吉林大学学报(工学版) ›› 2024, Vol. 54 ›› Issue (9): 2520-2530.doi: 10.13229/j.cnki.jdxbgxb.20221460

• 交通运输工程·土木工程 • 上一篇    

建成环境对路内停车泊位使用率的非线性影响模型

周锡浈1,2(),宫贺3,李敦敦4,季彦婕1,2(),严杰1,2   

  1. 1.东南大学 交通学院，南京 211189
    2.东南大学 现代城市交通技术江苏高校协同创新中心，南京 210096
    3.常熟市公安局 交通警察大队，江苏 常熟 215505
    4.江南爱停车科技有限公司，江苏 常熟 215500
  • 收稿日期:2023-02-28 出版日期:2024-09-01 发布日期:2024-10-28
  • 通讯作者: 季彦婕 E-mail:zhouxizhen@seu.edu.cn;jiyanjie@seu.edu.cn
  • 作者简介:周锡浈（1995-），女，博士研究生.研究方向：智慧交通大数据挖掘与建模.E-mail：zhouxizhen@seu.edu.cn
  • 基金资助:
    国家重点研发计划项目(2018YFB1600900)

Nonlinear model for impact of built environment on curb parking spaces occupancy

Xi-zhen ZHOU1,2(),He GONG3,Dun-dun LI4,Yan-jie JI1,2(),Jie YAN1,2   

  1. 1.School of Transportation，Southeast University，Nanjing 211189，China
    2.Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies，Southeast University，Nanjing 210096，China
    3.Public Security Bureau of Changshu，Traffic Police Brigade，Changshu 215505，China
    4.Genland Ipark Technology Co. ，Ltd. ，Changshu 215500，China
  • Received:2023-02-28 Online:2024-09-01 Published:2024-10-28
  • Contact: Yan-jie JI E-mail:zhouxizhen@seu.edu.cn;jiyanjie@seu.edu.cn

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摘要：

为精细化把握微观空间尺度下的路内停车需求规律，针对工作日和非工作日两种典型场景，分别构建了分位数回归模型，揭示了土地利用、停车站点属性、交通因素、社会人口和经济对路内停车泊位使用率的非线性影响。研究结果表明：分位数回归模型捕捉研究变量间复杂关系的性能显著优于传统线性回归模型；解释变量的回归系数和显著性水平随着分位数的变化而变化，且工作日和非工作日存在明显异质性。例如，停车站点周边公交站的数量与低、高泊位使用率密切相关，而该变量在中等泊位使用率下无统计学意义；工作日餐饮类兴趣点对路内停车泊位使用率的影响并不显著。

关键词: 交通运输系统工程, 路内停车泊位, 使用率, 分位数回归模型, 非线性影响

Abstract:

To finely grasp the curb parking demand pattern at a micro-spatial scale， quantile regression models for two typical scenarios， namely workdays and weekends were proposed， to explore the nonlinear effects of land utilization， curb parking spaces， traffic factors， and socioeconomic and population on curb parking space occupancy. The results show that quantile regression is superior to linear regression in capturing the complex relationship between variables. And the coefficients and significance levels of explanatory variables change as the quantile changes. At the same time， there is an obvious heterogeneity between workdays and weekends. For example， the number of bus stations around the parking sites is closely related to the low and high occupancies， but this variable has no statistical significance under the medium occupancy. The catering services POIs do not significantly influence curb parking space occupancy on workdays.

Key words: engineering of communication and transportation system, curb parking, occupancy, quantile regression, non-linear impact

中图分类号: 

  • U491.1

图1

研究区域"

表1

自变量和因变量的描述性统计"

变量解 释平均值标准差
因变量
工作日泊位使用率工作日的路内停车泊位小时使用率/%33.9524.72
非工作日泊位使用率非工作日的路内停车泊位小时使用率/%33.5325.56
土地利用
餐饮类缓冲区内餐饮类（中餐厅、外国餐厅、咖啡厅等）兴趣点的密度/（个·km-2）31.5826.2
住宅类缓冲区内住宅类（小区、宾馆、别墅等）兴趣点的密度/（个·km-2）9.445.51
工作类缓冲区内工作类（公司企业、商务大厦等）兴趣点的密度/（个·km-2）21.6815.92
休闲类缓冲区内休闲类（运动场馆、娱乐场所等）兴趣点的密度/（个·km-2）16.3317.67
医疗服务类缓冲区内医疗服务类（综合医院、专科医院等）兴趣点的密度/（个·km-2）8.716.42
科教类缓冲区内科教类（中学、小学、大学等）兴趣点的密度/（个·km-2）7.585.29
景区类缓冲区内景区类（公园广场、风景名胜等）兴趣点的密度/（个·km-2）4.817.04
购物类缓冲区内购物类（商场、家电卖场等）兴趣点的密度/（个·km-2）84.3771.4
行政机构类缓冲区内行政机构类（政府机关、公检法机构等）兴趣点的密度/（个·km-2）18.1013.11
土地利用混合度缓冲区内用地类型的熵指数0.580.08
交通因素
公交站密度缓冲区内公交站的密度/（个·km-2）40.18105.45
交叉口密度缓冲区内交叉口的密度/（个·km-2）57.3340.8
路网密度缓冲区内道路长度的密度/（km·km-2）24.517.69
停车站点因素
首个收费率停车站点第一个收费时段的费率/（元·小时）5.811.81
免费停车时长停车站点的免费停车时长/分34.7114.34
最高收费上限停车站点处的24小时的收费上限/元38.5417.43
站点泊位数停车站点所提供的泊位数/个44.2724.75
时段订单发生时间所处时段11.686.83
社会人口和经济
平均房价缓冲区内的平均房价/（千元·km-2）18.546.20
人口密度缓冲区内的人口密度（百人·km-2）167.64134.91

表2

工作日场景下分位数回归模型解释变量系数"

解释变量OLS10th30th50th70th80th90th
修正的多元相关系数0.4970.3710.3870.4010.4450.4830.531
餐饮类-0.0160.035*-0.035-0.0220.0370.0190.018
住宅类1.232***-0.695***-0.476***1.804***1.769***1.807***1.505***
工作类0.255**0.333***0.238***0.084**0.063*0.019-0.012
休闲类0.082*-0.265***-0.214***0.280***0.209***0.169**0.205**
医疗服务类0.338***0.179***0.244***0.246***0.272**0.392***0.243**
科教类-0.232***0.368***0.013-0.597***-0.319**-0.10220.061
景区类-0.479***-0.697***-1.125***-0.289**-0.171-0.276**-0.774***
购物类0.0090.043***0.168***-0.008-0.035**-0.054**-0.019
行政机构类0.190**-0.0855-0.077**0.459***0.532***0.532***0.610***
土地利用混合度39.418**1.091100.886**15.6235.428-0.91023.495*
公交站密度0.013*-0.047***-0.034***0.032***0.059***0.106***0.114***
交叉口密度0.172**0.077***0.129***0.203***0.152***0.160***0.249***
路网密度-1.392**0.109-0.722***-1.826***-1.690***-1.546***-1.432***
首个收费率-1.980***1.935***3.159***-5.376***-8.327***-8.096***-7.372***
免费停车时长0.135**-0.247***-0.582***0.152***0.409***0.322***0.294***
最高收费上限0.122***-0.093***-0.117***0.322***0.551***0.495***0.367***
时段0.426***0.168***0.372***0.520***0.552***0.498***0.369***
站点泊位数-0.204**0.073***0.096***-0.135***-0.312***-0.370***-0.396***
平均房价0.389**0.097***0.147***0.285***0.643***0.714***0.707***
人口密度-0.045**0.017***0.010***-0.058***-0.084***-0.089***-0.092***
常数项20.053**-6.531**-40.453***45.766**66.985**80.751**74.644**

表3

非工作日场景下分位数回归模型解释变量系数"

解释变量OLS10th30th50th70th80th90th
修正的多元相关系数0.4780.3760.4080.4060.4490.4860.522
餐饮类0.161***0.073***0.073**0.079*0.315***0.305**0.407*
住宅类0.952***-1.011***-0.932***0.799***2.274***1.915***1.484***
工作类0.156***0.207***0.121***0.051-0.010-0.001-0.074*
休闲类0.061-0.320***-0.324***0.114**0.212**0.112-0.087
医疗服务类-0.249**0.212***0.03-0.251**-0.118-0.181-0.400**
科教类-0.267**0.373***-0.113-0.791***-0.667***-0.0860.229
景区类-0.256**-0.617***-1.378***-0.690***0.0150.2460.280*
购物类0.0010.048***0.212***0.098***-0.091**-0.095**-0.087**
行政机构类0.191**0.031-0.127***0.309***0.613***0.550***0.505***
土地利用混合度2.0137.830114.927**33.697**-76.897**-83.045**-70.696**
公交站密度0.009-0.026***-0.032***-0.0070.046***0.079***0.105***
交叉口密度0.127**0.118***0.118***0.135***0.153***0.086**0.115**
路网密度-1.191***-0.254***-0.500***-1.183***-1.917***-1.539***-1.255***
首个收费率-1.521**1.883***5.755***0.926*-6.209***-6.250***-5.432***
免费停车时长-1.521-0.335***-0.882***-0.520***0.258***0.292**0.287**
最高收费上限0.261**-0.094***-0.136***0.260***0.599***0.572***0.106*
时段0.401**0.091**0.329***0.494***0.568***0.484***0.368***
站点泊位数-0.245**0.065***0.068***-0.107*-0.373**-0.438**-0.523**
平均房价0.593*0.084**0.298***0.395***1.199**1.132**0.965**
人口密度-0.050**0.022***0.008**-0.060**-0.090**-0.091**-0.092**
常数项39.848**-0.469-52.725**21.470**101.511***113.765***123.322***

图2

分位数回归模型中交通因素变量的估计值"

图3

分位数回归模型中路内停车站点因素变量的估计值"

图4

分位数回归模型中土地使用因素变量的估计值"

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