城市交通拥塞因子时空变化特征及源解析研究

doi:10.16097/j.cnki.1009-6744.2023.03.031

摘要/Abstract

摘要： 交通拥塞因子是造成“点-线-面”三级城市交通拥塞效应的重要源。为深入探究城市交通拥塞因子的时空变化特征和影响程度，本文引入用于地质和生态风险评价的相关方法。首先，基于“源-路径-目标”模型，综合考虑城市交通拥塞多变综合复杂性，深入剖析建立城市交通拥塞评价影响指标体系；其次，采用Mann-Kendall 趋势检验法评估不同交通拥塞因子的时空变化趋势和突变点，利用小波分析探究周期性变化规律；改进内梅罗污染指数法对交通拥塞因子的特征及风险程度进行评估；最后，采用person相关性分析和正定矩阵因子分解(PMF)模型分析其主要来源，评估结果的不确定性。结果表明：超车、违反交通规则、随意变换车道、停车、违法违规占用道路、不良天气状况、突发交通事故7个因子的大小变化较为明显，变异系数均超过40%；城市交通拥塞程度因子Ʃ18Bi在1月份的早、午、晚高峰最大(11.23)，3月份达到最低(8.12)。城市交通拥塞整体呈现晚高峰＞早高峰＞中高峰的时间变化特征，且存在8a(第一主周期)和4a(第二主周期)2个周期时间变化。内梅罗污染指数法得到的结果以中等及以上程度交通拥塞为主。研究结果可为城市交通拥塞节点治理提供科学依据。

关键词: 城市交通, 风险评价模型, 源解析, 城市交通拥塞, 时空变化特征, 不确定性分析

Abstract: The traffic congestion factor is an important source of urban traffic congestion effect at the "point-line surface" level. In order to investigate the spatial and temporal characteristics of urban traffic congestion factors and their influence degree, this paper introduces the relevant methods for geological and ecological risk evaluation. Firstly, based on the "source-path-target" model, the multivariate and comprehensive complexity of urban traffic congestion is considered, and the impact index system of urban traffic congestion evaluation is analyzed in depth. to explore the law of periodic changes; improve the Nemero pollution index method to assess the characteristics and risk degree of traffic congestion factors; finally, use person correlation analysis and positive definite matrix factor decomposition (PMF) model to analyze their main sources and assess the uncertainty of the results. The results showed that the magnitude of seven factors, namely, overtaking, traffic rule violation, random lane change, parking, illegal and illegal road occupation, adverse weather conditions, and sudden traffic accidents, varied more significantly, with coefficients of variation exceeding 40%; the urban traffic congestion degree factor Ʃ18Bi was the largest in the morning, afternoon, and evening peaks in January (11.23) and reached the lowest in March ( 8.12). The overall urban traffic congestion shows a time-varying characteristic of evening peak > morning peak > mid-peak, and there are 2 cycles of time variation of 8a (first main cycle) and 4a (second main cycle). The results obtained by the Nemero pollution index method are dominated by moderate and above traffic congestion. The results of the study can provide a scientific basis for urban traffic congestion node management.

Key words: urban traffic, risk assessment model, source analysis, urban traffic congestion, spatiotemporal variation characteristics, uncertainty analysis

中图分类号:

U121

赵雪亭, 胡立伟. 城市交通拥塞因子时空变化特征及源解析研究[J]. 交通运输系统工程与信息, 2023, 23(3): 300-310.

ZHAO Xue-ting, HU Li-wei. Spatial and Temporal Variation Characteristics of Urban Traffic Congestion Factors and Source Analysis[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(3): 300-310.

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http://www.tseit.org.cn/CN/Y2023/V23/I3/300

参考文献

[1] LIZBETIN J, BARTUSKA L. The influence of human factor on congestion formation on urban roads[J]. Procedia engineering, 2017, 187: 206-211.

[2] RAHMAN M M, NAJAF P, FIELDS M G, et al. Traffic congestion and its urban scale factors: Empirical evidence from American urban areas[J]. International Journal of Sustainable Transportation, 2022, 16(5): 406-421.

[3] ZHOU Y, MAO S, ZHAO H, et al. How rainfalls influence urban traffic congestion and its associated economic losses at present and in future: taking cities in the Beijing-Tianjin-Hebei region, China for example?[J]. Theoretical and Applied Climatology, 2022, 150(1-2):537-550.

[4] ZHANG M, LIU Y, XIAO Y, et al. Vulnerability and resilience of urban traffic to precipitation in China[J]. International journal of environmental research and public health, 2021, 18(23): 12342.

[5] SUN C, LU J. The Relative Roles of Socioeconomic Factors and Governance Policies in Urban Traffic Congestion: A Global Perspective[J]. Land, 2022, 11(10): 1616.

[6] MOYANO A, STEPNIAK M, MOYA-GóMEZ B, et al. Traffic congestion and economic context: changes of spatiotemporal patterns of traffic travel times during crisis and post-crisis periods[J]. Transportation, 2021, 48(6): 3301-3324.

[7] 胡立伟, 杨锦青, 何越人, 等. 基于改进 BP 神经网络的城市交通拥塞环境下车辆运行风险识别研究[J]. 公路交通科技, 2019, 36(10): 105-113. [HU L W, YANG J Q, HE Y R, et al. Study on Vehicle Operational Risk Identification in Urban Traffic Congestion Based on Improved BP Neural Network[J]. Journal of Highway and Transportation Research and Development, 2019, 36(10):105-113.]

[8] 孙建平, 郭继孚, 张溪, 等. 基于速度变化的偶发性交通拥堵时空分布特性研究[J]. 交通运输系统工程与信息, 2019, 19(2): 196-201. [SUN J P, GUO J F, ZHANG X, et al. Spatial and Temporal Distribution of Occasional Congestion Based on Speed Variation[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(2):196-201.]

[9] 韦清波, 何兆成, 郑喜双, 等. 考虑多因素的城市道路交通拥堵指数预测研究[J]. 交通运输系统工程与信息, 2017, 17(1): 74-81. [WEI Q B, HE Z C, ZHENG X S, et al. Prediction of Urban Traffic Performance Index Considering Multiple Factors [J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(1):74-81.]

[10] 王晓旭, 王丽珍, 王家龙.交通数据的时空并置模糊拥堵模式挖掘[J].清华大学学报(自然科学版),2020,60(8):683-692. [WANG X X, WANG L Z, WANG J L, et al. Mining spatio-temporal co-location fuzzy congestion patterns from traffic datasets[J]. Journal of Tsinghua University (Science and Technology), 2020, 60(8):683-692.]

[11] 张光南, 钟俏婷, 杨清玄. 交通违法事故时空分布特征及其影响因素——以广州市为例[J]. 交通运输系统工程与信息, 2019, 19(3): 208-214. [ZHANG G N, ZHONG Q T, YANG Q X. Temporal-spatial Characteristics and Influencing Factors of At-fault Traffic Crashes:A Case Study in Guangzhou [J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(3):208-214.]

[12] ANDREO B, GOLDSCHEIDER N, VADILLO I, et al. Karst groundwater protection: First application of a Pan-European approach to vulnerability, hazard and risk mapping in the Sierra de Líbar (Southern Spain) [J]. Science of the Total Environment. 2006,357(1-3):54-73.

[13] WANG Z, HUA P, LI R, et al. Concentration decline in response to source shift of trace metals in Elbe River, Germany: a long-term trend analysis during 1998–2016[J]. Environmental pollution, 2019, 250: 511-519.

[14] SU X Q, AN J L, ZHANG Y X, et al. Prediction of ozone hourly concentrations by support vector machine and kernel extreme learning machine using wavelet transformation and partial least squares methods[J]. Atmospheric Pollution Research, 2020, 11(6):51-60.

[15] YAO Y, HE C, LI S, et al. Properties of particulate matter and gaseous pollutants in Shandong, China: Daily fluctuation, influencing factors, and spatiotemporal distribution[J]. Science of The Total Environment, 2019, 660: 384-394.

[16] 陈佳木, 吴志华, 刘文浩, 等. 湖南水口山多金属矿区废石堆重金属污染评价及赋存形态分析[J]. 地球科学, 2021, 46(11): 4127-4139. [CHEN J M, WU Z H, LIU W H. Heavy Metal Pollution Evaluation and Species Analysis of Waste Rock Piles in Shuikoushan, Hunan Province [J]. Earth Science, 2021, 46(11):4127-4139.]

[17] WANG Z, SHEN Q, HUA P, et al. Characterizing the anthropogenic-induced trace elements in an urban aquatic environment: A source apportionment and risk assessment with uncertainty consideration[J]. Journal of Environmental Management, 2020, 275: 111288.

[18] DONG L, ZHANG J. Predicting polycyclic aromatic hydrocarbons in surface water by a multiscale feature extraction-based deep learning approach[J]. Science of The Total Environment, 2021, 799: 149509.

[19] BROWN S G, EBERLY S, PAATERO P, et al. Methods for estimating uncertainty in PMF solutions: Examples with ambient air and water quality data and guidance on reporting PMF results[J]. Science of the Total Environment, 2015, 518: 626-635.