Multiple-factor perceived features of traffic quality influencing trip decision

HU Pan; YANG Xiao-guang

Volume 17 Issue 2

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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.

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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.

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.

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Multiple-factor perceived features of traffic quality influencing trip decision

Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China

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Author Bio:
HU Pan(1983-), male, doctoral student, +86-21-69589878, hupan213@163.com

YANG Xiao-guang(1959-), male, professor, PhD, +86-21-69589878, yangxg@tongji.edu.cn
Received Date: 2016-11-11
Publish Date: 2017-04-25

Abstract

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.
- traffic management,
- traffic quality factor,
- factor analysis,
- structural equation model,
- trip decision,
- integrated path coefficient

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[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]	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]	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]	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]	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]	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]	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]	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.