Skip to main content
SpringerLink
Log in

Weighted Multi-attribute Framework to Identify Freeway Incident Hot Spots in a Spatiotemporal Context

  • Research Article - Civil Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

The present work used the analytical hierarchy process supported by a machine learning technique (namely random forest variable importance) in a geospatial environment to identify traffic-incident hot spots where seven incident attributes (incident severity level, vehicle type, number of vehicles involved, number of weather condition factors, number of lanes blocked, time period, and incident duration) and incident frequency were considered. Using a frame of raster representation, each factor was ranked and weighted based on the literature, expert surveys, and random forest technique to perform hot spot analysis and generate a critical road index map. Statistical analysis of spatial clustering and hot spot spatial densities was carried out based on Moran’s I method of spatial autocorrelation, Getis–Ord Gi* statistics, and point kernel density. Based on weighted attributes of more than 130,000 incidents from Harris County, Texas, for the period between 2004 and 2013, the method successfully identified critical road segments and highlighted factors that contribute to criticality. The results show that high-severity segments are located in the downtown area during the period of 2004 to 2010, and shifted northwest of the downtown area during the period of 2010 to 2013. The random forest variable importance revealed that the number of lanes blocked along with time periods was the most critical factor affecting the severity of traffic incidents. Though the application of the method was demonstrated using incident database from Harris County, it can be generalized to other cities. Traffic agencies can use the suggested approach to garner and visualize reliable information that ensures better traffic management schemes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Junhua, W.; Haozhe, C.; Shi, Q.: Estimating freeway incident duration using accelerated failure time modeling. Saf. Sci. 54, 43–50 (2013). https://doi.org/10.1016/j.ssci.2012.11.009

    Article  Google Scholar 

  2. Lin, L.; Wang, Q.; Sadek, A.W.: A combined M5P tree and hazard-based duration model for predicting urban freeway traffic accident durations. Accid. Anal. Prev. 91, 114–126 (2016). https://doi.org/10.1016/j.aap.2016.03.001

    Article  Google Scholar 

  3. Eckley, D.C.; Curtin, K.M.: Evaluating the spatiotemporal clustering of traffic incidents. Comput. Environ. Urban Syst. (2012). https://doi.org/10.1016/j.compenvurbsys.2012.06.004

    Google Scholar 

  4. Wang, J.; Liu, B.; Fu, T.; Liu, S.; Stipancic, J.: Modeling when and where a secondary accident occurs. Accid. Anal. Prev. (2018). https://doi.org/10.1016/j.aap.2018.01.024

    Google Scholar 

  5. Cheng, Z.; Zu, Z.; Lu, J.: Traffic crash evolution characteristic analysis and spatiotemporal hotspot identification of urban road intersections. Sustainability 11, 1–17 (2019). https://doi.org/10.3390/su11010160

    Google Scholar 

  6. Colak, H.E.; Memisoglu, T.; Erbas, Y.S.; Bediroglu, S.: Hot spot analysis based on network spatial weights to determine spatial statistics of traffic accidents in Rize, Turkey. Arabian J. Geosci. 11, 151 (2018)

    Article  Google Scholar 

  7. Hamad, K.; Quiroga, C.: Spatial analysis of freeway incidents and incident detection. In: Transportation Research Board 86th Annual Meeting Proceedings, Transportation Research Board. Washington, DC (2007).

  8. Hamad, K.; Quiroga, C.: Geovisualization of archived ITS data-case studies. IEEE Trans. Intell. Transp. Syst. 17, 104–112 (2016). https://doi.org/10.1109/TITS.2015.2460995

    Article  Google Scholar 

  9. Khattak, A.J.; Wang, X.; Zhang, H.: Spatial analysis and modeling of traffic incidents for proactive incident management and strategic planning. Transp. Res. Rec. 2178, 128–137 (2010). https://doi.org/10.3141/2178-14

    Article  Google Scholar 

  10. Songchitruksa, P.; Zeng, X.: Getis–Ord spatial statistics to identify hot spots by using incident management data. Transp. Res. Rec. J. Transp. Res. Board 2165, 42–51 (2010). https://doi.org/10.3141/2165-05

    Article  Google Scholar 

  11. Erdogan, S.; Yilmaz, I.; Baybura, T.; Gullu, M.: Geographical information systems aided traffic accident analysis system case study: city of Afyonkarahisar. Accid. Anal. Prev. 40, 174–181 (2008). https://doi.org/10.1016/j.aap.2007.05.004

    Article  Google Scholar 

  12. Truong, L.T.; Somenahalli, S.V.C.: Using GIS to identify pedestrian-vehicle crash hot spots and unsafe bus stops. J. Public Transp. 14, 99–114 (2011)

    Article  Google Scholar 

  13. Kaygisiz, Ö.; Düzgün, Ş.; Yildiz, A.; Senbil, M.: Spatio-temporal accident analysis for accident prevention in relation to behavioral factors in driving: the case of South Anatolian Motorway. Transp. Res. Part F Traffic Psychol. Behav. 33, 128–140 (2015). https://doi.org/10.1016/j.trf.2015.07.002

    Article  Google Scholar 

  14. Chung, Y.; Recker, W.W.: Spatiotemporal analysis of traffic congestion caused by rubbernecking at freeway accidents. IEEE Trans. Intell. Transp. Syst. 14, 1416–1422 (2013). https://doi.org/10.1109/TITS.2013.2261987

    Article  Google Scholar 

  15. Benedek, J.; Marian, S.; Cristian, T.: Hotspots and social background of urban traffic crashes: a case study in Cluj-Napoca (Romania). Accid. Anal. Prev. 87, 117–126 (2016). https://doi.org/10.1016/j.aap.2015.11.026

    Article  Google Scholar 

  16. Prasannakumar, V.; Vijith, H.; Charutha, R.; Geetha, N.: Spatio-temporal clustering of road accidents: GIS based analysis and assessment. Proc. Soc. Behav. Sci. 21, 317–325 (2011). https://doi.org/10.1016/j.sbspro.2011.07.020

    Article  Google Scholar 

  17. Harirforoush, H.; Bellalite, L.: A new integrated GIS-based analysis to detect hotspots: a case study of the city of Sherbrooke. Accid. Anal. Prev. (2016). https://doi.org/10.1016/j.aap.2016.08.015

    Google Scholar 

  18. Anderson, T.K.: Kernel density estimation and K-means clustering to profile road accident hotspots. Accid. Anal. Prev. 41, 359–364 (2009). https://doi.org/10.1016/j.aap.2008.12.014

    Article  Google Scholar 

  19. Xie, Z.; Yan, J.: Computers, environment and urban systems: kernel density estimation of traffic accidents in a network space. Comput. Environ. Urban Syst. 32, 396–406 (2008). https://doi.org/10.1016/j.compenvurbsys.2008.05.001

    Article  Google Scholar 

  20. Loo, B.P.Y.: Validating crash locations for quantitative spatial analysis: a GIS-based approach. Accid. Anal. Prev. 38, 879–886 (2006). https://doi.org/10.1016/j.aap.2006.02.012

    Article  Google Scholar 

  21. Anderson, T.: Comparison of spatial methods for measuring road accident “hotspots”: a case study of London. J. Maps 3, 55–63 (2007). https://doi.org/10.1080/jom.2007.9710827

    Article  Google Scholar 

  22. Shariff, S.S.R.; Maad, H.A.; Halim, N.N.A.; Derasit, Z.: Determining hotspots of road accidents using spatial analysis. Indones. J. Electr. Eng. Comput. Sci. 9, 146–151 (2018). https://doi.org/10.11591/ijeecs.v9.i1.pp146-151

    Article  Google Scholar 

  23. United States Census Bureau: 2010 Census Gazetteer Files. https://www.census.gov/geo/maps-data/data/gazetteer2010.html. Accessed July 2017

  24. Jeffrey, T.P.: Chicago, Detroit, Baltimore Lead Nation in Population Loss; Maricopa County Has Biggest Gain. (2017). https://www.cnsnews.com/news/article/terence-p-jeffrey/chicago-detroit-baltimore-lead-nation-population-loss-maricopa-county. Accessed July 2017

  25. Texas Department of Transportation: Crashes and Injuries by County. (2017). http://ftp.dot.state.tx.us/pub/txdot-info/trf/crash_statistics/2016/12.pdf. Accessed July 2017

  26. Briggs, V.; Jasper, K.: Organizing for regional transportation operations: Houston TranStar. Report No. FHWA-OP-01-139. (2001)

  27. Khattak, A.J.; Liu, J.; Wali, B.; Li, X.; Ng, M.: Modeling traffic incident duration using quantile regression. Transp. Res. Rec. J. Transp. Res. Board 2554, 139–148 (2016). https://doi.org/10.3141/2554-15

    Article  Google Scholar 

  28. Chang, H.; Chang, T.: Prediction of freeway incident duration based on classification tree analysis. J. East. Asia Soc. Transp. Stud. 10, 1964–1977 (2013)

    Google Scholar 

  29. Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001). https://doi.org/10.1017/CBO9781107415324.004

    Article  MATH  Google Scholar 

  30. Naghibi, S.A.; Pourghasemi, H.R.; Dixon, B.: GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environ. Monit. Assess. 188, 1–27 (2016). https://doi.org/10.1007/s10661-015-5049-6

    Article  Google Scholar 

  31. Rahmati, O.; Pourghasemi, H.R.; Melesse, A.M.: Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: a case study at Mehran Region, Iran. Catena 137, 360–372 (2016). https://doi.org/10.1016/j.catena.2015.10.010

    Article  Google Scholar 

  32. Han, H.; Guo, X.; Yu, H.: Variable selection using mean decrease accuracy and mean decrease gini based on random forest. In: Proceedings of the IEEE International Conference on Software Engineering and Service Sciences, ICSESS, pp. 219–224. IEEE (2017).

  33. Mitra, S.: Spatial autocorrelation and Bayesian spatial statistical method for analyzing intersections prone to injury crashes. Transp. Res. Rec. J. Transp. Res. Board 2136, 92–100 (2009). https://doi.org/10.3141/2136-11

    Article  Google Scholar 

  34. Soltani, A.; Askari, S.: Exploring spatial autocorrelation of traffic crashes based on severity. Injury 48, 637–647 (2017). https://doi.org/10.1016/j.injury.2017.01.032

    Article  Google Scholar 

  35. Ord, J.K.; Getis, A.: Local spatial autocorrelation statistics: distributional issues and an application. Geogr. Anal. 27, 286–306 (1995). https://doi.org/10.1111/j.1538-4632.1995.tb00912.x

    Article  Google Scholar 

  36. Peeters, A.; Zude, M.; Käthner, J.; Ünlü, M.; Kanber, R.; Hetzroni, A.; Gebbers, R.; Ben-Gal, A.: Getis–Ord’s hot- and cold-spot statistics as a basis for multivariate spatial clustering of orchard tree data. Comput. Electron. Agric. 111, 140–150 (2015). https://doi.org/10.1016/j.compag.2014.12.011

    Article  Google Scholar 

  37. Barrell, J.; Grant, J.: Detecting hot and cold spots in a seagrass landscape using local indicators of spatial association. Landsc.Ecol. 28, 2005–2018 (2013). https://doi.org/10.1007/s10980-013-9937-2

    Article  Google Scholar 

  38. Yu, H.; Liu, P.; Chen, J.; Wang, H.: Comparative analysis of the spatial analysis methods for hotspot identification. Accid. Anal. Prev. 66, 80–88 (2014). https://doi.org/10.1016/j.aap.2014.01.017

    Article  Google Scholar 

  39. Chen, X.; Huang, L.; Dai, D.; Zhu, M.; Jin, K.: Hotspots of road traffic crashes in a redeveloping area of Shanghai. Int. J. Inj. Control Saf. Promot. 25, 293–302 (2018). https://doi.org/10.1080/17457300.2018.1431938

    Article  Google Scholar 

  40. Dereli, M.A.; Erdogan, S.: A new model for determining the traffic accident black spots using GIS-aided spatial statistical methods. Transp. Res. Part A Pol. Pract. 103, 106–117 (2017). https://doi.org/10.1016/j.tra.2017.05.031

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to recognize the support received from Houston TranStar and Texas A&M Transportation Institute in providing the data utilized to conduct this research study.

Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, University of Sharjah, Sharjah, United Arab Emirates

    Rami Al-Ruzouq, Khaled Hamad, Saleh Abu Dabous & Waleed Zeiada

  2. Sustainable Civil Infrastructure Research Group, Research Institute of Sciences and Engineering, University of Sharjah, Sharjah, United Arab Emirates

    Rami Al-Ruzouq, Khaled Hamad, Saleh Abu Dabous, Waleed Zeiada & Mohamad Ali Khalil

  3. Texas A&M Transportation Institute, Houston, TX, USA

    Tony Voigt

Authors
  1. Rami Al-Ruzouq
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Khaled Hamad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Saleh Abu Dabous
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Waleed Zeiada
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mohamad Ali Khalil
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tony Voigt
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rami Al-Ruzouq.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Al-Ruzouq, R., Hamad, K., Abu Dabous, S. et al. Weighted Multi-attribute Framework to Identify Freeway Incident Hot Spots in a Spatiotemporal Context. Arab J Sci Eng 44, 8205–8223 (2019). https://doi.org/10.1007/s13369-019-03881-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-019-03881-z

Keywords

Search

Navigation