留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

影响出行决策的交通质量多因素感知特征

胡盼 杨晓光

胡盼, 杨晓光. 影响出行决策的交通质量多因素感知特征[J]. 交通运输工程学报, 2017, 17(2): 117-125.
引用本文: 胡盼, 杨晓光. 影响出行决策的交通质量多因素感知特征[J]. 交通运输工程学报, 2017, 17(2): 117-125.
HU Pan, YANG Xiao-guang. Multiple-factor perceived features of traffic quality influencing trip decision[J]. Journal of Traffic and Transportation Engineering, 2017, 17(2): 117-125.
Citation: HU Pan, YANG Xiao-guang. Multiple-factor perceived features of traffic quality influencing trip decision[J]. Journal of Traffic and Transportation Engineering, 2017, 17(2): 117-125.

影响出行决策的交通质量多因素感知特征

基金项目: 

国家自然科学基金项目 51238008

详细信息
    作者简介:

    胡盼(1983-), 男, 湖北孝感人, 同济大学工学博士研究生, 从事智能交通系统研究

    杨晓光(1959-), 男, 江苏宿迁人, 同济大学教授, 工学博士

  • 中图分类号: U491.114

Multiple-factor perceived features of traffic quality influencing trip decision

More Information
  • 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。

     

  • 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  2  初始模型路径

    Figure  2.  Initial model paths

    图  3  修正模型路径

    Figure  3.  Revised model paths

    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

    图  5  修正的超大型城市SEM

    Figure  5.  Revised SEM in super-large city

    表  1  交通质量因素

    Table  1.   Traffic quality factors

    下载: 导出CSV

    表  2  基于需求层次理论的交通质量因素

    Table  2.   Traffic quality factors based on needs hierarchy theory

    下载: 导出CSV

    表  3  调研样本特征

    Table  3.   Research sample characteristics

    下载: 导出CSV

    表  4  因子分析结果

    Table  4.   Factor analysis result

    下载: 导出CSV

    表  5  SEM变量体系

    Table  5.   SEM variables system

    下载: 导出CSV

    表  6  初始模型与修正模型的拟合指数

    Table  6.   Fitting indexes of initial and revised model

    下载: 导出CSV

    表  7  复核效度检验

    Table  7.   Check validity test

    下载: 导出CSV

    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.htm

    ZHENG 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/201405009

    YANG 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/201601016

    HE 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.htm

    YANG 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.htm

    LIU 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.htm

    LI 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.htm

    LI 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.
  • 加载中
图(5) / 表(8)
计量
  • 文章访问数:  812
  • HTML全文浏览量:  144
  • PDF下载量:  750
  • 被引次数: 0
出版历程
  • 收稿日期:  2016-11-11
  • 刊出日期:  2017-04-25

目录

    /

    返回文章
    返回