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Articles

Modeling spatial spillover effect on intersection crash propensity: a case study at the county level in Ohio

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References

  • Abdel-Aty, M. A., & Radwan, A. E. (2000). Modeling traffic accident occurrence and involvement. Accident; Analysis and Prevention, 32(5), 633–642. doi:10.1016/S0001-4575(99)00094-9
  • Aguero-Valverde, J. (2014). Direct spatial correlation in crash frequency models: Estimation of the effective range. Journal of Transportation Safety & Security, 6(1), 21–33. doi:10.1080/19439962.2013.799108
  • Aguilar, C., Russo, B. J., Mohebbi, A., & Akbariyeh, S. (2022). Analysis of factors affecting the frequency of crashes on interstate freeways by vehicle type considering multiple weather variables. Journal of Transportation Safety & Security, 14(6), 973–1001. doi:10.1080/19439962.2020.1869875
  • Arash, M. R., Agbelie, B. R. D. K., & Lee, Y. (2016). Statistical modeling of total crash frequency at highway intersections. Journal of Traffic and Transactions Engineering(English Edition), 3(2), 166–171. doi:10.1016/j.jtte.2016.03.003
  • Bhowmik, T., Yasmin, S., & Eluru, N. (2021). A new econometric approach for modeling several count variables: A case study of crash frequency analysis by crash type and severity. Transportation Research Part B: Methodological, 153, 172–203. doi:10.1016/j.trb.2021.09.008
  • Bibby, A. (2017). Why work from home? Here are 9 big reasons. Retrieved June 2021, from https://www.flexjobs.com/blog/post/why-work-from-home/.
  • Bil, M., Andrasik, R., & Sedonik, J. (2019). A detailed spatiotemporal analysis of traffic crash hotspots. Applied Geography (Sevenoaks), 107, 82–90.
  • BLO. (2021). Motor vehicle accidents. Retrieved June 2021, from https://beckerlaw.com/blog/2-5-million-rear-end-collisions-every-year-make-common-type-crash/#:∼:text=Nearly%202.5%20million%20rear%20end,can%20be%20done%20about%20it%3F.
  • Cai, Q., Lee, J., Eluru, N., & Abdel-Aty, M. (2016). Macro-level pedestrian and bicycle crash analysis: Incorporating spatial spillover effects in dual state count models. Accident; Analysis and Prevention, 93, 14–22. doi:10.1016/j.aap.2016.04.018
  • Chen, C., Zhang, G., Yang, J., Milton, J. C., & Alcántara, A. D. (2016). An explanatory analysis of driver injury severity in rear-end crashes using a decision table/naïve bayes (DTNB) hybrid classifier. Accident; Analysis and Prevention, 90, 95–107. doi:10.1016/j.aap.2016.02.002
  • Cheng, K.-L., Hsu, S.-C., Li, W.-M., & Ma, H.-W. (2018). Quantifying potential anthropogenic resources of buildings through hot spot analysis. Resources, Conservation and Recycling, 133, 10–20. doi:10.1016/j.resconrec.2018.02.003
  • Chin, H. C., & Quddus, M. A. (2003). Applying the random effect negative binomial model to examine traffic accident occurrence at signalized intersections. Accident; Analysis and Prevention, 35(2), 253–259. doi:10.1016/S0001-4575(02)00003-9
  • Dong, C., Clarke, D. B., Richards, S. H., & Huang, B. (2014). Differences in passenger car and large truck involved crash frequencies at urban signalized intersections: An exploratory analysis. Accident; Analysis and Prevention, 62, 87–94. doi:10.1016/j.aap.2013.09.011
  • El-Basyouny, K., & Sayed, T. (2009). Collision prediction models using multivariate Poisson-lognormal regression. Accident; Analysis and Prevention, 41(4), 820–828. doi:10.1016/j.aap.2009.04.005
  • ESRI. (2020). How hot spot analysis (Getis-Ord Gi*) Works. Retrieved December 2020, from https://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/h-how-hot-spot-analysis-getis-ord-gi-spatial-stati.htm.
  • Frost, J. (2021). Interpreting correlation coefficients. https://statisticsbyjim.com/basics/
  • Greibe, P. (2003). Accident prediction models for urban roads. Accident; Analysis and Prevention, 35(2), 273–285. doi:10.1016/S0001-4575(02)00005-2
  • Guikema, S. D., Coffelt, J. P., & Goffelt, J. P. (2008). A flexible count data regression model for risk analysis. Risk Analysis: An Official Publication of the Society for Risk Analysis, 28(1), 213–223. doi:10.1111/j.1539-6924.2008.01014.x
  • Guo, F., Wang, X., & Abdel-Aty, M. A. (2010). Modeling signalized intersection safety with corridor-level spatial correlations. Accident; Analysis and Prevention, 42(1), 84–92. doi:10.1016/j.aap.2009.07.005
  • Hou, Q., Zhang, X., Li, B., Zhang, X., & Wang, W. (2019). Identification of low-carbon travel block based on GIS hotspot analysis using spatial distribution learning algorithm. Neural Computing and Applications, 31(9), 4703–4713. doi:10.1007/s00521-018-3447-8
  • Huang, H., Song, B., Xu, P., Zeng, 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, 248–256. doi:10.1016/j.jtrangeo.2016.06.012
  • Hussain, M. S., Goswami, A. K., & Gupta, A. (2022). Predicting pedestrian crash locations in urban India: An integrated GIS-based spatiotemporal HSID technique. Journal of Transportation Safety & Security, 1–34. doi:10.1080/19439962.2022.2048759
  • Jones, B., Janssen, L., & Mannering, F. (1991). Analysis of the frequency and duration of freeway accidents in Seattle. Accident; Analysis and Prevention, 23(4), 239–255. doi:10.1016/0001-4575(91)90003-N
  • Jouffe, Y., Caubel, D., Fol, S., & Motte-Baumvol, B. (2019). Dealing with inequality in mobility: tactics, strategies and projects for poor households on the outskirts of Paris. Cybergeo. doi:10.4000/cybergeo.33479
  • Khazraee, S. H., Sáez‐Castillo, A. J., Geedipally, S. R., & Lord, D. (2015). Application of the hyper‐poisson generalized linear model for analyzing motor vehicle crashes. Risk Analysis: An Official Publication of the Society for Risk Analysis, 35(5), 919–930. doi:10.1111/risa.12296
  • Kumar, C. N., Parida, M., & Jain, S. S. (2013). Poisson family regression techniques for prediction of crash counts using Bayesian inference. Procedia - Social and Behavioral Sciences, 104, 982–991. doi:10.1016/j.sbspro.2013.11.193
  • Lee, J., Abdel-Aty, M., & Cai, Q. (2017). Intersection crash prediction modeling with macro-level data from various geographic units. Accident; Analysis and Prevention, 102, 213–226. doi:10.1016/j.aap.2017.03.009
  • Lee, J., Abdel-Aty, M., & Jiang, X. (2014). Development of zone system for macro-level traffic safety analysis. Journal of Transport Geography, 38, 13–21. doi:10.1016/j.jtrangeo.2014.04.018
  • Lee, J., Yasmin, S., Eluru, N., Abdel-Aty, M., & Cai, Q. (2018). Analysis of crash proportion by vehicle type at traffic analysis zone level: A mixed fractional split multinomial logit modeling approach with spatial effects. Accident; Analysis and Prevention, 111, 12–22. doi:10.1016/j.aap.2017.11.017
  • Li, Z., Chen, C., Ci, Y., Zhang, G., Wu, Q., Liu, C., & Qian, Z. (2018). Examining driver injury severity in intersection-related crashes using cluster analysis and hierarchical Bayesian models. Accident; Analysis and Prevention, 120, 139–151. doi:10.1016/j.aap.2018.08.009
  • Lord, D., & Mannering, F. (2010). The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives. Transportation Research Part A: Policy and Practice, 44(5), 291–305. doi:10.1016/j.tra.2010.02.001
  • Lu, J., Pan, F., & Xiang, Q. (2008). Level-of-safety service for safety performance evaluation of highway intersections. Transportation Research Record: Journal of the Transportation Research Board, 2075(1), 24–33. doi:10.3141/2075-04
  • Meng, Y., Wu, L., Ma, C., Guo, X., & Wang, X. (2022). A comparative analysis of intersection hotspot identification: Fixed vs. varying dispersion parameters in negative binomial models. Journal of Transportation Safety & Security, 14(2), 305–322. doi:10.1080/19439962.2020.1779421
  • Miaou, S.-P. (1994). The relationship between truck accidents and geometric design of road sections: Poisson versus negative binomial regressions. Accident; Analysis and Prevention, 26(4), 471–482. doi:10.1016/0001-4575(94)90038-8
  • Miaou, S.-P., & Lum, H. (1993). Modeling vehicle accidents and highway geometric design relationships. Accident; Analysis and Prevention, 25(6), 689–709. doi:10.1016/0001-4575(93)90034-T
  • Mitra, S., & Washington, S. (2007). On the nature of over-dispersion in motor vehicle crash prediction models. Accident Analysis and Prevention. 39(3), 459–468. doi:10.1016/j.aap.2006.08.002
  • NHTSA. (2022)., In response to this crisis, earlier this year USDOT unveiled the National Roadway Safety Strategy that is now getting resources from the President’s Bipartisan Infrastructure Law [Online]. Retrieved July 2022, from https://www.nhtsa.gov/press-releases/early-estimate-2021-traffic-fatalities.
  • Odland, J. (1988). Spatial autocorrelation. Scientific geography series. Sage Publications.
  • OKI. (2021). Crash rates. Retrieved June 2021, from https://gis.oki.org/crashrates/.
  • Osama, A., Sayed, T., & Sacchi, E. (2018). A novel technique to identify hot zones for active commuters’ crashes. Transportation Research Record: Journal of the Transportation Research Board, 2672(38), 266–276. doi:10.1177/0361198118786829
  • Park, M., Lee, D., & Jeon, J. (2016). Random parameter negative binomial model of signalized intersections. Mathematical Problems in Engineering, 2016, 1–8. doi:10.1155/2016/1436364
  • Poch, M., & Mannering, F. (1996). Negative binomial analysis of intersection-crash frequencies. Journal of Transportation Engineering, 122(2), 105–113. doi:10.1061/(ASCE)0733-947X(1996)122:2(105)
  • Sacchi, E., Sayed, T., & El-Basyouny, K. (2015). Multivariate full Bayesian hot spot identification and ranking new technique. Transportation Research Record: Journal of the Transportation Research Board, 2515(1), 1–9. doi:10.3141/2515-01
  • Sáez-Castillo, A. J., & Conde-Sánchez, A. (2013). A Hyper-Poisson regression model for overdispersed and underdispersed count data. Computational Statistics & Data Analysis, 61, 148–157. doi:10.1016/j.csda.2012.12.009
  • Scheiner, J., Scheiner, J., Holz-Rau, C., & Holz-Rau, C. (2017). Women’s complex daily lives: A gendered look at trip chaining and activity pattern entropy in Germany. Transportation, 44(1), 117–138. doi:10.1007/s11116-015-9627-9
  • Shaon, M. R., & Qin, X. (2016). Use of mixed distribution generalized linear models to quantify safety effects of rural roadway features. Transportation Research Record: Journal of the Transportation Research Board, 2583(1), 134–141. doi:10.3141/2583-17
  • TIMS. (2020a). Data download. Retrieved June, 2021, from https://gis.dot.state.oh.us/tims/Data/Download.
  • TIMS. (2020b). GCAT crash analysis tool. Retrieved June, 2021, from https://gis.dot.state.oh.us/tims/CrashAnalytics/Login .
  • USCB. (2020). Advanced search. Retrieved June 2021, from https://data.census.gov/cedsci/.
  • USCB. (2021). QuickFacts. Retrieved June, 2021, from https://www.census.gov/quickfacts/hamiltoncountyohio.
  • Wang, Y., Xiong, R., Yu, H., Bao, J., & Yang, Z. (2021). A semantic embedding methodology for motor vehicle crash records: A case study of traffic safety in Manhattan Borough of New York City. Journal of Transportation Safety & Security, 1–21. doi:10.1080/19439962.2021.1994681
  • Weng, J., Gan, X., & Chen, J. (2021). A separate analysis of crash frequency for the highways involving traffic hazards and involving no traffic hazards. Journal of Transportation Safety & Security, 13(8), 822–841. doi:10.1080/19439962.2019.1690086
  • Weng, J., Gan, X., & Zhang, Z. (2021). A quantitative risk assessment model for evaluating hazmat transportation accident risk. Safety Science, 137, 105198. doi:10.1016/j.ssci.2021.105198
  • WIKI. (2020a). Ohio. Retrieved December 2020 from https://en.wikipedia.org/wiki/Ohio.
  • WIKI. (2020b). Political divisions of the United States. Retrieved December 2020, from https://en.wikipedia.org/wiki/Political_divisions_of_the_United_States.
  • Wu, P., Meng, X., & Song, L. (2020). A novel ensemble learning method for crash prediction using road geometric alignments and traffic data. Journal of Transportation Safety & Security, 12(9), 1128–1146. doi:10.1080/19439962.2019.1579288
  • Xie, K., Wang, X., Ozbay, K., & Yang, H. (2014). Crash frequency modeling for signalized intersections in a high-density urban road network. Analytic Methods in Accident Research, 2, 39–51. doi:10.1016/j.amar.2014.06.001
  • Ye, X., Wang, K., Zou, Y., & Lord, D. (2018b). A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data. PloS One, 13(5), e0197338. doi:10.1371/journal.pone.0197338
  • Yuan, L., & Lu, J. (2008). Safety evaluation and improvements for highway intersections. Transportation Research Record: Journal of the Transportation Research Board, 2060(1), 46–52. doi:10.3141/2060-06
  • Zeng, Q., Wen, H., Wong, S. C., Huang, H., Guo, Q., & Pei, X. (2020). Spatial joint analysis for zonal daytime and nighttime crash frequencies using a Bayesian bivariate conditional autoregressive model. Journal of Transportation Safety & Security, 12(4), 566–585. doi:10.1080/19439962.2018.1516259
  • Zou, Y., Zhong, X., Ash, J., Zeng, Z., Wang, Y., Hao, Y., & Peng, Y. (2017). Developing A clustering-based empirical bayes analysis method for hotspot identification. Journal of Advanced Transportation, 2017, 1–9. doi:10.1155/2017/5230248

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