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【背景】随着城市化进程加速和机动车保有量持续攀升，交通事故频发已成为制约我国社会安全发展的重要公共问题。传统研究多采用单变量模型分析死亡或受伤人数，难以揭示二者之间的联合作用机制。此外，相关文献对变量影响的时变性解析不足。【方法】构建了基于高斯Copula的双变量负二项回归模型，分时段系统评估了京津冀地级市事故死亡与受伤人数的联合分布特征及建成环境因素对交通事故伤亡的时变影响。【数据】研究分别基于2017/2018/2019/2020年京津冀地区交通事故伤亡人数，同时结合社会经济指标与兴趣点（Point Of Interest， POI）数据，最终从经济发展、人口数据、生活服务类、消费娱乐类、公司商务类、风景名胜类、公共服务类、交通服务类8个维度构成建成环境指标。【结果】研究发现，不同年份京津冀地级市交通事故死亡与受伤人数之间均存在显著的正相关性，相关系数分别为0.644/0.610/0.527/0.503，且P值均小于0.001。基于最优高斯Copula的双变量负二项回归模型结果表明，死亡人数的时间稳定性因素包括地区生产总值、人口密度、风景名胜等5个变量，其中人口密度在2020年影响最大（–0.930， P <0.001）；受伤人数的时间稳定性因素则包括人口密度、汽车服务、餐饮服务等6个变量，其中餐饮服务在2019年影响最大（1.634， P <0.001）。【应用】本文进一步拓展了交通事故伤亡数据建模的理论方法，并为京津冀区域降低人车冲突风险、调整动态通行方案、改善交通基础设施等交通安全管理与城市规划决策提供了有力支撑。
Abstract：[Background] With the acceleration of urbanization and the continuous growth of motor vehicle ownership， traffic accidents have become a critical public issue constraining social safety development in China. Traditional studies often employ univariate models focusing solely on either fatalities or injuries， which limits the understanding of their joint mechanisms. In addition， existing research provides insufficient evidence on the temporal variability of influencing factors. [Method] This study develops a bivariate negative binomial regression model based on the Gaussian Copula framework to examine the joint distribution of traffic fatalities and injuries across prefecture-level cities in the Beijing–Tianjin–Hebei region. The model further evaluates the temporal stability of the impacts of built environment characteristics on crash casualties over multiple periods. [Data] The analysis utilizes crash records for 2017–2020 from the Beijing–Tianjin–Hebei region， integrated with socioeconomic indicators and Point of Interest (POI) data. Built environment variables are constructed from eight dimensions， including economic development， demographic attributes， living services， consumption and entertainment facilities， business establishments， scenic attractions， public services， and transportation facilities. [Results] The results reveal significant positive correlations between fatalities and injuries across all years， with correlation coefficients of 0.644， 0.610， 0.527， and 0.503 (all P < 0.001). The optimal Gaussian Copula–based bivariate negative binomial models indicate that the temporally stable factors influencing fatalities include GDP， population density， and scenic attractions， among others， with population density showing the strongest effect in 2020 (–0.930， P < 0.001). For injuries， the stable factors include population density， automobile services， and catering services， with the latter showing the highest impact in 2019 (1.634， P < 0.001). [Application] This study extends the theoretical framework for modeling crash data and provides empirical insights to support traffic safety management and urban planning in the Beijing–Tianjin–Hebei region， particularly in mitigating human–vehicle conflicts， optimizing dynamic traffic control strategies， and improving transportation infrastructure.
[1] World Health Organization. Road traffic injuries[EB/OL]. (2023-12-13)[2025-09-27]. https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries/.
[2] 国家统计局. 2024年中国统计年鉴[M]. 北京: 中国统计出版社， 2024. National Bureau of Statistics of China. China statistical yearbook 2024 [M]. Beijing: China Statistics Press， 2024.
[3] 丁陈卓， 丁伟杰， 周妍， 等. 城市建成环境交通事故空间分布影响因素分析研究[J]. 警察技术， 2024(5): 89-93. DING Chenzhuo， DING Weijie， ZHOU Yan， et al. Analysis of the relationship between urban built environment and traffic accident distribution[J]. Police Technology， 2024(5): 89-93.
[4] QU X， ZHU X， XIAO X， et al. Exploring the influences of point-of-interest on traffic crashes during weekdays and weekends via multi-scale geographically weighted regression[J]. ISPRS International Journal of Geo-Information， 2021， 10(11): 791.
[5] MIRZAHOSSEIN H， ADIBI H， KHISHDARI A， et al. Evaluating geometric design characteristics of two-lane two-way rural roads on crash frequencies occurrences[J]. Iranian Journal of Science and Technology， Transactions of Civil Engineering， 2023， 47(4): 2497-2506.
[6] HARYADI B. Development of a crash frequency model of Indonesian freeways[J]. Transport Problems， 2024， 19(3): 71-80.
[7] 沈文泰. 城郊主干道交通安全风险分析方法[D]. 北京: 中国人民公安大学， 2024. SHEN Wen-tai. Traffic safety risk analysis methodology for suburban trunk road[D]. Beijing: People's Public Security University of China， 2024.
[8] AZIMIAN A， DIMITRA PYRIALAKOU V， LAVRENZ S， et al. Exploring the effects of area-level factors on traffic crash frequency by severity using multivariate space-time models[J]. Analytic Methods in Accident Research， 2021， 31: 100163.
[9] ZENG Q， WANG F， WANG Q， et al. Bayesian multivariate spatial modeling for crash frequencies by injury severity at daytime and nighttime in traffic analysis zones[J]. Transportation Letters， 2023， 15(6): 553-560.
[10] ZHANG P， WANG C， CHEN F， et al. A random-parameter negative binomial model for assessing freeway crash frequency by injury severity: daytime versus nighttime[J]. Sustainability， 2022， 14(15): 9061.
[11] KITALI A E， KIDANDO E， ASIF RAIHAN M， et al. Exploring the need to model two- and multiple-vehicle crashes separately[J]. Transportation Research Record: Journal of the Transportation Research Board， 2022， 2676(1): 622-636.
[12] XU X， HARDIN J W. Regression models for bivariate count outcomes[J]. The Stata Journal: Promoting Communications on Statistics and Stata， 2016， 16(2): 301-315.
[13] AHMAD N， GAYAH V V， DONNELL E T. Copula-based bivariate count data regression models for simultaneous estimation of crash counts based on severity and number of vehicles[J]. Accident Analysis & Prevention， 2023， 181: 106928.
[14] MANNERING F. Temporal instability and the analysis of highway accident data[J]. Analytic Methods in Accident Research， 2018， 17: 1-13.
[15] ZHANG S. Road traffic safety in developing countries: taking China as an example[J]. Iranian Journal of Public Health， 2024， 53(2): 356-366.
[16] TANG X， BI R， WANG Z. Spatial analysis of moving-vehicle crashes and fixed-object crashes based on multi-scale geographically weighted regression[J]. Accident Analysis & Prevention， 2023， 189: 107123.
[17] 王虹霞， 平思亮. 驾驶风格对交通事故影响分析[J]. 汽车实用技术， 2022， 47(22): 172-175. WANG Hongxia， PING Siliang. Analysis of the influence of driving style on traffic accidents[J]. Automobile Applied Technology， 2022， 47(22): 172-175.
[18] 李聪颖， 吴佳西， 张洪涛， 等. 建成环境对城市机动车出行时空异质影响研究[J]. 交通运输工程与信息学报， 2024， 22(4): 52-66. LI Congying， WU Jiaxi， ZHANG Hongtao， et al. Spatio-temporal heterogeneous effects of built environment on urban motorized travel[J]. Journal of Transportation Engineering and Information， 2024， 22(4): 52-66.
[19] 毛新博， 高歌， 李甜， 等. 考虑交互效应的建成环境对网约车时空分异影响分析[J]. 交通运输工程与信息学报， 2025， 23(2): 16-31. MAO Xinbo， GAO Ge， LI Tian， et al. Effect of built environment considering interaction effect on spatiotem poral differentiation of online car-hailing trips[J]. Journal of Transportation Engineering and Information， 2025， 23(2): 16-31.
[20] 阙祖琛. 基于多源数据的城市交通事故风险评估与事故严重程度建模[D]. 杭州: 浙江大学， 2023. QUE Zu-chen. Risk assessment and accident severity modeling of urban traffic accidents based on multi-source data[D]. Hangzhou: Zhejiang University， 2023.
[21] UMAIR M， AHMAD RANA I， LODHI R H. The impact of urban design and the built environment on road traffic crashes: a case study of Rawalpindi， Pakistan[J]. Case Studies on Transport Policy， 2022， 10(1): 417-426.
[22] PERVEZ A， LEE J， HUANG H. Exploring factors affecting the injury severity of freeway tunnel crashes: a random parameters approach with heterogeneity in means and variances[J]. Accident Analysis & Prevention， 2022， 178: 106835.
[23] FU C， SAYED T. Multivariate Bayesian hierarchical Gaussian copula modeling of the non-stationary traffic conflict extremes for crash estimation[J]. Analytic Methods in Accident Research， 2021， 29: 100154.
[24] NIEHAUS J M， ZHU L， COOK S J， et al. bizicount: bivariate zero-inflated count copula regression using R[J]. Journal of Statistical Software， 2024， 109(1): 1-42.
[25] 百度地图. 2020年度中国城市交通报告[EB/OL]. (2021-01)[2025-11-27]. https://huiyan.baidu.com/boswebsite/jiaotong/cms/reports/traffic/2020annualtrafficreport/.
[26] 北京市人民政府国有资产监督管理委员会. 北京公共交通控股（集团）有限公司[EB/OL]. (2024-06-17)[2025-11-27]. https://gzw.beijing.gov.cn/yggq/jgqy/201812/t20181220＿9793.html.
[27] 天津市交通运输委员会. 天津市综合运输服务“十四五”发展规划[EB/OL]. (2021-11)[2025-11-27]. https://jtys.tj.gov.cn/ZMHD5819/jcyjzj/202111/t20211101＿5670714.html type=2.
[28] 北京交通发展研究院. 2021北京交通发展年度报告[EB/OL]. (2021-09-03)[2025-11-27]. https://www.bjtrc.org.cn/List/index/cid/7.html.
[29] LI J， LI C， ZHAO X， et al. Do road network patterns and points of interest influence bicycle safety Evidence from dockless bike sharing in China and policy implications for traffic safety planning[J]. Transport Policy， 2024， 149: 21-35.
[30] 国务院办公厅. 国务院办公厅关于加快发展流通促进商业消费的意见[EB/OL]. (2019-08-27)[2025-11-27]. https://www.gov.cn/zhengce/zhengceku/2019-08/27/content＿5424989.htm.
[31] 北京市统计局. 北京市2020年国民经济和社会发展统计公报[EB/OL]. (2021-03-12)[2025-11-27]. https://www.beijing.gov.cn/gongkai/shuju/tjgb/202103/t20210312＿2305454.html.
[32] 天津市统计局. 2020年天津市国民经济和社会发展统计公报[EB/OL]. (2021-03-15)[2025-11-27]. https://www.tj.gov.cn/sq/tjgb/202103/t20210315＿5384328.html.
[33] 河北省统计局. 河北省2020年国民经济和社会发展统计公报[EB/OL]. (2021-02-25)[2025-11-27]. http://tjj.hebei.gov.cn/hbstjj/sj/tjgb/101702088630989.html.
[34] CHUNG H， DUAN Q， CHEN Z， et al. Investigating the effects of POI-based land use on traffic accidents in Suzhou Industrial Park， China[J]. Case Studies on Transport Policy， 2023， 12: 100933.
基本信息:
DOI：10.19961/j.cnki.1672-4747.2025.09.033
中图分类号:U491.31
引用信息:
[1]陈鹏,宋栋栋,支丹月,等.建成环境对交通事故伤亡的时变影响—基于Copula的双变量方法[J].交通运输工程与信息学报().DOI:10.19961/j.cnki.1672-4747.2025.09.033.
基金信息:
中国博士后科学基金资助项目（2025M771633）; 陕西省自然科学基础研究计划项目（2025JC-YBQN-558）; 长安大学中央高校基本科研业务专项资金资助项目（300102225101）
2025-12-19
2025-12-19
2025-12-19