[1]赵志远,郁勋剑,黄永刚,等.顾及街景信息的城市交通违法行为影响因素分析[J].华侨大学学报(自然科学版),2023,44(6):759-768.[doi:10.11830/ISSN.1000-5013.202307002]
　ZHAO Zhiyuan,YU Xunjian,HUANG Yonggang,et al.Analysis of Influencing Factors of Urban Traffic Violations Considering Street View Information[J].Journal of Huaqiao University(Natural Science),2023,44(6):759-768.[doi:10.11830/ISSN.1000-5013.202307002]
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顾及街景信息的城市交通违法行为影响因素分析()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第44卷
期数:
2023年第6期
页码:
759-768
栏目:
出版日期:
2023-11-20

文章信息/Info

Title:
Analysis of Influencing Factors of Urban Traffic Violations Considering Street View Information
文章编号:
1000-5013(2023)06-0759-10
作者:
赵志远1; 2; 3;  郁勋剑1; 2;  黄永刚1; 2;  吴升1; 2; 3
1. 福州大学 数字中国研究院(福建), 福建 福州 350108;2. 福州大学 空间数据挖掘与信息共享教育部重点实验室, 福建 福州 350002;3. 福建省数字经济联盟, 福建 福州 350003
Author(s):
ZHAO Zhiyuan1; 2; 3;  YU Xunjian1; 2;  HUANG Yonggang1; 2;  WU Sheng1; 2; 3
1. Academy of Digital China(Fujian), Fuzhou University, Fuzhou 350108, China; 2. Key Laboratory of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou 350002, China; 3. The Digital Economy Alliance of Fujian Pro
关键词:
城市交通违法行为;  地理环境特征指标体系;  街景数据;  影响因素
Keywords:
urban traffic violations;  geographical environment characteristic indicator system;  street view data;  influencing factors
分类号:
TP311.13;D922.14
DOI:
10.11830/ISSN.1000-5013.202307002
文献标志码:
A
摘要:
以2017年福州市交通违法数据为例,综合利用街景、路网和兴趣点等数据,构建三类地理环境特征指标体系,利用多元线性回归、岭回归模型和地理探测器,定量分析地理环境特征指标与城市交通违法行为之间的关系。结果表明:绿地广场用地、商服设施用地、交通服务设施密度、人车空间配比、道路拥挤指数与机动车交通违法行为关系密切,解释程度超过50%;居住用地、交叉口密度、公共管理与公共服务用地与非机动车交通违法行为关系密切,解释程度超过30%;公共管理与公共服务用地对两种典型交通违法行为的影响均较大,而土地利用熵、交叉口密度对行人和非机动车违反交通信号灯通行违法行为的影响更强;地理环境特征指标对不同交通违法行为解释程度的差异随着违法区域和违法类型等属性的变化而变化;街景数据所反映的局部空间环境因素提高了对机动车违法行为的解释程度,但对非机动车违法行为不明显。
Abstract:
Taking the traffic violation data of Fuzhou City in 2017 as an example, three types of geographical environment characteristic indicator systems are constructed by comprehensively utilizing data such as data of street view, data of road network, and data of interest points. Multiple linear regression, ridge regression models, and geographic detectors are used to quantitatively analyze the relationship between geographical environment characteristic indicators and urban traffic violations. The results show that there is a close relationship between the land of green squares, the land of commercial service facilities, traffic service facility density, pedestrian and vehicle space ratio, road congestion index, and motor vehicle traffic violations, with an explana-tion degree exceeding 50%. Residential land, intersection density, public management and public service land are closely related to non-motor vehicle traffic violations, with an explanation degree exceeding 30%. Public management and public service land have a significant impact on both typical traffic violations which violating traffic lights for passage, while land use entropy and intersection density has a stronger impact on illegal behavior of pedestrians and non motorized vehicles violating traffic lights. The differences in the degree of interpretation of different traffic violations by geographical environmental characteristic indicators vary with the changes in attributes such as illegal areas and types of violations. The local spatial environmental factors reflected in data of street view have increased the degree of explanation for motor vehicle violations, but are not significant for non-motor vehicle violations.

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备注/Memo

备注/Memo:
收稿日期: 2023-07-04
通信作者: 赵志远(1989-),男,副研究员,主要从事时空大数据分析与挖掘的研究。E-mail:zyzhao@fzu.edu.cn.
基金项目: 国家自然科学基金资助项目(42201500); 福建省中青年教师教育科研项目(JAT210012); 空间数据挖掘与信息共享教育部重点实验室开放基金资助项目(2022LSDMIS03)http://www.hdxb.hqu.edu.cn
更新日期/Last Update: 2023-11-20