286
Views
3
CrossRef citations to date
0
Altmetric
Articles

A semantic embedding methodology for motor vehicle crash records: A case study of traffic safety in Manhattan Borough of New York City

ORCID Icon, , ORCID Icon, &

References

  • Aguero-Valverde, J. (2013). Multivariate spatial models of excess crash frequency at area level: Case of Costa Rica. Accident; Analysis and Prevention, 59, 365–373. doi:10.1016/j.aap.2013.06.014
  • Aguero-Valverde, J., & Jovanis, P. P. (2006). Spatial analysis of fatal and injury crashes in Pennsylvania. Accident; Analysis and Prevention, 38(3), 618–625. doi:10.1016/j.aap.2005.12.006
  • Bao, J., Xu, C., Liu, P., & Wang, W. (2017). Exploring bikesharing travel patterns and trip purposes using smart card data and online point of interests. Networks and Spatial Economics, 17(4), 1231–1253. doi:10.1007/s11067-017-9366-x
  • Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.
  • Cai, Q., Abdel-Aty, M., Lee, J., & Eluru, N. (2017). Comparative analysis of zonal systems for macro-level crash modeling. Journal of Safety Research, 61, 157–166. doi:10.1016/j.jsr.2017.02.018
  • Caliendo, C., Guida, M., & Parisi, A. (2007). A crash-prediction model for multilane roads. Accident; Analysis and Prevention, 39(4), 657–670. doi:10.1016/j.aap.2006.10.012
  • Carson, J., & Mannering, F. (2001). The effect of ice warning signs on ice-accident frequencies and severities. Accident; Analysis and Prevention, 33(1), 99–109. doi:10.1016/S0001-4575(00)00020-8
  • Chang, F., Xu, P., Zhou, H., Lee, J., & Huang, H. (2019). Identifying motorcycle high-risk traffic scenarios through interactive analysis of driver behavior and traffic characteristics. Transportation Research Part F: Traffic Psychology and Behaviour, 62, 844–854. doi:10.1016/j.trf.2019.03.010
  • Chang, L.-Y., & Chen, W.-C. (2005). Data mining of tree-based models to analyze freeway accident frequency. Journal of Safety Research, 36(4), 365–375. doi:10.1016/j.jsr.2005.06.013
  • Chen, K., Liu, P., Li, Z., Wang, Y., & Lu, Y. (2021). Modeling Anticipation and Relaxation of Lane Changing Behavior Using Deep Learning. Transportation Research Record, 03611981211028624.
  • Chen, Y., & Persaud, B. (2014). Methodology to develop crash modification functions for road safety treatments with fully specified and hierarchical models. Accident; Analysis and Prevention, 70, 131–139. doi:10.1016/j.aap.2014.03.012
  • Dai, D., Taquechel, E., Steward, J., & Strasser, S. (2010). The impact of built environment on pedestrian crashes and the identification of crash clusters on an urban university campus. Western Journal of Emergency Medicine, 11(3), 294.
  • Das, S., Dutta, A., & Geedipally, S. R. (2021). Applying Bayesian data mining to measure the effect of vehicular defects on crash severity. Journal of Transportation Safety & Security, 13(6), 605–617. doi:10.1080/19439962.2019.1658674
  • Dumbaugh, E., & Rae, R. (2009). Safe urban form: Revisiting the relationship between community design and traffic safety. Journal of the American Planning Association, 75(3), 309–329. doi:10.1080/01944360902950349
  • Elamrani Abou Elassad, Z., Mousannif, H., & Al Moatassime, H. (2020). Class-imbalanced crash prediction based on real-time traffic and weather data: A driving simulator study. Traffic Injury Prevention, 21(3), 201–208. doi:10.1080/15389588.2020.1723794
  • Elvik, R. (2009). Developing accident modification functions: Exploratory study. Transportation Research Record: Journal of the Transportation Research Board, 2103(1), 18–24. doi:10.3141/2103-03
  • Fawcett, L., Thorpe, N., Matthews, J., & Kremer, K. (2017). A novel Bayesian hierarchical model for road safety hotspot prediction. Accident; Analysis and Prevention, 99(Pt A), 262–271. doi:10.1016/j.aap.2016.11.021
  • Guo, Y., Sayed, T., & Essa, M. (2020). Real-time conflict-based Bayesian Tobit models for safety evaluation of signalized intersections. Accident; Analysis and Prevention, 144, 105660. https://doi.org/10.1016/j.aap.2020.105660 32623321
  • Hadayeghi, A., Shalaby, A. S., & Persaud, B. (2003). Macrolevel accident prediction models for evaluating safety of urban transportation systems. Transportation Research Record: Journal of the Transportation Research Board, 1840(1), 87–95. doi:10.3141/1840-10
  • Hasan, S., & Ukkusuri, S. V. (2014). Urban activity pattern classification using topic models from online geo-location data. Transportation Research Part C: Emerging Technologies, 44, 363–381. doi:10.1016/j.trc.2014.04.003
  • Hosseinpour, M., Sahebi, S., Zamzuri, Z. H., Yahaya, A. S., & Ismail, N. (2018). Predicting crash frequency for multi-vehicle collision types using multivariate Poisson-lognormal spatial model: A comparative analysis. Accident; Analysis and Prevention, 118, 277–288. doi:10.1016/j.aap.2018.05.003
  • Huang, H., Chang, F., Zhou, H., & Lee, J. (2019). Modeling unobserved heterogeneity for zonal crash frequencies: A Bayesian multivariate random-parameters model with mixture components for spatially correlated data. Analytic Methods in Accident Research, 24, 100105. doi:10.1016/j.amar.2019.100105
  • Inada, H., Li, Q., Bachani, A., & Hyder, A. A. (2019). Forecasting global road traffic injury mortality for 2030. Injury Prevention, 2019, 043336. doi:10.1136/injuryprev-2019-043336
  • Ivan, J. N., Wang, C., & Bernardo, N. R. (2000). Explaining two-lane highway crash rates using land use and hourly exposure. Accident; Analysis and Prevention, 32(6), 787–795. doi:10.1016/S0001-4575(99)00132-3
  • Jia, R., Khadka, A., & Kim, I. (2018). Traffic crash analysis with point-of-interest spatial clustering. Accident; Analysis and Prevention, 121, 223–230. doi:10.1016/j.aap.2018.09.018
  • Jung, S., Qin, X., & Oh, C. (2019). A risk-based systematic method for identifying fog-related crash prone locations. Applied Spatial Analysis and Policy, 12(3), 729–751. doi:10.1007/s12061-018-9265-7
  • Kiattikomol, V., Chatterjee, A., Hummer, J. E., & Younger, M. S. (2008). Planning level regression models for prediction of crashes on interchange and noninterchange segments of urban freeways. Journal of Transportation Engineering, 134(3), 111–117. doi:10.1061/(ASCE)0733-947X(2008)134:3(111)
  • Kodinariya, T. M., & Makwana, P. R. (2013). Review on determining number of cluster in K-means clustering. International Journal, 1(6), 90–95.
  • 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
  • Li, D., Song, Y., Sze, N. N., Li, Y., Miwa, T., & Yamamoto, T. (2021). An alternative closed-form crash severity model with the non-identical, heavy-tailed, and asymmetric properties. Accident; Analysis and Prevention, 158, 106192. https://doi.org/10.1016/j.aap.2021.106192 34029919
  • Liu, C., Zhao, M., Li, W., & Sharma, A. (2018). Multivariate random parameters zero-inflated negative binomial regression for analyzing urban midblock crashes. Analytic Methods in Accident Research, 17, 32–46. doi:10.1016/j.amar.2018.03.001
  • Lovegrove, G. R., & Sayed, T. (2006). Using macrolevel collision prediction models in road safety planning applications. Transportation Research Record: Journal of the Transportation Research Board, 1950(1), 73–82. doi:10.1177/0361198106195000109
  • Macqueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the fifthBerkeley Symposium on Mathematical Statistics & Probability, 1(14), 281–297.
  • Mannering, F., Bhat, C. R., Shankar, V., & Abdel-Aty, M. (2020). Big data, traditional data and the tradeoffs between prediction and causality in highway-safety analysis. Analytic Methods in Accident Research, 25, 100113. doi:10.1016/j.amar.2020.100113
  • Martin, J.-L. (2002). Relationship between crash rate and hourly traffic flow on interurban motorways. Accident; Analysis and Prevention, 34(5), 619–629. doi:10.1016/S0001-4575(01)00061-6
  • Mitra, S. (2009). Spatial autocorrelation and Bayesian spatial statistical method for analyzing intersections prone to injury crashes. Transportation Research Record: Journal of the Transportation Research Board, 2136(1), 92–100. doi:10.3141/2136-11
  • Montella, A. (2010). A comparative analysis of hotspot identification methods. Accident; Analysis and Prevention, 42(2), 571–581. doi:10.1016/j.aap.2009.09.025
  • Pan, Y., Chen, S., Niu, S., Ma, Y., & Tang, K. (2020). Investigating the impacts of built environment on traffic states incorporating spatial heterogeneity. Journal of Transport Geography, 83, 102663.
  • Pereira, F. C., Rodrigues, F., & Ben-Akiva, M. (2013). Text analysis in incident duration prediction. Transportation Research Part C: Emerging Technologies, 37, 177–192. doi:10.1016/j.trc.2013.10.002
  • Pitt, R., Guyer, B., Hsieh, C.-C., & Malek, M. (1990). The severity of pedestrian injuries in children: An analysis of the pedestrian injury causation study. Accident; Analysis and Prevention, 22(6), 549–559. doi:10.1016/0001-4575(90)90027-I
  • Ponnaluri, R. V., & Santhi, Y. D. (2009). Road crash history and risk groups in India: Need for new initiatives and safety policies. Transportation Research Record: Journal of the Transportation Research Board, 2114(1), 64–71. doi:10.3141/2114-08
  • Saeed, T. U., Hall, T., Baroud, H., & Volovski, M. J. (2019). Analyzing road crash frequencies with uncorrelated and correlated random-parameters count models: An empirical assessment of multilane highways. Analytic Methods in Accident Research, 23, 100101. doi:10.1016/j.amar.2019.100101
  • Saha, D., Alluri, P., Gan, A., & Wu, W. (2018). Spatial analysis of macro-level bicycle crashes using the class of conditional autoregressive models. Accident; Analysis and Prevention, 118, 166–177. doi:10.1016/j.aap.2018.02.014
  • Saunier, N., Mourji, N., & Agard, B. (2011). Mining microscopic data of vehicle conflicts and collisions to investigate collision factors. Transportation Research Record: Journal of the Transportation Research Board, 2237(1), 41–50. doi:10.3141/2237-05
  • Siddiqui, C., Abdel-Aty, M., & Huang, H. (2012). Aggregate nonparametric safety analysis of traffic zones. Accident; Analysis and Prevention, 45, 317–325. doi:10.1016/j.aap.2011.07.019
  • Usman, T., Fu, L., & Miranda-Moreno, L. F. (2011). Accident prediction models for winter road safety: Does temporal aggregation of data matter? Transportation Research Record: Journal of the Transportation Research Board, 2237(1), 144–151. doi:10.3141/2237-16
  • Wang, C., Xie, Y., Huang, H., & Liu, P. (2021). A review of surrogate safety measures and their applications in connected and automated vehicles safety modeling. Accident; Analysis and Prevention, 157, 106157. https://doi.org/10.1016/j.aap.2021.106157 33975090
  • Wang, L., & Abdel-Aty, M. (2016). Microscopic safety evaluation and prediction for freeway-to-freeway interchange ramps. Transportation Research Record: Journal of the Transportation Research Board, 2583(1), 56–64. doi:10.3141/2583-08
  • Wang, S., Chen, Y., Huang, J., Chen, N., & Lu, Y. (2019). Macrolevel traffic crash analysis: A spatial econometric model approach. Mathematical Problems in Engineering, 2019, 1–10. doi:10.1155/2019/5306247
  • Wang, X., Cottrell, W., & Mu, S. (2005). Using K-means clustering to identify time-of-day break points for traffic signal timing plans. Proceedings of the 2005 IEEE Intelligent Transportation Systems, 2005, 519–524. IEEE. doi:10.1109/ITSC.2005.1520102
  • 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. doi:10.1016/j.aap.2008.10.001
  • Yannis, G., Papadimitriou, E., & Antoniou, C. (2007). Multilevel modelling for the regional effect of enforcement on road accidents. Accident; Analysis and Prevention, 39(4), 818–825. doi:10.1016/j.aap.2006.12.004
  • Yu, H., Yuan, R., Li, Z., Zhang, G., & Ma, D. T. (2020). Identifying heterogeneous factors for driver injury severity variations in snow-related rural single-vehicle crashes. Accident; Analysis and Prevention, 144, 105587. https://doi.org/10.1016/j.aap.2020.105587 32540621
  • Yu, R., & Abdel-Aty, M. (2013). Investigating the different characteristics of weekday and weekend crashes. Journal of Safety Research, 46, 91–97. doi:10.1016/j.jsr.2013.05.002
  • 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
  • Zha, L., Lord, D., & Zou, Y. (2016). The Poisson inverse Gaussian (PIG) generalized linear regression model for analyzing motor vehicle crash data. Journal of Transportation Safety & Security, 8(1), 18–35. doi:10.1080/19439962.2014.977502
  • Zhang, B., Chen, S., Ma, Y., Li, T., & Tang, K. (2020). Analysis on spatiotemporal urban mobility based on online car-hailing data. Journal of Transport Geography, 82, 102568.
  • Zhang, Y., Li, H., Sze, N. N., & Ren, G. (2021). Propensity score methods for road safety evaluation: Practical suggestions from a simulation study. Accident; Analysis and Prevention, 158, 106200. https://doi.org/10.1016/j.aap.2021.106200 34052597
  • Zou, W., Wang, X., & Zhang, D. (2017). Truck crash severity in New York city: An investigation of the spatial and the time of day effects. Accident; Analysis and Prevention, 99(Pt A), 249–261. doi:10.1016/j.aap.2016.11.024

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.