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Effect of Roadside Features on Injury Severity of Traffic Accidents

  • Biswajeet PradhanEmail author
  • Maher Ibrahim Sameen
Chapter
Part of the Advances in Science, Technology & Innovation book series (ASTI)

Abstract

Everyone wants safe transportation systems to travel from a place to another easily and securely. However, many issues and challenges make transportation systems less safe than they should be. Among these issues are rapid urbanisation over various landscape forms, population growth and migration of people from rural to urban areas. Other challenges include lack of technical tools that can support road safety managers to efficiently simulate future scenarios and create remarkable plans for solving problems concerning road safety. If these problems continue, then failure of transportation systems will greatly affect the stability and development of modern cities because transportation systems are the heart of the cities. Thus, providing solutions for such problems is among the previous research topics in the fields of transportation and geomatics.

References

  1. Baatz, M., & Schäpe, A. (2000). Multiresolution segmentation: An optimization approach for high quality multi-scale image segmentation (eCognition).Google Scholar
  2. Ben-Bassat, T., & Shinar, D. (2011). Effect of shoulder width, guardrail and roadway geometry on driver perception and behavior. Accident Analysis and Prevention, 43(6), 2142–2152.CrossRefGoogle Scholar
  3. Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2–16.CrossRefGoogle Scholar
  4. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.CrossRefGoogle Scholar
  5. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297.zbMATHGoogle Scholar
  6. Esch, T., Thiel, M., Bock, M., Roth, A., & Dech, S. (2008). Improvement of image segmentation accuracy based on multiscale optimization procedure. IEEE Geoscience and Remote Sensing Letters, 5(3), 463–467.CrossRefGoogle Scholar
  7. Fanos, A. M., & Pradhan, B. (2016). Multi-scenario Rockfall hazard assessment using LiDAR data and GIS. Geotechnical and Geological Engineering, 34(5), 1375–1393.  https://doi.org/10.1007/s10706-016-0049-z.CrossRefGoogle Scholar
  8. Fanos, A. M., & Pradhan, B. (2018). Laser scanning systems and techniques in rockfall source identification and risk assessment: A critical review. Earth Systems and Environment (Article online first available).  https://doi.org/10.1007/s41748-018-0046-x.
  9. Fanos, A. M., Pradhan, B., Aziz, A. A., Jebur, M. N., & Park, H. -J. (2016). Assessment of multi-scenario rockfall hazard based on mechanical parameters using high-resolution airborne laser scanning data and GIS in a tropical area. Environmental Earth Sciences, 75:1129. http://dx.doi.org/1007/s12665-016-5936-3.
  10. Fanos, A. M., Pradhan, B., Mansor, S., Yusoff, Z. M., & Abdullah, A. F. B. (2018). A hybrid model using machine learning methods and GIS for potential rockfall source identification from airborne laser scanning data. Landslides (pp. 1–18) (online first available).  https://doi.org/10.1007/s10346-018-0990-4.
  11. Gong, J., Zhou, H., Gordon, C., & Jalayer, M. (2012, June). Mobile terrestrial laser scanning for highway inventory data collection. In Proceedings of International Conference on Computing in Civil Engineering (pp. 17–20).‏ Clearwater Beach, FL, USA.Google Scholar
  12. Idrees, M. O., & Pradhan, B. (2016). A decade of modern cave surveying with terrestrial laser scanning: A review of sensors, method and application development. International Journal of Speleology, 45(1), 71–88.  https://doi.org/10.5038/1827-806X.45.1.1923.CrossRefGoogle Scholar
  13. Idrees, M. O., & Pradhan, B. (2018). Geostructural stability assessment of cave using rock surface discontinuity extracted from terrestrial laser scanning point cloud. Journal of Rock Mechanics and Geotechnical Engineering, 10(3), 534–544.  https://doi.org/10.1016/j.jrmge.2017.11.011.CrossRefGoogle Scholar
  14. Idrees, M. O., Pradhan, B., Buchroithner, M. F., Shafri, H. Z. M., & Bejo, S. K. (2016). Assessing the transferability of a hybrid Taguchi-objective function method to optimize image segmentation for detecting and counting cave roosting birds using terrestrial laser scanning data. Journal of Applied Remote Sensing, 10(3), 035023.  https://doi.org/10.1117/1.JRS.10.035023.CrossRefGoogle Scholar
  15. Jalayer, M., Gong, J., Zhou, H., & Grinter, M. (2015). Evaluation of remote sensing technologies for collecting roadside feature data to support highway safety manual implementation. Journal of Transportation Safety & Security, 7(4), 345–357.CrossRefGoogle Scholar
  16. McCarthy, T., Fotheringham, S., Charlton, M., Winstanley, A. C., & O’Malley, V. (2007). Integration of LIDAR and stereoscopic imagery for route corridor surveying. Mobile Mapping Technology, 37, 1125–1130.Google Scholar
  17. Mezaal, M. R., & Pradhan, B. (2017). Data mining-aided automatic landslide detection using airborne laser scanning data in densely forested tropical areas. Korean Journal of Remote Sensing, 34(1), 45–74.  https://doi.org/10.7780/kjrs.2018.34.1.4.CrossRefGoogle Scholar
  18. Mezaal, M. R., & Pradhan, B. (2018). An improved algorithm for identifying shallow and deep-seated landslides in dense tropical forest from airborne laser scanning data. CATENA, 167, 147–159.  https://doi.org/10.1016/j.catena.2018.04.038.CrossRefGoogle Scholar
  19. Mezaal, M. R., Pradhan, B., Sameen, M. I., Shafri, H. Z. M., & Yusoff, Z. M. (2017a). Optimized neural architecture for automatic landslide detection from high-resolution airborne laser scanning data. Applied Sciences, 7(7), 730.CrossRefGoogle Scholar
  20. Mezaal, M. R., Pradhan, B., Shafri, H. Z. M., & Yusoff, Z. M. (2017b). Automatic landslide detection using Dempster-Shafer theory from LiDAR-derived data and orthophotos. Geomatics, Natural Hazards and Risk, 8(2), 1935–1954.  https://doi.org/10.1080/19475705.2017.1401013.CrossRefGoogle Scholar
  21. Mezaal, M. R., Pradhan, B., & Rizeei, H. M. (2018). Improving landslide detection from airborne laser scanning data using optimized Dempster-Shafer. Remote Sensing, 10(7), 1029.  https://doi.org/10.3390/rs10071029.CrossRefGoogle Scholar
  22. Müllerová, J., Pergl, J., & Pyšek, P. (2013). Remote sensing as a tool for monitoring plant invasions: Testing the effects of data resolution and image classification approach on the detection of a model plant species Heracleum mantegazzianum (giant hogweed). International Journal of Applied Earth Observation and Geoinformation, 25, 55–65.CrossRefGoogle Scholar
  23. Ozdemir, A. (2016). Sinkhole susceptibility mapping using logistic regression in Karapınar (Konya, Turkey). Bulletin of Engineering Geology and the Environment, 75(2), 681–707.  https://doi.org/10.1007/s10064-015-0778-x.CrossRefGoogle Scholar
  24. Pradhan, B., Jebur, M. N., Shafri, H. Z. M., & Tehrany, M. S. (2015). Data fusion technique using wavelet transform and Taguchi methods for automatic landslide detection from airborne laser scanning data and quickbird satellite imagery. IEEE Transactions on Geoscience and Remote Sensing.Google Scholar
  25. Rifaat, S. M., Tay, R., & de Barros, A. (2011). Effect of street pattern on the severity of crashes involving vulnerable road users. Accident Analysis and Prevention, 43(1), 276–283.CrossRefGoogle Scholar
  26. Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., & Rigol-Sanchez, J. P. (2012). An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS Journal of Photogrammetry and Remote Sensing, 67, 93–104.CrossRefGoogle Scholar
  27. Samui, P. (2008). Slope stability analysis: A support vector machine approach. Environmental Geology, 56, 255–267.CrossRefGoogle Scholar
  28. Savolainen, P. T., Mannering, F. L., Lord, D., & Quddus, M. A. (2011). The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives. Accident Analysis and Prevention, 43(5), 1666–1676.CrossRefGoogle Scholar
  29. Theofilatos, A., Graham, D., & Yannis, G. (2012). Factors affecting accident severity inside and outside urban areas in Greece. Traffic Injury Prevention, 13(5), 458–467.CrossRefGoogle Scholar
  30. Venkataraman, N., Ulfarsson, G. F., & Shankar, V. N. (2013). Random parameter models of interstate crash frequencies by severity, number of vehicles involved, collision and location type. Accident Analysis and Prevention, 59, 309–318.CrossRefGoogle Scholar
  31. Wang, Y. G., Chen, K. M., Ci, Y. S., & Hu, L. W. (2011). Safety performance audit for roadside and median barriers using freeway crash records: Case study in Jiangxi, China. Scientia Iranica, 18(6), 1222–1230.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS)University of Technology SydneySydneyAustralia
  2. 2.Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS)University of Technology SydneySydneyAustralia

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