Open Access
Issue |
MATEC Web Conf.
Volume 250, 2018
The 12th International Civil Engineering Post Graduate Conference (SEPKA) – The 3rd International Symposium on Expertise of Engineering Design (ISEED) (SEPKA-ISEED 2018)
|
|
---|---|---|
Article Number | 02005 | |
Number of page(s) | 10 | |
Section | Transportation Engineering | |
DOI | https://doi.org/10.1051/matecconf/201825002005 | |
Published online | 11 December 2018 |
- B.R. Kadali, V. Perumal, Pedestrians’gap acceptance behavior at mid-block location, Int. J. of Engineering and Technology, 4, 158–161 (2012) [CrossRef] [Google Scholar]
- H. Guo, F. Zhao, W. Wang, Y. Zhou, Y. Zhang, G. Wets, Modeling the perceptions and preferences of pedestrians on crossing facilities, Discrete Dynamics in Nature and Society, 8 (2014) [Google Scholar]
- P. R. Anciaes, P. Jones, How do pedestrians balance safety, walking time, and the utility of crossing the road? A stated preference study. Pioneering Research and Skills, 8, 1–19 (2016) [Google Scholar]
- B.R. Kadali, P. Vedagiri, Modelling pedestrian road crossing behaviour under mixed traffic condition. European Transport Trasporti Europei, 55, 1–17 (2013) [Google Scholar]
- E. Papadimitriou, G Yannis, J. Golias, A critical assessment of pedestrian behaviour models. Transportation Research Part F: Traffic Psychology and Behaviour, 3, 242–255 (2009) [CrossRef] [Google Scholar]
- L.B. Trifiletti, A.C. Gielen, D.A. Sleet, K. Hopkins, Behavioral and social sciences theories and models: Are they used in unintentional injury prevention research?, Health Education Research, 20(3), 298-307, (2005) [CrossRef] [Google Scholar]
- I. Ajzen, The Theory of Planned Behavior. Organizational Behavior and Human DecisionProcesses, 50, 179–211, (1991) [CrossRef] [Google Scholar]
- I. M. Rosenstock, Why people use health services? Milbank Memorial Fund Quarterly, 44, 94–124, (1966) [CrossRef] [Google Scholar]
- F.D. Davis, Perceived Usefulness, Perceived ease of use and user acceptance of Information Technology. MIS Quarterly, 13 (3), 319–340, (1989) [CrossRef] [Google Scholar]
- H. Zhou, S. B. Romero, X. Qin, An extension of the theory of planned behavior to predict pedestrians’ violating crossing behavior using structural equation modeling. Accident Analysis & Prevention, 95, 417–424, (2016) [CrossRef] [Google Scholar]
- E.M. Diaz, Theory of planned behavior and pedestrians’ intentions to violate traffic regulations. Transp. Res. F, 5, 169–175, (2002) [CrossRef] [Google Scholar]
- F. K. Winston, L. Jacobsohn. A practical approach for applying best prevention practices in behavioural interventions to injury. Injury Prevention, 16, 107-112, (2010) [CrossRef] [Google Scholar]
- K.. Ambak, R. Ismail, R. A. Abdullah, M. N. Borhan, Using Structural Equation Modeling and the Behavioral Sciences Theories in Predicting Helmet Use. Proceeding of the International Conference on Advanced Science, Engineering and Information Technology 201. (2011) [Google Scholar]
- S.E. Johnson, A. Hall, The prediction of safe lifting behavior: An application of The Theory of Planned Behavior. Journal of Safety Research, 36, 63–73, (2005) [CrossRef] [Google Scholar]
- F. Letirand, P. Delhomme, Speed Behavior as a choice between observing and exceeding the speed limit. Transportation Research Part F, 8, 481–492, (2005) [CrossRef] [Google Scholar]
- B. K. Barton, S. M. Kologi, & A, Distracted pedestrians in crosswalks: An application of the Theory of Planned Behavior. Transportation Research Part F: Traffic Psychology and Behaviour, 37, 129–137. Siron, (2016). [CrossRef] [Google Scholar]
- H.W. Warner, L. Abreg, Drivers’ decision to speed: a study inspired by The Theory of Planned Behaviour, Transportation Research Part F, 9,427–433, (2006) [CrossRef] [Google Scholar]
- S. E. Forward, The theory of planned behaviour : The role of descriptive norms and past behaviour in the prediction of drivers ’ intentions to violate. Transportation Research Part F: Psychology and Behaviour, 12(3), 198–207, (2009) [CrossRef] [Google Scholar]
- D. Evans, P. Norman, Predicting adolescent pedestrians’ road-crossing intention : an application and extension of the theory of planned behavior. Health Education Resources, 18, 267-277, (2003) [CrossRef] [Google Scholar]
- S. Osswald, D. Wurhofer, S. Trosterer, Predicting information technology usage in the car: Towards a car technology aceptance model, Automotive U1’12, Portsmouth, (2012) [Google Scholar]
- R.C. Maccallum, J.T. Austin, Applications of Structural Equation Modeling in psychological research. Annual Reviews Psychology, 51, 201–226, (2000) [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
- T.F. Golob, Structural equation modeling for travel behavior research. Transportation Research Part B, 37, 1-25, (2003) [CrossRef] [Google Scholar]
- K. Ambak, R. Ismail, R. A. Abdullah, A. A. Latiff, M. E. Sanik, Application of technology acceptance model in predicting behavioral intention to use safety helmet reminder system. Research Journal of Applied Sciences, Engineering and Technology, 5(3), 881–888, (2013). [CrossRef] [Google Scholar]
- R. Weston, P.A. Jr. Gore, A brief guide to structural equation modelling, Couns. Psychol, 34(5), 719-751, (2006) [CrossRef] [Google Scholar]
- A. B. Costello, J. W. Osborne, Best Practices in Exploratory Factor Analysis : Four Recommendations for Getting the Most From Your Analysis. Practical Assessment, Research & Education, 10, 1–9, (2005). [CrossRef] [Google Scholar]
- M. Gallagher, T. Brown, Introduction to Confirmatory Factor Analysis and Structural Equation Modeling. Handbook of Quantitative Methods for Educational Research, 5(revision C), 289–314, (2013). [CrossRef] [Google Scholar]
- S. Ahmad, N.N.A. Zulkurnain, I. Khairushalimi, Assessing the Fitness of a Measurement Model Using Confirmatory Factor Analysis (CFA), International Journal of Innovation and Applied Studies, 17, 159-168, (2016). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.