Geographically weighted negative binomial regression applied to zonal level safety performance models

Accident Analysis and Prevention

Volumn 106, Issue , 2017, Pages 254-261

  Gomes, Marcos José Timbó Lima ^a   Cunto, Flávio ^a   Silva, Alan Ricardo ^b  

^a FEDERAL UNIVERSITY OF CEARÁ   (Brazil)

^b UNIVERSITY OF BRASILIA   (Brazil)

Author keywords

Geographically weighted negative binomial regression; Geographically weighted poisson regression; Local spatial models; Safety performance models; Spatial dependency

Indexed keywords

BINS; ECONOMICS; MAXIMUM LIKELIHOOD; POISSON DISTRIBUTION; ROADS AND STREETS; SAFETY ENGINEERING;

NEGATIVE BINOMIAL REGRESSION; POISSON REGRESSION; SAFETY PERFORMANCE; SPATIAL DEPENDENCIES; SPATIAL MODELS;

REGRESSION ANALYSIS;

BRAZIL; ENVIRONMENTAL PLANNING; HUMAN; REGRESSION ANALYSIS; SAFETY; SOCIOECONOMICS; SPATIAL REGRESSION; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; TRAFFIC ACCIDENT; TRAFFIC AND TRANSPORT;

ACCIDENTS, TRAFFIC; BRAZIL; ENVIRONMENT DESIGN; HUMANS; LINEAR MODELS; MODELS, STATISTICAL; REGRESSION ANALYSIS; SAFETY; SOCIOECONOMIC FACTORS; SPATIAL REGRESSION; TRANSPORTATION;

EID: 85021160967     PISSN: 00014575     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.aap.2017.06.011     Document Type: Article

References (39)

1. Abdel-Aty, M., Lee, J., Siddiqui, C., Choi, K., Geographical unit based analysis in the context of transportation safety planning. Transp. Res. Part A Policy Pract. 49 (2013), 62–75.
2. Aguero-Valverde, J., Jovanis, P.P., Spatial analysis of fatal and injury crashes in Pennsylvania. Accid. Anal. Prev. 38 (2006), 618–625.
3. Cai, Q., Lee, J., Eluru, N., Abdel-Aty, M., Macro-level pedestrian and bicycle crash analysis: incorporating spatial spillover effects in dual state count models. Accid. Anal. Prev. 93 (2016), 14–22.
4. Caliper, TransCAD – Transportation Workstation Software, User's Guide, Version 5.0 for Windows. 2008, Caliper Corporation, Newton, USA.
5. Chatterjee, A., Wegmann, F., Fortey, N., Everett, J., Incorporating safety and security issues in urban transportation planning. A Paper Presented at the 80th Annual TRB Meeting, Washington, D.C, 2001.
6. Chen, V.Y.J., Yang, T.C., SAS macro programs for geographically weighted generalized linear modeling with spatial point data: applications to health research. Comput. Methods Programs Biomed. 107 (2012), 262–273.
7. Dumbaugh, E., Meyer, M.D., Washington, S., Incorporating sate highway safety agencies into safety-Conscious planning process. A Paper Presented at the 83th Annual TRB Meeting, Washington, D.C, 2004.
8. Erdogan, S., Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey. J. Saf. Res. 40 (2009), 341–351.
9. Farber, S.E., Páez, A., A systematic investigation of cross-validation in GWR model estimation: empirical analysis and Monte Carlo simulations. J. Geog. Syst. 9 (2007), 371–396.
10. Fotheringham, A.S., Oshan, T.M., Geographically weighted regression and multicollinearity: dispelling the myth. J. Geogr. Syst. 18:4 (2016), 303–329.
11. Fotheringham, A.S., Brunsdon, C., Charlton, M.E., Geographically Weighted Regression: the Analysis of Spatially Varying Relationship. 2002, Wiley, Chichester.
12. Hadayeghi, A., Shalaby, A.S., Persaud, B.N., Macro-level accident prediction models for evaluating safety of urban transportation systems. Transp. Res. Rec. 1840:1 (2003), 87–95.
13. Hadayeghi, A., Shalaby, A.S., Persaud, B.N., Development of planning level transportation safety tools using Geographically Weighted Poisson Regression. Accid. Anal. Prev. 42 (2010), 676–688.
14. Heiberger, R.M., Holland, B., Statistical Analysis and Data Display – an Intermediate Course with Examples in R. 2nd edition, 2015, Springer (eBook).
15. Huang, H., Xu, P., Abdel-Aty, M., Transportation safety planning: a spatial analysis approach. TRB 92th Annual Meeting (TRB 2013-1855), 2013.
16. ITE, Transportation Planning Handbook. 3rd edition, 2009, Institute of Transportation Engineers.
17. Lee, J., Abdel-Aty, M.E., Jiang, X., Development of zone system for macro-level traffic safety analysis. J. Transp. Geogr. 38 (2014), 13–21.
18. Li, Z., Wang, W., Liu, P., Bigham, J.M., Ragland, D.R., Using geographically weighted Poisson regression for county-level crash modeling in California. Saf. Sci. 58 (2013), 89–97.
19. Lord, D., Persaud, B.N., Estimating the safety performance of urban road transportation networks. Accid. Anal. Prev. 36:4 (2004), 609–620.
20. Lovegrove, G.R., Sayed, T., Macro-level collision prediction models for evaluating neighbourhood traffic safety. Can. J. Civ. Eng. 33 (2006), 609–621.
21. Matkan, A.A., Mohaymany, A.S., Mirbagheri, B., Shahri, M., Explorative spatial analysis of traffic accidents using GWPR model for urban safety planning. 3rd International Conference on Road Safety and Simulation, September 14–16, 2011, Indianapolis, USA, 2011.
22. Nakaya, T., Fortheringham, A.S., Brunsdon, C., Charlton, M., Geographically Weighted Poisson regression for disease association mapping. Stat. Med. 24 (2005), 2695–2717.
23. Nakaya, T., Charlton, M., Lewis, P., Fortheringham, S., Brunsdon, C., Windows Application for Geographically Weighted Regression Modeling. 2012, Ritsumeikan University, Kyoto, Japan.
24. Pirdavani, A., Bellemans, T., Brijs, T., Wets, G., Application of geographically weighted regression technique in spatial analysis of fatal and injury crashes. J. Transp. Eng., 140(8), 2014.
25. Pulugurha, S.S., Duddu, V.R., Kotagiri, Y., Traffic analysis zone level crash estimation models based on land use characteristics. Accid. Anal. Prev. 50 (2013), 678–687.
26. Quddus, M.A., Modeling area-wide count outcomes with spatial correlation and heterogeneity: an analysis of London crash data. Accid. Anal. Prev. 40 (2008), 1486–1497.
27. Rhee, K.A., Kim, J.K., Lee, Y., Ulfarsson, G.F., Spatial regression analysis of traffic crashes in Seoul. Accid. Anal. Prev. 91 (2016), 190–199.
28. Siddiqui, C., Abdel-Aty, M., Choi, K., Macroscopic spatial analysis of pedestrian and bicycle crashes. Accid. Anal. Prev. 45 (2012), 382–391.
29. Silva, A.R., Fotheringham, A.S., The multiple testing issue in geographically weighted regression. Geogr. Anal. 48 (2016), 233–247.
30. Silva, A.R., Rodrigues, T.C.V., Geographically weighted negative binomial regression-incorporating overdispersion. Stat. Comput. 24 (2014), 769–783.
31. Silva, A.R., Rodrigues, T.C.V., A SAS. 2016 (Available at: < http://support.sas.com/resources/papers/proceedings16/8000-2016.pdf>).
32. Song, B., Huang, H., Zeng, Q., Deng, Q., Abdel-Aty, M., Comparative analysis of macro and micro models for zonal crash prediction. TRB 94th Annual Meeting (n. 15-2774), 2015.
33. Tarko, A.P., Inerowicz, M., Ramos, J., Li, W., Tool with road-level crash prediction for transportation safety planning. Transp. Res. Rec.: J. Transp. Res. Board 2083:1 (2008), 16–25.
34. Tarko, A.P., Calibration of safety prediction models for planning transportation networks. Transp. Res. Rec.: J. Transp. Res. Board 1950:1 (2006), 83–91.
35. Wang, X., Yang, J., Lee, C., Ji, Z., You, S., Macro-level safety analysis of pedestrian crashes in Shanghai, China. Accid. Anal. Prev. 96 (2016), 12–21.
36. Wheeler, D., Tiefelsdorf, M., Multicollinearity and correlation among local regression coefficients in geographically weighted regression. J. Geogr. Syst. 7:2 (2005), 161–187.
37. Xu, P., Huang, H., Modeling crash spatial heterogeneity: random parameter versus geographically weighting. Accid. Anal. Prev. 75 (2015), 16–25.
38. Xu, P., Huang, H., Dong, N., Wong, S.C., Revisiting crash spatial heterogeneity: a bayesian spatially varying coefficients approach. Accid. Anal. Prev. 98 (2017), 330–337.
39. Yu, C., Xu, M., Local variations in the impacts of built environments on traffic safety. J. Plann. Educ. Res., 2017, 1–15.