490
Views
5
CrossRef citations to date
0
Altmetric
Article

Identification and spatiotemporal evolution analysis of high-risk crash spots in urban roads at the microzone-level: Using the space-time cube method

, &

References

  • Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2004). Hierarchical modeling and analysis for spatial data. Boca Raton: CRC Press.
  • Bao, J., Liu, P., & Ukkusuri, S. V. (2019). A spatiotemporal deep learning approach for citywide short-term crash risk prediction with multi-source data. Accident Analysis and Prevention, 122, 239–254. doi:10.1016/j.aap.2018.10.015
  • Benedek, J., Ciobanu, S. M., & Man, T. C. (2016). Hotspots and social background of urban traffic crashes: a case study in cluj-napoca (romania). Accident Analysis & Prevention, 87, 117–126. doi:10.1016/j.aap.2015.11.026
  • Briz-Redón, A., Martínez-Ruiz, F., & Montes, F. (2019a). Spatial analysis of traffic accidents near and between road intersections in a directed linear network. Accident Analysis and Prevention., 132, 105252. doi:10.1016/j.aap.2019.07.028
  • Briz-Redón, A., Martínez-Ruiz, F., & Montes, F. (2019b). Identification of differential risk hotspots for collision and vehicle type in a directed linear network. Accident Analysis and Prevention., 132, 105278. doi:10.1016/j.aap.2019.105278
  • Cheng, S., Zhang, B., Peng, P., Yang, Z., & Lu, F. (2020). Spatiotemporal evolution pattern detection for heavy-duty diesel truck emissions using trajectory mining: A case study of Tianjin. Journal of Cleaner Production, 244, 118654. doi:10.1016/j.jclepro.2019.118654
  • Cressie, N., & Wikle, C. K. (2011). Statistics for spatio-temporal data. Singapore: John Wiley.
  • Esri. (2016). ArcGIS 10.4 for Desktop Web Help: Create space time cube. Retreived from http://desktop.arcgis.com/en/arcmap/10.3/tools/space-time-pattern-mining-toolbox/create-space-time-cube.htm.
  • Ferreira, S., & Couto, A. (2015). A probabilistic approach towards a crash risk assessment of urban segments. Accident Analysis and Prevention., 50, 97–105. doi:10.1016/j.trc.2014.09.012
  • Gatalsky, P., Andrienko, N., & Andrienko, G. (2004). Interactive analysis of event data using space-time cube. Eighth International Conference on Information Visualisation, Proceedings [Paper presentation]. , IEEE.
  • Getis, A., & Ord, J. K. (2010). The analysis of spatial association by use of distance statistics. Berlin, Heidelberg: Springer.
  • Hägertrand, T. (1970). What about people in regional science? Ninth European Congress of the Science Association.
  • Hou, Q., Huo, X., & Leng, J. (2020). A correlated random parameters tobit model to analyze the safety effects and temporal instability of factors affecting crash rates. Accident Analysis and Prevention., 134, 105–326.
  • Hou, Q., Tarko, A. P., & Meng, X. (2018). Investigating factors of crash frequency with random effects and random parameters models: New insights from Chinese freeway study. Accident Analysis and Prevention, 120, 1–12. doi:10.1016/j.aap.2018.07.010
  • Hao, W., & Daniel, J. (2014). Motor vehicle driver injury severity study under various traffic control at highway-rail grade crossings in the united states. Journal of Safety Research, 51, 41–48. doi:10.1016/j.jsr.2014.08.002
  • Huang, H., Chin, H. C., & Haque, M. M. (2009). Empirical evaluation of alternative approaches in identifying crash hot spots. Naive ranking, empirical Bayes, and full Bayes methods. Transportation Research Record: Journal of the Transportation Research Board, 2103(1), 32–41. doi:10.3141/2103-05
  • 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
  • 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
  • Kendall, M. G., & Gibbons, J. D. (1990). Rank correlation methods (5th ed.).  London: Edward Arnold.
  • Knox, E. G., & Bartlett, M. S. (1964). The detection of space-time interaction. Applied Statistics, 13(1), 25–99. doi:10.2307/2985220
  • Kraak, M. (2003 The space-time cube revisited from a geovisualization perespective [Paper presentation]. Proceedings. of the 21st International Cartographic Conference (ICC), 1988–1996.
  • Lan, B., & Persaud, B. (2011). Fully Bayesian approach to investigate and evaluate ranking criteria for blackspot identification. Transportation Research Record: Journal of the Transportation Research Board, 2237(1), 117–125. doi:10.3141/2237-13
  • Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. New York: Houghton Mifflin.
  • Lee, M., & Khattak, A. J. (2019). Case study of crash severity spatial pattern identification in hot spot analysis. Transportation Research Record: Journal of the Transportation Research Board Advanced online publication. doi:10.1177/0361198119845367
  • Li, Y., & Fan, W. (2019). Modelling severity of pedestrian-injury in pedestrian-vehicle crashes with latent class clustering and partial proportional odds model: A case study of North Carolina. Accident Analysis & Prevention, 131, 284–296. doi:10.1016/j.aap.2019.07.008
  • Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13(3), 245. doi:10.2307/1907187
  • McLachlan, G. J., & Peel, D. (2000). Finite mixture models. Wiley Series in Probability and Statistics, John Wiley & Sons, New York.
  • Meyer, S., Warnke, I., Rossler, W., & Held, L. (2016). Model-based testing for space-time interaction using point processes: An application to psychiatric hospital admissions in an urban area. Spatial and Spatio-Temporal Epidemiology, 17, 15–25. doi:10.1016/j.sste.2016.03.002
  • Ministry of Public Security of People’s Republic of China (2018). Annual Statistical Reports of Road Traffic Accidents.
  • Montella, A. (2010). A comparative analysis of hotspot identification methods. Accident Analysis & Prevention, 42(2), 571–581. doi:10.1016/j.aap.2009.09.025
  • Mussone, L., Bassani, M., & Masci, P. (2017). Analysis of factors affecting the severity of crashes in urban road intersections. Accident Analysis and Prevention, 103, 112–122. doi:10.1016/j.aap.2017.04.007
  • National Bureau of Statistics of China (2019). China statistical yearbook. Beijing: China Statistics Press.
  • Nguyen, H. H., Taneerananon, P., & Luathep, P. (2015). Approach to Identifying Black Spots Based on Potential Saving in Accident Costs. Engineering Journal, 20(2), 110–122.
  • Park, H., & Oh, C. (2019). A vehicle speed harmonization strategy for minimizing inter-vehicle crash risks. Accident Analysis and Prevention, 128, 230–239. doi:10.1016/j.aap.2019.04.014
  • Plug, C., Xia, J. C., & Caulfield, C. (2011). Spatial and temporal visualisation techniques for crash analysis. Accident Analysis and Prevention, 43(6), 1937–1946. doi:10.1016/j.aap.2011.05.007
  • Song, L., Li, Y., Fan, W., & Wu, P. (2020). Modeling pedestrian-injury severities in pedestrian-vehicle crashes considering spatiotemporal patterns: insights from different hierarchical bayesian random-effects models. Analytic Methods in Accident Research, 100137.
  • Stipancic, J., Miranda-Moreno, L., Saunier, N., & Labbe, A. (2019). Network screening for large urban road networks: Using GPS data and surrogate measures to model crash frequency and severity. Accident Analysis and Prevention, 125, 290–231. doi:10.1016/j.aap.2019.02.016
  • Sun, M., Sun, X., & Shan, D. (2019). Pedestrian crash analysis with latent class clustering method. Accident Analysis and Prevention, 124, 50–57. doi:10.1016/j.aap.2018.12.016
  • Xie, Z., & Yan, J. (2013). Detecting traffic accident clusters with network kernel density estimation and local spatial statistics: an integrated approach. Journal of Transport Geography, 31, 64–71. doi:10.1016/j.jtrangeo.2013.05.009
  • Yang, J., & Li, Q. (2012). Progress in industrial and civil engineering (Part 1). Switzerland: Trans Tech Publications.
  • Ye, W., Ma, Z., & Ha, X. (2018). Spatial-temporal patterns of PM2.5 concentrations for 338 Chinese cities. The Science of the Total Environment, 631-632, 524–533. 631e632, 524e533. doi:10.1016/j.scitotenv.2018.03.057
  • Youngok, K., Nahye, C., Serin, S., & Peng, C. (2018). Spatiotemporal characteristics of elderly population’s traffic accidents in Seoul using space-time cube and space-time kernel density estimation. PLOS One, 13(5), e0196845.
  • Yu, H., Liu, P., Chen, J., & Wang, H. (2014). Comparative analysis of the spatial analysis methods for hotspot identification. Accident Analysis and Prevention, 66, 80–88. doi:10.1016/j.aap.2014.01.017
  • Yu, R., Wang, X., & Abdel-Aty, M. (2017). A hybrid latent class analysis modeling approach to analyze urban expressway crash risk. Accident Analysis and Prevention, 101, 37–43. doi:10.1016/j.aap.2017.02.002
  • Ziakopoulos, A., & Yannis, G. (2020). A review of spatial approaches in road safety. Accident Analysis and Prevention, 135, 105323. doi:10.1016/j.aap.2019.105323

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.