Have a personal or library account? Click to login
Macro-Level Modeling of Urban Transportation Safety: Case-Study of Mashhad (Iran) Cover

Macro-Level Modeling of Urban Transportation Safety: Case-Study of Mashhad (Iran)

Open Access
|Nov 2017

References

  1. 1. Cameron, A.C. and Trivedi, P. K. (1998) Regression Analysis of Count Data. New York: Cambridge University Press.10.1017/CBO9780511814365
  2. 2. de Guevara, F.L., Washington, S.P., Oh, J. (2004) Forecasting Crashes at the Planning Level: A Simultaneous Negative Binomial Crash Model Applied in Tucson, Arizona. Transportation Research Record 1897, TRB, National Research Council, Washington, D.C.10.3141/1897-25
  3. 3. ESRI. (2011) ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research Instiude.
  4. 4. Hadayeghi, A., Shalaby, A. S., Persaud, B. (2003) Macro-Level Accident Prediction Models for Evaluating Safety of Urban Transportation Systems. Transportation Research Record 1840, TRB, National Research Council, Washington D.C.10.3141/1840-10
  5. 5. Hadayeghi, A., Shalaby, A.S., Pesuad, B. (2007) Safety Prediction Models: A Proactive Tool for Safety Evaluation in Urban Transportation Planning Applications. Transportation Research Record, TRB, National Research Council, Washington D.C.10.3141/2019-27
  6. 6. Huang, H., Song, B., Xu, P., Jae, Q., Lee, J., Abdel-Aty, M. (2016) Macro and micro models for zonal crash prediction with application in hot zones identification, Journal of Transport Geography, 54, pp. 248-256.10.1016/j.jtrangeo.2016.06.012
  7. 7. IBM Corp. (2014) IBM SPSS Statistics 23. - www.vsni.co.uk.
  8. 8. MTTO. (2009) Comprehensive Transportation Studies of Mashhad City, update. Mashhad, Iran: Mashhad Transportation and Traffic Organization (MTTO).
  9. 9. Naderan, A. (2010) Estimating Crash Frequencies in Urban Districts using Aggregate (Macro) Models. Tehran, Iran: Ph.D. Thesis in Iran University of Science and Technology.
  10. 10. Naderan, A., Shahi, J. (2010) Aggregate crash prediction models: Introducing crash generation concept. Accident Analysis and Prevention, 42 (1), 339-346.10.1016/j.aap.2009.08.02019887176
  11. 11. Quddus, M.A. (2008) Modelling area-wide count outcomes with spatial correlation and heterogeneity: An analysis of London crash data. Accident Analysis and Prevention 40 (2008) 1486-1497.10.1016/j.aap.2008.03.00918606282
  12. 12. Tasic, I., Porter, R.J. (2016) Modeling spatial relationships between multimodal transportation infrastructure and traffic safety outcomes in urban environments. Safety Science, 82 (2016), 325-337.10.1016/j.ssci.2015.09.021
  13. 13. Wang, J., Huang, H., Zeng, Q. (2017) The effect of zonal factors in estimating crash risks by transportationmodes: Motor vehicle, bicycle and pedestrian. Accident Analysis and Prevention, 98, pp. 223-231.10.1016/j.aap.2016.10.01827770688
  14. 14. Washington, S., Schalwyk, I.V., Meyer, M., Dumbaugh, E., Zoll, M. (2006) Incorporating Safety into Long-Range Transportation Planning. NCHRP Report 546, TRB, National Cooperative Highway Research Program,Washington D.C.
  15. 15. Wier, M., Weintraub, J., Humphreys, E.H., Seto, E., Bhatia, R. (2009) An area-level model of vehicle-pedestrian injury collisions with implications for land use and transportation planning. Accident Analysis and Prevention, 41 (1), 137-145.10.1016/j.aap.2008.10.00119114148
DOI: https://doi.org/10.1515/ttj-2017-0025 | Journal eISSN: 1407-6179 | Journal ISSN: 1407-6160
Language: English
Page range: 282 - 288
Published on: Nov 22, 2017
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2017 Mehdi Mohammadi, Gholamali Shafabakhsh, Ali Naderan, published by Transport and Telecommunication Institute
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.