-
摘要: 分析了影响出行决策的交通质量因素及重要程度, 筛选了10个主要因素, 运用需求层次模型构建了交通质量因素基本体系, 利用探索性因子分析法构建了交通质量因素结构体系分析模型, 基于结构方程模型模拟了出行者个体特征、成本约束、出行决策及交通质量因素之间的影响关系, 基于出行意愿调查数据进行试验分析, 得到了交通质量因素的二阶层次结构体系和结构方程模型路径系数图, 分析了交通质量因素对出行决策影响程度的综合路径系数与不同城市等级交通质量因素重要性的数值特征。分析结果表明: 交通质量因素结构体系的前2个因子共解释了84.9%的总方差, 且2个因子载荷系数均大于0.6, 表明交通质量因素结构体系具有合理性; 试验数据的克朗巴哈系数为0.86, 效度检验系数为0.84, 具有较高的信度及效度; 结构方程模型的复核效度整体平均值为86.9%, 且各个路径系数的复核效度均大于80%, 模型对任意样本适用性较好; 按照重要度排序, 交通质量前4个因素依次为可靠性、快捷性、经济性、舒适性, 综合路径系数分别为0.78、0.73、0.67、0.60;不同城市等级的交通质量因素重要度具有差异性, 超大型城市最重要的因素是可靠性, 路径系数为1.44, 而小城市最重要的因素是舒适性, 路径系数为1.72。可见, 针对城市居民对交通质量因素感知特征制定相应的改善政策, 可提高交通质量改善的效率和有效性。Abstract: The traffic quality factors influencing trip decision and their importance were analyzed, ten primary factors were selected, and a basic system of traffic quality factors was constructed according to the need hierarchy model.A model analyzing the structural system of traffic quality factors was established by using the exploratory factor analysis method.The influencing relationships among personal features, cost constrains, trip decision and traffic quality factors were simulated and quantified according to the structural equation model.Using the travel intention survey data in an experimental analysis, a second-order hierarchical structure of traffic quality factors and a path coefficient diagram of structural equation model were established.The comprehensive route coefficient was analyzed to show the impact degrees of traffic quality factors on trip decision, and the numerical features of the importances of traffic quality factors indifferent urban levels were studied.Analysis result shows that the first two factors in the structural system of traffic quality factors explain 84.9% of the total variance, and the factor load coefficients of the two factors all exceed 0.6, which indicates that the structure system of traffic quality factors is rational.The Cronbach'sαand the validity check coefficient of test data are 0.86 and 0.84, respectively, which shows that the test data have good reliability and validity.The total average value of composite validities in structural equation model is 86.9%, and the composite validities of path coefficients all exceed 80%, which shows that the model can adapt to random sample well.According to the importance degrees, the first four factors of traffic quality are successively reliability, velocity, economy and comfort, and the comprehensive path coefficients are 0.78, 0.73, 0.67, 0.60, respectively.Furthermore, the importance degrees of traffic quality factors are different in different urban levels.The most important factor in superlarge city is reliability with a path coefficient 1.44, while the most important factor in small city is comfort with a path coefficient 1.72.Therefore, it will achieve higher efficiency and effectiveness for improving traffic quality when making improving policies according to urban citizens' perceived features of traffic quality factors.
-
图 1 交通质量影响因素结构体系
Figure 1. Structural system of influencing factors of traffic quality
图 4 影响因素综合路径系数排序
Figure 4. Sequence of the integrated path coefficients for influencing factors
表 8 不同城市等级交通质量因素路径系数
Table 8. Path coefficients of traffic quality factors in different urban levels
下载: 导出CSV
-
[1] ELEFTERIADOU L, DOWLING R G, RYUS P. Exploring multimodal analysis in the highway capacity manual 2010[J]. Institute of Transportation Engineers, 2015, 85 (2): 27-31. [2] KITA H, KOUCHI A. A utility-based evaluation method on the perceived quality of traffic service[J]. Procedia-Social and Behavioral Sciences, 2011, 16 (1): 820-831. [3] DAS S, PANDIT D. Determination of level-of-service scale values for quantitative bus transit service attributes based on user perception[J]. Transportmetrica A: Transport Science, 2015, 11 (1): 1-21. doi: 10.1080/23249935.2014.910563 [4] CHANG T H, TSENG J S, YE Y H. Green safety index representing traffic levels of service for online application[J]. IET Intelligent Transport Systems, 2016, 10 (6): 421-427. doi: 10.1049/iet-its.2015.0086 [5] 郑玲钰, 赵益, 王忠宇, 等. 基于用户感知的城市道路交通服务水平评价方法[J]. 同济大学学报: 自然科学版, 2016, 44 (5): 753-757. https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ201605015.htmZHENG Ling-yu, ZHAO Yi, WANG Zhong-yu, et al. Level of service evaluation of urban streets based on user perception[J]. Journal of Tongji University: Natural Science, 2016, 44 (5): 753-757. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ201605015.htm [6] 杨富社, 邹群, 李明, 等. 基于行程速度指数的城市快速路服务水平评价方法[J]. 交通运输工程学报, 2014, 14 (5): 68-73. http://transport.chd.edu.cn/article/id/201405009YANG Fu-she, ZOU Qun, LI Ming, et al. Evaluation method of urban expressway service level based on traveling speed index[J]. Journal of Traffic and Transportation Engineering, 2014, 14 (5): 68-73. (in Chinese). http://transport.chd.edu.cn/article/id/201405009 [7] AHMAD F, MAHMUD S A, YOUSAF F Z. Shortest processing time scheduling to reduce traffic congestion in dense urban areas[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 47 (5): 838-855. doi: 10.1109/TSMC.2016.2521838 [8] JO Y, KIM Y, JUNG I. Variable speed limit to improve safety near traffic congestion on urban freeways[J]. International Journal of Fuzzy Systems, 2012, 14 (2): 278-288. [9] SUN Hui-jun, WU Jian-jun, MA Dan, et al. Spatial distribution complexities of traffic congestion and bottlenecks in different network topologies[J]. Applied Mathematical Modelling, 2014, 38 (2): 496-505. doi: 10.1016/j.apm.2013.06.027 [10] HUANG Yi-sheng, WENG Yi-shun, WU Wei-min, et al. Control strategies for solving the problem of traffic congestion[J]. IET Intelligent Transport Systems, 2016, 10 (10): 642-648. doi: 10.1049/iet-its.2016.0003 [11] 何兆成, 周亚强, 余志. 基于数据可视化的区域交通状态特征评价方法[J]. 交通运输工程学报, 2016, 16 (1): 133-140. http://transport.chd.edu.cn/article/id/201601016HE Zhao-cheng, ZHOU Ya-qiang, YU Zhi. Regional traffic state evaluation method based on data visualization[J]. Journal of Traffic and Transportation Engineering, 2016, 16 (1): 133-140. (in Chinese). http://transport.chd.edu.cn/article/id/201601016 [12] YOUNES M B, BOUKERCHE A. A performance evaluation of an efficient traffic congestion detection protocol (ECODE) for intelligent transportation systems[J]. Ad Hoc Networks, 2015, 24 (2): 317-336. [13] ANDRD P, ROBIN L. Traffic congestion pricing methodologies and technologies[J]. Transportation Research Part C: Emerging Techologies, 2011, 19 (6): 1377-1399. doi: 10.1016/j.trc.2011.02.010 [14] CHOW A H F, ALEX S, IOANNIS T, et al. Empirical assessment of urban traffic congestion[J]. Journal of Advanced Transportation, 2014, 48 (8): 1000-1016. doi: 10.1002/atr.1241 [15] ZHENG Z, AHN S, CHEN D, et al. Applications of wavelet transform for analysis of freeway traffic: Bottlenecks, transient traffic, and traffic oscillations[J]. Transportation Research Part B: Methodological, 2011, 45 (2): 372-384. doi: 10.1016/j.trb.2010.08.002 [16] MALIK M H, JAMIL M, KHAN M N, et al. Formal modelling of TCP congestion control mechanisms ECN/RED and SAP-LAW in the presence of UDP traffic[J]. EURASIP Journal on Wireless Communications and Networking, 2016, 2016 (1): 1-12. doi: 10.1186/s13638-015-0498-8 [17] 杨浩雄, 李金丹, 张浩, 等. 基于系统动力学的城市交通拥堵治理问题研究[J]. 系统工程理论与实践, 2014, 34 (8): 2135-2143. https://www.cnki.com.cn/Article/CJFDTOTAL-XTLL201408024.htmYANG Hao-xiong, LI Jin-dan, ZHANG Hao, et al. Research on the governance of urban traffic jam based on system dynamics[J]. Systems Engineering—Theory and Practice, 2014, 34 (8): 2135-2143. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XTLL201408024.htm [18] 刘有军, 田聪. 不同路网形态下城市交通拥堵特性分析[J]. 中国公路学报, 2013, 26 (1): 163-190. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201301028.htmLIU You-jun, TIAN Cong. Analysis of dynamic characteristics of urban traffic congestion based on specificity of network configuration[J]. China Journal of Highway and Transport, 2013, 26 (1): 163-190. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGL201301028.htm [19] 李树彬, 高自友, 吴建军, 等. 基于事件的交通拥堵模拟与消散策略研究[J]. 系统仿真学报, 2012, 24 (8): 1707-1713. https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ201208028.htmLI Shu-bin, GAO Zi-you, WU Jian-jun, et al. Incident-based traffic jams simulation and dispersing strategies[J]. Journal of System Simulation, 2012, 24 (8): 1707-1713. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ201208028.htm [20] ALI A, KIM J, LEE S. Travel behavior analysis using smart card data[J]. KSCE Journal of Civil Engineering, 2016, 20 (4): 1532-1539. doi: 10.1007/s12205-015-1694-0 [21] 李树彬, 吴建军, 高自友, 等. 基于复杂网络的交通拥堵与传播动力学分析[J]. 物理学报, 2011, 60 (5): 140-148. https://www.cnki.com.cn/Article/CJFDTOTAL-WLXB201105022.htmLI Shu-bin, WU Jian-jun, GAO Zi-you, et al. The analysis of traffic congestion and dynamic propagation properties based on complex network[J]. Acta Physica Sinica, 2011, 60 (5): 140-148. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-WLXB201105022.htm [22] REN H, FOLMER H. Determinants of residential satisfaction in urban China: a multi-group structural equation analysis[J]. Urban Studies, 2017, 54 (6): 1407-1425. doi: 10.1177/0042098015627112 [23] DEBOECK P R, BOULTON A J. Integration of stochastic differential equations using structural equation modeling: a method to facilitate model fitting and pedagogy[J]. Structural Equation Modeling: a Multidisciplinary Journal, 2016, 23 (6): 888-903. doi: 10.1080/10705511.2016.1218763 [24] BAGOZZI R, YI Y. Specification, evaluation, and interpretation of structural equation models[J]. Academy of Marketing Science, 2012, 40 (1): 8-34. [25] CONTIG, FRHWIRTH-SCHNATTER S, HECKMAN J J, et al. Bayesian exploratory factor analysis[J]. Journal of Econometrics, 2014, 183 (1): 31-57. -
点击查看大图
图(5) / 表(8)
计量
- 文章访问数: 792
- HTML全文浏览量: 137
- PDF下载量: 750
- 被引次数: 0
