车数、O点数目、D点数目,将高峰停车数、O点数目、D点数目均大于指定数值阈值的格子定义为有效格子,有效格子总数记为m;Divide the research area into multiple grids, and the grid side lengths increase from the set value in turn, and count the number of peak parking lots, the number of points O, and the number of points D in each grid under the condition of different grid side lengths. The grids whose number of points and the number of D points are greater than the specified numerical threshold are defined as valid grids, and the total number of valid grids is denoted as m;
对每个有效格子内的高峰停车数、O点数目和D点数目进行相关性分析,计算不同格子边长条件下高峰停车数与O点数目的相关系数rO,计算不同格子边长条件下高峰停车数与D点数目的相关系数rD,综合考虑有效样本数m、rO和rD确定最优格式边长,根据所述最优格式边长对所述研究区域进行停车小区划分。The correlation analysis is carried out on the number of peak parking, the number of O points and the number of D points in each valid grid, and the correlation coefficient r O between the number of peak parking and the number of O points under the condition of different grid side lengths is calculated, and the peak value under the condition of different grid side lengths is calculated. The correlation coefficient r D between the number of parking lots and the number of points D is determined by comprehensively considering the number of valid samples m, r O and r D to determine the optimal format side length, and the research area is divided into parking cells according to the optimal format side length.
进一步地,所述的基于所述研究区域的OD数据、停车数据和停车小区划分方案进行回归分析,构建停车需求预测模型,包括:Further, the regression analysis is carried out based on the OD data, parking data and the parking cell division scheme of the research area, and a parking demand prediction model is constructed, including:
在根据所述最优格式边长对所述研究区域进行停车小区划分后,将所述研究区域中的每个格子定义为一个停车小区,分别确定每个停车小区的高峰停车数、O点数目和D点数目;After the research area is divided into parking areas according to the optimal format side length, each grid in the research area is defined as a parking area, and the number of peak parking areas and the number of O points in each parking area are determined respectively. and the number of D points;
根据每个停车小区的高峰停车数、O点数目,建立研究区域内停车小区的高峰停车数和O点数目之间的一元线性拟合函数Pi=αxOi;According to the peak parking number and the number of O points in each parking area, establish a univariate linear fitting function P i =αx Oi between the peak parking number of the parking area and the number of O points in the study area;
根据每个停车小区的高峰停车数、D点数目,建立研究区域内停车小区的高峰停车数和D点数目之间的一元线性拟合函数Pi=βxDi;According to the peak parking number and the number of D points in each parking area, establish a univariate linear fitting function P i =βx Di between the peak parking number of the parking area and the number of D points in the study area;
其中Pi为停车小区i内高峰停车数量,xOi为停车小区内O点数目,xDi为停车小区内D点数目,α和β分别为停车小区关于O点和D点数目的回归系数;Among them, Pi is the number of peak parking spaces in the parking area i , x Oi is the number of points O in the parking area, x Di is the number of points D in the parking area, and α and β are the regression coefficients of the number of points O and D in the parking area, respectively;
将所述研究区域对应的高峰停车数分别和O点数目、D点数目之间的一元线性拟合函数作为停车需求预测模型。The univariate linear fitting function between the peak parking numbers corresponding to the research area, the number of points O and the number of points D, respectively, is used as a parking demand prediction model.
进一步地,所述的根据所述停车需求预测模型对目标停车区域进行停车需求预测包括:Further, the predicting the parking demand of the target parking area according to the parking demand prediction model includes:
将目标停车区域的O点数目输入所述停车需求预测模型中的高峰停车数和O点数目之间的一元线性拟合函数,得到所述目标停车区域的高峰停车数;Input the number of 0 points of the target parking area into the univariate linear fitting function between the number of peak parking and the number of 0 points in the parking demand prediction model, to obtain the peak parking number of the target parking area;
和/或,and / or,
将目标停车区域的D点数目输入所述停车需求预测模型中的高峰停车数和D点数目之间的一元线性拟合函数,得到所述目标停车区域的高峰停车数。The number of D points in the target parking area is input into the univariate linear fitting function between the number of peak parking and the number of D points in the parking demand prediction model to obtain the peak parking number in the target parking area.
由上述本发明的实施例提供的技术方案可以看出,本发明实施例的方法通过对OD数据和已知区域的停车数据进行回归分析,得到停车需求预测模型,然后利用停车需求预测模型估计未知区域停车需求,摒弃了传统的大规模调查,节省了人力和物力,具有简便快捷准确的优点,可为停车规划、停车资源配置、停车问题解决提供参考和技术支持。It can be seen from the technical solutions provided by the above embodiments of the present invention that the method of the embodiment of the present invention obtains a parking demand prediction model by performing regression analysis on OD data and parking data in a known area, and then uses the parking demand prediction model to estimate the unknown Regional parking demand, abandoning the traditional large-scale survey, saving manpower and material resources, has the advantages of simplicity, speed and accuracy, and can provide reference and technical support for parking planning, parking resource allocation, and parking problem solving.
本发明附加的方面和优点将在下面的描述中部分给出,这些将从下面的描述中变得明显,或通过本发明的实践了解到。Additional aspects and advantages of the present invention will be set forth in part in the following description, which will be apparent from the following description, or may be learned by practice of the present invention.
附图说明Description of drawings
为了更清楚地说明本发明实施例的技术方案,下面将对实施例描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本发明的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。In order to illustrate the technical solutions of the embodiments of the present invention more clearly, the following briefly introduces the accompanying drawings used in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without any creative effort.
图1为本发明实施例提供的一种基于OD数据的城市停车需求预测方法的处理流程图;1 is a process flow diagram of a method for predicting urban parking demand based on OD data provided by an embodiment of the present invention;
图2为本发明实施例提供的一种不同格子边长条件停车数与起点数目、D点数目的相关性分析图;Fig. 2 is a kind of correlation analysis diagram of the number of parking lots and the number of starting points and the number of D points with different grid side length conditions provided by an embodiment of the present invention;
图3为本发明实施例提供的一种停车小区划分示意图;3 is a schematic diagram of a parking cell division provided by an embodiment of the present invention;
图4为本发明实施例提供的一种停车小区的高峰停车数与起点数目散点图;FIG. 4 is a scatter diagram of the number of peak parking and the number of starting points of a parking area provided by an embodiment of the present invention;
图5为本发明实施例提供的一种停车小区的高峰停车数与D点数目散点图。FIG. 5 is a scatter diagram of the number of peak parking spaces and the number of D points in a parking area provided by an embodiment of the present invention.
具体实施方式Detailed ways
下面详细描述本发明的实施方式,所述实施方式的示例在附图中示出,其中自始至终相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面通过参考附图描述的实施方式是示例性的,仅用于解释本发明,而不能解释为对本发明的限制。Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but not to be construed as a limitation of the present invention.
本技术领域技术人员可以理解,除非特意声明,这里使用的单数形式“一”、“一个”、“所述”和“该”也可包括复数形式。应该进一步理解的是,本发明的说明书中使用的措辞“包括”是指存在所述特征、整数、步骤、操作、元件和/或组件,但是并不排除存在或添加一个或多个其他特征、整数、步骤、操作、元件、组件和/或它们的组。应该理解,当我们称元件被“连接”或“耦接”到另一元件时,它可以直接连接或耦接到其他元件,或者也可以存在中间元件。此外,这里使用的“连接”或“耦接”可以包括无线连接或耦接。这里使用的措辞“和/或”包括一个或更多个相关联的列出项的任一单元和全部组合。It will be understood by those skilled in the art that the singular forms "a", "an", "the" and "the" as used herein can include the plural forms as well, unless expressly stated otherwise. It should be further understood that the word "comprising" used in the description of the present invention refers to the presence of stated features, integers, steps, operations, elements and/or components, but does not exclude the presence or addition of one or more other features, Integers, steps, operations, elements, components and/or groups thereof. It will be understood that when we refer to an element as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Furthermore, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
本技术领域技术人员可以理解,除非另外定义,这里使用的所有术语(包括技术术语和科学术语)具有与本发明所属领域中的普通技术人员的一般理解相同的意义。还应该理解的是,诸如通用字典中定义的那些术语应该被理解为具有与现有技术的上下文中的意义一致的意义,并且除非像这里一样定义,不会用理想化或过于正式的含义来解释。It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It should also be understood that terms such as those defined in the general dictionary should be understood to have meanings consistent with their meanings in the context of the prior art and, unless defined as herein, are not to be taken in an idealized or overly formal sense. explain.
为便于对本发明实施例的理解,下面将结合附图以几个具体实施例为例做进一步的解释说明,且各个实施例并不构成对本发明实施例的限定。In order to facilitate the understanding of the embodiments of the present invention, the following will take several specific embodiments as examples for further explanation and description in conjunction with the accompanying drawings, and each embodiment does not constitute a limitation to the embodiments of the present invention.
本发明实施例提供的一种基于OD数据的停车需求预测方法的处理流程如图1所示,包括如下的处理步骤:A processing flow of a method for predicting parking demand based on OD data provided by an embodiment of the present invention is shown in FIG. 1 , and includes the following processing steps:
步骤S110、获取研究区域的OD数据和停车数据。Step S110, acquire OD data and parking data of the research area.
步骤S120、进行停车小区划分,依据研究目的确定最优的停车小区划分方案。Step S120, dividing the parking area, and determining the optimal parking area dividing scheme according to the research purpose.
步骤S130、基于步骤S110获得OD数据和停车数据以及步骤二得到的停车小区划分,进行回归分析,构建停车需求预测模型。In step S130, based on the OD data and parking data obtained in step S110 and the parking cell division obtained in step 2, regression analysis is performed to construct a parking demand prediction model.
步骤S140、根据所述停车需求预测模型对目标停车区域进行停车需求预测。Step S140: Predict the parking demand for the target parking area according to the parking demand prediction model.
在一些实施例中,前述步骤S110的获取研究区域的OD数据和停车数据具体实现包括:In some embodiments, the specific implementation of obtaining the OD data and parking data of the research area in the aforementioned step S110 includes:
提取研究区域中的汽车出行的OD数据,该OD数据指描述出行的一系列数据,主要包括:编号、O点(出行起点)经纬度、D点(出行终点)经纬度、开始时间、结束时间、行程距离。如下表1所示。Extract the OD data of car trips in the study area. The OD data refers to a series of data describing trips, mainly including: number, O point (travel start point) latitude and longitude, D point (travel end point) longitude and latitude, start time, end time, itinerary distance. As shown in Table 1 below.
表1Table 1
提取研究区域中的汽车出行的停车数据,停车数据主要包括停车场ID、高峰时刻、高峰停车数。Extract the parking data of car travel in the study area. The parking data mainly includes the parking lot ID, peak hours, and peak parking numbers.
在一些实施例中,对前述步骤1中提取的OD数据和停车数据中误差及异常值进行预处理,筛除字段取值为空的数据。并根据所研究区域的经纬度坐标范围删除起点或D点不在该区域内的数据。考虑到公共交通站点服务半径为500m,故删除行程距离小于500米的数据。In some embodiments, the errors and abnormal values in the OD data and parking data extracted in the foregoing step 1 are preprocessed, and the data whose field value is empty is filtered out. And according to the latitude and longitude coordinate range of the studied area, delete the data whose starting point or point D is not in this area. Considering that the service radius of public transport stations is 500m, the data with travel distance less than 500m are deleted.
依据某次出行(设编号为j)的开始时间tOj、结束时间tDj以及行程距离Lj可以计算行程时间和行程速度,假设行程时间为tj、行程速度为Vj,则tj=tDj-tOj,Vj=Lj/tj。The travel time and travel speed can be calculated according to the start time t Oj , the end time t Dj and the travel distance L j of a trip (set as j), assuming that the travel time is t j and the travel speed is V j , then t j = t Dj -t Oj , V j =L j /t j .
根据上述步骤中计算出的再生数据,删除行程时间tj≤0min的数据;删除行程速度Vj≥100km/h的数据。According to the regeneration data calculated in the above steps, delete the data of travel time t j ≤ 0min; delete the data of travel speed V j ≥ 100km/h.
本领域技术人员应能理解上述行程时间tj、行程速度Vj的阈值数值仅为举例,其他现有的或今后可能出现的行程时间tj、行程速度Vj的阈值数值如可适用于本发明实施例,也应包含在本发明保护范围以内,并在此以引用方式包含于此。Those skilled in the art should understand that the above threshold values of travel time t j and travel speed V j are only examples, and other existing or possible future threshold values of travel time t j and travel speed V j are applicable to the present invention. The embodiments of the invention should also be included within the protection scope of the present invention, and are incorporated herein by reference.
在一些实施例中,前述步骤S120中的进行停车小区划分,依据研究目的确定最优的停车小区划分方案的具体实现包括:In some embodiments, the specific implementation of determining the optimal parking cell division scheme according to the research purpose of performing parking cell division in the aforementioned step S120 includes:
步骤1、将研究区域划分为若干格子,每个格子作为停车需求预测的基本研究单元,用于确定出行起点、出行终点的所属单元。Step 1. Divide the research area into several grids, and each grid is used as a basic research unit for parking demand forecasting, and is used to determine the unit to which the travel start point and travel end point belong.
步骤2、格子边长R从设定数值(比如200m)开始设置,以100m为单位递增,分别统计不同R下的有效格子数、每个格子内的高峰停车数、起点数目和D点数目。Step 2. The grid side length R is set from the set value (such as 200m), and is incremented in units of 100m, and the number of valid grids under different R, the number of peak stops in each grid, the number of starting points and the number of D points are counted.
步骤3、将高峰停车数、起点数目、D点数目均大于指定数值阈值的格子定义为有效格子,统计有效格子的数目,记为m。将未获取高峰停车数的格子定义为未知格子。
步骤4、分别计算不同R条件下,高峰停车数与起点数目、高峰停车数与D点数目的相关系数rO和rD。Step 4: Calculate the correlation coefficients r O and r D of the number of peak stops and the number of starting points, and the number of peak stops and the number of D points under different R conditions.
步骤5、依据研究目的,考虑有效样本数m、rO和rD确定最优格式边长,根据所述最优格式边长对所述研究区域进行停车小区划分。Step 5: According to the research purpose, consider the effective sample numbers m, r O and r D to determine the optimal format side length, and divide the research area into parking cells according to the optimal format side length.
步骤6、在确定的最优格子边长下,把每个格子定义为一个停车小区,分别确定每个停车小区的高峰停车数、起点数目、D点数目。Step 6. Under the determined optimal grid side length, define each grid as a parking area, and determine the number of peak parking, the number of starting points, and the number of D points in each parking area.
图2为本发明实施例提供的一种不同格子边长条件停车数与起点数目、D点数目的相关性分析图,在一些实施例中,前述步骤5中选取最优格子边长时,有效样本数m、rO和rD至少满足:m≥10,rO和rD≥0.85。2 is a correlation analysis diagram of the number of parking spaces, the number of starting points, and the number of D points with different grid side length conditions provided by an embodiment of the present invention. In some embodiments, when the optimal grid side length is selected in the foregoing step 5, the effective sample The numbers m, r O and r D at least satisfy: m ≥ 10, r O and r D ≥ 0.85.
本领域技术人员应能理解上述m、rO和rD的有效样本数的阈值数值仅为举例,其他现有的或今后可能出现的有效样本数的阈值数值如可适用于本发明实施例,也应包含在本发明保护范围以内,并在此以引用方式包含于此。Those skilled in the art should understand that the above-mentioned threshold values for the number of valid samples of m, r O , and r D are only examples, and other existing or possible threshold values for the number of valid samples in the future may be applicable to the embodiments of the present invention, It should also be included within the scope of the present invention and is hereby incorporated by reference.
在一些实施例中,前述步骤S130中的基于OD数据和停车数据以及停车小区划分,进行回归分析构建停车需求预测模型的具体实现包括:In some embodiments, the specific implementation of performing regression analysis to construct a parking demand prediction model based on OD data, parking data and parking cell division in the aforementioned step S130 includes:
根据每个停车小区的高峰停车数、O点数目,建立研究区域内停车小区的高峰停车数和O点数目之间的一元线性拟合函数Pi=αxOi,该一元线性拟合函数Pi=αxOi适合于研究区域内所有的停车小区;According to the peak parking number and the number of O points in each parking area, a univariate linear fitting function P i =αx Oi is established between the peak parking number of the parking area and the number of O points in the study area. The univariate linear fitting function P i = αx Oi is suitable for all parking areas in the study area;
根据每个停车小区的高峰停车数、D点数目,建立研究区域内停车小区的高峰停车数和D点数目之间的一元线性拟合函数Pi=βxDi,该一元线性拟合函数Pi=βxDi适合于研究区域内所有的停车小区;According to the peak parking number and the number of D points in each parking area, a univariate linear fitting function P i =βx Di between the peak parking number of the parking area and the number of D points in the study area is established. The univariate linear fitting function P i = βx Di is suitable for all parking areas in the study area;
其中Pi为停车小区i内高峰停车数量,xOi为停车小区内O点数目,xDi为停车小区内D点数目,α和β分别为停车小区关于O点和D点数目的回归系数;Among them, Pi is the number of peak parking spaces in the parking area i , x Oi is the number of points O in the parking area, x Di is the number of points D in the parking area, and α and β are the regression coefficients of the number of points O and D in the parking area, respectively;
将所述研究区域对应的高峰停车数分别和O点数目、D点数目之间的一元线性拟合函数作为停车需求预测模型。The univariate linear fitting function between the peak parking numbers corresponding to the research area, the number of points O and the number of points D, respectively, is used as a parking demand prediction model.
图3为本发明实施例提供的一种停车小区划分示意图,图4为停车小区的高峰停车数与起点数目的散点图,图5为停车小区的高峰停车数与D点数目的散点图。将所述研究区域中的每个格子定义为一个停车小区,分别确定每个停车小区的高峰停车数、O点数目和D点数目。如图3所示。分析高峰停车数、起点数目、D点数目的关系,如图4、图5所示,分别观察停车小区的高峰停车数和O点数目、D点数目的散点图,可以初步判断停车小区的高峰停车数和O点数目、D点数目之间均存在一定的正相关性,因此进行下一步相关性分析。FIG. 3 is a schematic diagram of a parking area division provided by an embodiment of the present invention, FIG. 4 is a scatter diagram of the number of parking areas at the peak of the parking area and the number of starting points, and FIG. 5 is a scatter diagram of the number of parking areas at the peak of the parking area and the number of D points. Each grid in the study area is defined as a parking area, and the number of peak parking, the number of points O and the number of points D of each parking area are determined respectively. As shown in Figure 3. Analyze the relationship between the number of peak parking, the number of starting points, and the number of points D, as shown in Figure 4 and Figure 5, and observe the scatter plots of the number of peak parking, the number of points O, and the number of points D in the parking area, and you can preliminarily determine the peak parking in the parking area. There is a certain positive correlation between the number of O points and the number of D points, so the next correlation analysis is carried out.
由相关性分析结果,根据每个停车小区的高峰停车数、O点数目,建立停车小区的高峰停车数和O点数目之间的一元线性拟合函数Pi=0.009xOi,一元线性回归模型对高峰停车数与O点数目关系拟合结果R方是0.913,相对误差是58.45%。According to the results of correlation analysis, according to the peak parking number and the number of O points in each parking area, a univariate linear fitting function P i = 0.009x Oi between the peak parking number and the number of O points in each parking area is established, a univariate linear regression model The R-square of the fitting result of the relationship between the number of peak stops and the number of O points is 0.913, and the relative error is 58.45%.
根据每个停车小区的高峰停车数、D点数目,建立停车小区的高峰停车数和D点数目之间的一元线性拟合函数Pi=0.009xDi,一元线性回归模型对高峰停车数与D点数目关系拟合结果R方是0.965,相对误差是40.69%。According to the peak parking number and the number of D points in each parking area, a univariate linear fitting function P i = 0.009x Di between the peak parking number of the parking area and the number of D points is established. The R-square of the fitting result of point number relationship is 0.965, and the relative error is 40.69%.
其中Pi为停车小区i内高峰停车数量,xOi为停车小区内O点数目,xDi为停车小区内D点数目,α和β分别为停车小区关于O点和D点数目的回归系数;Among them, Pi is the number of peak parking spaces in the parking area i , x Oi is the number of points O in the parking area, x Di is the number of points D in the parking area, and α and β are the regression coefficients of the number of points O and D in the parking area, respectively;
将所述研究区域对应的高峰停车数分别和O点数目、D点数目之间的一元线性拟合函数作为停车需求预测模型。The univariate linear fitting function between the peak parking numbers corresponding to the research area, the number of points O and the number of points D, respectively, is used as a parking demand prediction model.
在一些实施例中,前述步骤S140中的根据所述停车需求预测模型对目标停车区域进行停车需求预测的具体实现包括:In some embodiments, the specific implementation of predicting the parking demand in the target parking area according to the parking demand prediction model in the foregoing step S140 includes:
利用最终得到的停车需求预测模型以及目标停车区域中O点数目与D点数目,可以预测得到目标停车区域中的高峰停车数Pi,从而实现对该目标停车区域的停车需求进行预测。Using the finally obtained parking demand prediction model and the number of points O and D in the target parking area, the peak parking number Pi in the target parking area can be predicted , so as to predict the parking demand of the target parking area.
将目标停车区域的O点数目输入所述停车需求预测模型中的高峰停车数和O点数目之间的一元线性拟合函数,得到所述目标停车区域的高峰停车数;Input the number of 0 points of the target parking area into the univariate linear fitting function between the number of peak parking and the number of 0 points in the parking demand prediction model, to obtain the peak parking number of the target parking area;
和/或,and / or,
将目标停车区域的D点数目输入所述停车需求预测模型中的高峰停车数和D点数目之间的一元线性拟合函数,得到所述目标停车区域的高峰停车数。The number of D points in the target parking area is input into the univariate linear fitting function between the number of peak parking and the number of D points in the parking demand prediction model to obtain the peak parking number in the target parking area.
在一些实施例中,可以通过比较两个一元线性回归结果的R方,选择R方较大的模型作为最终的停车需求预测模型。In some embodiments, a model with a larger R-square may be selected as the final parking demand prediction model by comparing the R-squares of the two univariate linear regression results.
综上所述,本发明实施例的方法通过对OD数据和已知区域的停车数据进行回归分析,得到停车需求预测模型,然后利用停车需求预测模型估计未知区域停车需求,摒弃了传统的大规模调查,节省了人力、物力和财力,具有简便快捷准确的优点,可为停车规划、停车资源配置、停车问题解决提供参考和技术支持。To sum up, the method of the embodiment of the present invention obtains a parking demand prediction model by performing regression analysis on OD data and parking data in a known area, and then uses the parking demand prediction model to estimate the parking demand in an unknown area, and abandons the traditional large-scale parking requirements. The survey saves manpower, material and financial resources, has the advantages of simplicity, speed and accuracy, and can provide reference and technical support for parking planning, parking resource allocation, and parking problem solving.
本领域普通技术人员可以理解:附图只是一个实施例的示意图,附图中的模块或流程并不一定是实施本发明所必须的。Those of ordinary skill in the art can understand that the accompanying drawing is only a schematic diagram of an embodiment, and the modules or processes in the accompanying drawing are not necessarily necessary to implement the present invention.
本说明书中的各个实施例均采用递进的方式描述,各个实施例之间相同相似的部分互相参见即可,每个实施例重点说明的都是与其他实施例的不同之处。尤其,对于装置或系统实施例而言,由于其基本相似于方法实施例,所以描述得比较简单,相关之处参见方法实施例的部分说明即可。以上所描述的装置及系统实施例仅仅是示意性的,其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的,作为单元显示的部件可以是或者也可以不是物理单元,即可以位于一个地方,或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。本领域普通技术人员在不付出创造性劳动的情况下,即可以理解并实施。Each embodiment in this specification is described in a progressive manner, and the same and similar parts between the various embodiments may be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the apparatus or system embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and reference may be made to some descriptions of the method embodiments for related parts. The apparatus and system embodiments described above are only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, It can be located in one place, or it can be distributed over multiple network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment. Those of ordinary skill in the art can understand and implement it without creative effort.
以上所述,仅为本发明较佳的具体实施方式,但本发明的保护范围并不局限于此,任何熟悉本技术领域的技术人员在本发明揭露的技术范围内,可轻易想到的变化或替换,都应涵盖在本发明的保护范围之内。因此,本发明的保护范围应该以权利要求的保护范围为准。The above description is only a preferred embodiment of the present invention, but the protection scope of the present invention is not limited to this. Substitutions should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (3)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201810348875.9A CN108596381B (en) | 2018-04-18 | 2018-04-18 | Urban parking demand prediction method based on OD data |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201810348875.9A CN108596381B (en) | 2018-04-18 | 2018-04-18 | Urban parking demand prediction method based on OD data |
Publications (2)
Publication Number | Publication Date |
---|---|
CN108596381A CN108596381A (en) | 2018-09-28 |
CN108596381B true CN108596381B (en) | 2022-06-03 |
Family
ID=63611115
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201810348875.9A Active CN108596381B (en) | 2018-04-18 | 2018-04-18 | Urban parking demand prediction method based on OD data |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN108596381B (en) |
Families Citing this family (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN110222131A (en) * | 2019-05-21 | 2019-09-10 | 北京交通大学 | The beginning and the end information extracting method and device |
CN110659774B (en) * | 2019-09-23 | 2022-08-02 | 北京交通大学 | Parking demand forecasting method driven by big data method |
Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102496076A (en) * | 2011-11-30 | 2012-06-13 | 广州市交通规划研究所 | Macroscopic, mid-scope and microscopic multilevel urban parking demand prediction model integrated system |
Family Cites Families (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN1959759A (en) * | 2006-11-17 | 2007-05-09 | 上海城市综合交通规划科技咨询有限公司 | Traffic analysis method based on fluctuated data of vehicles |
JP5129799B2 (en) * | 2009-11-24 | 2013-01-30 | 株式会社エヌ・ティ・ティ・ドコモ | Demand forecasting apparatus and demand forecasting method |
CN102013159A (en) * | 2010-10-26 | 2011-04-13 | 隋亚刚 | High-definition video detection data-based region dynamic origin and destination (OD) matrix acquiring method |
CN103413178A (en) * | 2013-07-13 | 2013-11-27 | 北京工业大学 | Sampling-based park-and-ride facility attraction demand quantitative classification calculation method |
CN104217250B (en) * | 2014-08-07 | 2017-05-31 | 北京市交通信息中心 | A kind of urban rail transit new line based on historical data opens passenger flow forecasting |
CN104933480B (en) * | 2015-06-10 | 2018-03-06 | 江苏省城市规划设计研究院 | A kind of traffic stops and start-ups delay amount Forecasting Methodology based on parking supply and demand regulation and control coefficient |
CN105046949A (en) * | 2015-06-12 | 2015-11-11 | 中南大学 | Method for achieving vehicle source prediction by calculating O-D flow based on mobile phone data |
CN105046350A (en) * | 2015-06-30 | 2015-11-11 | 东南大学 | AFC data-based public transport passenger flow OD real-time estimation method |
CN105513351A (en) * | 2015-12-17 | 2016-04-20 | 北京亚信蓝涛科技有限公司 | Traffic travel characteristic data extraction method based on big data |
CN105489056B (en) * | 2015-12-28 | 2018-01-26 | 中兴软创科技股份有限公司 | A kind of parking facilities' forecasting method based on OD matrixes |
CN106846805B (en) * | 2017-03-06 | 2019-11-08 | 南京多伦科技股份有限公司 | A kind of dynamic road grid traffic needing forecasting method and its system |
CN107679654B (en) * | 2017-09-25 | 2021-07-27 | 同济大学 | A kind of parking scale prediction control system and realization method |
-
2018
- 2018-04-18 CN CN201810348875.9A patent/CN108596381B/en active Active
Patent Citations (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN102496076A (en) * | 2011-11-30 | 2012-06-13 | 广州市交通规划研究所 | Macroscopic, mid-scope and microscopic multilevel urban parking demand prediction model integrated system |
Also Published As
Publication number | Publication date |
---|---|
CN108596381A (en) | 2018-09-28 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN107610469B (en) | Day-dimension area traffic index prediction method considering multi-factor influence | |
CN108346292B (en) | Urban expressway real-time traffic index calculation method based on checkpoint data | |
CN109558988B (en) | Electric vehicle energy consumption prediction method and system based on big data fusion | |
CN104064028B (en) | Based on public transport arrival time Forecasting Methodology and the system of multiple information data | |
CN105355049B (en) | A kind of highway evaluation of running status method based on macroscopical parent map | |
CN107958031B (en) | Resident travel OD distribution extraction method based on fusion data | |
CN102737500B (en) | Method for acquiring arrival interval reliability of urban bus | |
CN103903430B (en) | Dynamic fusion type travel time predicting method with multi-source and isomorphic data adopted | |
CN103077610A (en) | Road trip time estimating method and system | |
CN103745089A (en) | Multi-dimensional public transport operation index evaluation method | |
CN105679025B (en) | A kind of arterial street travel time estimation method based on Changeable weight mixed distribution | |
CN106845768A (en) | Bus hourage model building method based on survival analysis parameter distribution | |
CN104900057B (en) | A kind of Floating Car map-matching method in the major-minor road of city expressway | |
WO2012024976A1 (en) | Traffic information processing method and device thereof | |
CN112669595B (en) | A deep learning-based online car-hailing traffic flow prediction method | |
CN104851287A (en) | Method for urban road link travel time detection based on video detector | |
CN112036757A (en) | Parking transfer parking lot site selection method based on mobile phone signaling and floating car data | |
CN106997662A (en) | A kind of city bus operating mode construction method | |
CN108596381B (en) | Urban parking demand prediction method based on OD data | |
CN106056903A (en) | Method for detecting road congestion areas based on GPS (Global Positioning System) data | |
CN106327867A (en) | Bus punctuality prediction method based on GPS data | |
CN118152926A (en) | A real-time monitoring system for railway subgrade freezing damage and a method for predicting railway subgrade freezing damage | |
CN106980942B (en) | Method for measuring and calculating influence range of bicycle express way on public bicycle rental spots | |
CN104750919B (en) | A kind of road passage capability influence factor recognition methods | |
CN115331433B (en) | Multi-vehicle trajectory reconstruction method on urban main roads based on multi-source data |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |