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Computing traffic accident high-risk locations using graph analytics

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Abstract

Several studies on traffic accident hot spots have adopted spatial statistics and Geographic Information Systems where spatial point patterns are modelled solely on spatial dependence without consideration of the temporal dependence of the events. This could lead to under-estimation or over-estimation of results because of the temporal aggregation of the events to an absolute time point. Furthermore, traffic accidents are usually considered as events occurring randomly in two-dimensional geographic space. However, traffic accidents are network-constrained events. In this study, we adopt the connectivity of graph on a network space approach that identifies accident high risk-locations based on space–time-varying connectivity between traffic accident events and the road network geometry. A simple but extensible traffic accident space time-varying graph (STVG) model is developed and implemented using traffic accident data from 2010 to 2015 for Brevard County in Florida, United States. Traffic accident high risk-locations were identified and ranked in space and time using time-dependent degree centrality and PageRank centrality graph metrics respectively through time-incremental graph queries. In the analysis, traffic accident patterns were discovered based on network graph analytics. Our findings offer a new and efficient approach for identifying, ranking and profiling accident-prone areas in space and time at different scales.

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References

  1. Moons, E., Brijs, T., & Wets, G. (2009). Improving Moran’s index to identify hot spots in traffic safety. Geocomputation and Urban Planning. https://doi.org/10.1007/978-3-540-89930-3_7

    Article  Google Scholar 

  2. Bíl, M., Andrášik, R., & Sedoník, J. (2019). A detailed spatiotemporal analysis of traffic crash hotspots. Applied Geography, 107, 82–90. https://doi.org/10.1016/j.apgeog.2019.04.008

    Article  Google Scholar 

  3. Le, K. G., Liu, P., & Lin, L. (2020). Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques sin Hanoi Vietnam. Geo-spatial Information Science, 23(2), 153–164. https://doi.org/10.1080/10095020.2019.1683437

    Article  Google Scholar 

  4. Moran, P. A. (1948). The interpretation of statistical maps. Journal, Source Statistical, Royal Series, Society, 38(2), 189–203.

    Google Scholar 

  5. Getis, A., & Ord, J. K. (1992). The Analysis of Spatial association by use of distance statistics. Geographical Analysis, 24(3), 189–206. https://doi.org/10.1111/j.1538-4632.1992.tb00261.x

    Article  Google Scholar 

  6. Hashimoto, S., Yoshiki, S., Saeki, R., Mimura, Y., Ando, R., & Nanba, S. (2016). Development and application of traffic accident density estimation models using kernel density estimation. Journal of Traffic and Transportation Engineering, 3(3), 262–270. https://doi.org/10.1016/j.jtte.2016.01.005

    Article  Google Scholar 

  7. Caliendo, C., Guida, M., Postiglione, F., et al. (2021). A Bayesian bivariate hierarchical model with correlated parameters for the analysis of road crashes in Italian tunnels. Statistical Methods and Applications. https://doi.org/10.1007/s10260-021-00567-5

    Article  Google Scholar 

  8. Ma, L., Yan, X., & Qiao, W. (2014). A quasi-poisson approach on modeling accident hazard index for urban road segments. Discrete Dynamics in Nature and Society. https://doi.org/10.1155/2014/489052

    Article  Google Scholar 

  9. Tang, L., Kan, Z., Zhang, X., Sun, F., Yang, X., & Li, Q. (2016). A network Kernel Density Estimation for linear features in space–time analysis of big trace data. International Journal of Geographical Information Science, 30(9), 1717–1737. https://doi.org/10.1080/13658816.2015.1119279

    Article  Google Scholar 

  10. Satria, R., & Castro, M. (2016). GIS Tools for Analyzing Accidents and Road Design: A Review. Transportation Research Procedia, 18, 242–247. https://doi.org/10.1016/j.trpro.2016.12.033

    Article  Google Scholar 

  11. Zhang, Z., Ming, Y., & Song, G. (2019). Identify road clusters with high-frequency crashes using spatial data mining approach. Applied Sciences, 9(24), 5282. https://doi.org/10.3390/app9245282

    Article  Google Scholar 

  12. Okabe, A., Satoh, T., & Sugihara, K. (2009). A kernel density estimation method for networks, its computational method and a GIS-based tool. International Journal of Geographical Information Science, 23(1), 7–32. https://doi.org/10.1080/13658810802475491

    Article  Google Scholar 

  13. Thakali, L., Kwon, T. J., & Fu, L. (2015). Identification of crash hotspots using kernel density estimation and kriging methods: A comparison. Journal of Modern Transportation, 23, 93–106. https://doi.org/10.1007/s40534-015-0068-0

    Article  Google Scholar 

  14. 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. https://doi.org/10.1016/j.jtrangeo.2013.05.009

    Article  Google Scholar 

  15. Mohaymany, A. S., Shahri, M., & Mirbagheri, B. (2013). GIS-based method for detecting high-crash-risk road segments using network kernel density estimation. Geo-spatial Information Science, 16(2), 113–119. https://doi.org/10.1080/10095020.2013.766396

    Article  Google Scholar 

  16. Aguero-Valverde, J., & Jovanis, P. P. (2006). Spatial analysis of fatal and injury crashes in Pennsylvania. Accident Analysis and Prevention, 38(3), 618–625. https://doi.org/10.1016/j.aap.2005.12.006

    Article  Google Scholar 

  17. Zeng, Q., Sun, J., & Wen, H. (2017). Bayesian Hierarchical Modeling Monthly Crash Counts on Freeway Segments with Temporal Correlation. Journal of Advanced Transportation, 2017, 8p. https://doi.org/10.1155/2017/5391054

    Article  Google Scholar 

  18. Fawcett, L., Thorpe, N., Matthews, J., & Kremer, K. (2017). A novel Bayesian hierarchical model for road safety hotspot prediction. Accident Analysis & Prevention. https://doi.org/10.1016/j.aap.2016.11.021

    Article  Google Scholar 

  19. Hosseinlou, M. H., & Sohrabi, M. (2009). Predicting and identifying traffic hot spots applying neuro-fuzzy systems in intercity roads. International Journal of Environmental Science and Technology, 6, 309–314. https://doi.org/10.1007/BF03327634

    Article  Google Scholar 

  20. Effati, M., Rajabi, M. A., Samadzadegan, F., & Shabani, S. (2014). Transportation A geospatial neuro-fuzzy approach for identification of hazardous zones in regional transportation corridors. International Journal of Civil Engineering, 12(3), 289–303.

    Google Scholar 

  21. 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. https://doi.org/10.1016/j.aap.2017.04.007

    Article  Google Scholar 

  22. García de Soto, B., Bumbacher, A., Deublein, M., & Adey, B. T. (2018). Predicting road traffic accidents using artificial neural network models. Infrastructure Asset Management, 5(4), 132–144. https://doi.org/10.1680/jinam.17.00028

    Article  Google Scholar 

  23. Pradhan, B., & Ibrahim, S. M. (2020). Review of traffic accident predictions with neural networks. In B. Pradhan & M. Ibrahim Sameen (Eds.), Laser scanning systems in highway and safety assessment. Advances in science, technology & innovation. Springer.

    Google Scholar 

  24. Gutierrez-Osorio, C., & Pedraza, C. (2020). Modern data sources and techniques for analysis and forecast of road accidents: A review. Journal of Traffic and Transportation Engineering, 7(4), 432–446. https://doi.org/10.1016/j.jtte.2020.05.002

    Article  Google Scholar 

  25. Moghaddam Gilani, V. N., Hosseinian, S. M., Ghasedi, M., & Nikookar, M. (2021). Data-Driven Urban Traffic Accident Analysis and Prediction Using Logit and Machine Learning-Based Pattern Recognition Models. Mathematical Problems in Engineering. https://doi.org/10.1155/2021/9974219

    Article  Google Scholar 

  26. Erdogan, S., Yilmaz, I., Baybura, T., & Gullu, M. (2008). Geographical information systems aided traffic accident analysis system case study: City of Afyonkarahisar. Accident Analysis and Prevention, 40(1), 174–181. https://doi.org/10.1016/j.aap.2007.05.004

    Article  Google Scholar 

  27. Truong, L. T., & Somenahalli, S. V. (2011). Using GIS to Identify Pedestrian-Vehicle Crash Hot Spots and Unsafe Bus Stops. Journal of Public Transportation, 14(1), 99–114. https://doi.org/10.5038/2375-0901.14.1.6

    Article  Google Scholar 

  28. Kuo, P. F., Lord, D., & Walden, T. D. (2013). Using geographical information systems to organize police patrol routes effectively by grouping hotspots of crash and crime data. Journal of Transport Geography, 30, 138–148. https://doi.org/10.1016/j.jtrangeo.2013.04.006

    Article  Google Scholar 

  29. Rankavat, S., & Tiwari, G. (2013). Pedestrian Accident Analysis in Delhi using GIS. Journal of the Eastern Asia Society for Transportation Studies, 10, 1446–1457.

    Google Scholar 

  30. Tortum, A., & Atalay, A. (2015). Spatial analysis of road mortality rates in Turkey. Transport, 168(6), 532–542. https://doi.org/10.1680/jtran.14.00029

    Article  Google Scholar 

  31. Aghajani, M. A., Dezfoulian, R. S., Arjroody, A. R., & Rezaei, M. (2017). Applying GIS to Identify the Spatial and Temporal Patterns of Road Accidents Using Spatial Statistics (case study: Ilam Province, Iran). Transportation Research Procedia, 25, 2131–2143. https://doi.org/10.1016/j.trpro.2017.05.409

    Article  Google Scholar 

  32. Gatrell, A. C., Bailey, T. C., Diggle, P. J., & Rowlingson, B. S. (1996). Point Spatial application pattern analysis geographical epidemiology. Transactions of the Institute of British Geographers, 21(1), 256–274.

    Article  Google Scholar 

  33. Levine, N. E. D., Kim, K. E., & Nitz, L. H. (1995). Spatial analysis of Honolulu motor vehicle crashes: I. Spatial patterns. Accident Analysis and Prevention, 27(5), 663–674. https://doi.org/10.1016/0001-4575(95)00017-T

    Article  Google Scholar 

  34. Diaz-Varela, E. R., Vazquez-Gonzalez, I., Marey-Pérez, M. F., & Álvarez-López, C. J. (2011). Assessing methods of mitigating wildlife-vehicle collisions by accident characterization and spatial analysis. Transportation Research Part D: Transport and Environment, 16(4), 281–287. https://doi.org/10.1016/j.trd.2011.01.002

    Article  Google Scholar 

  35. Pulugurtha, S. S., Krishnakumar, V. K., & Nambisan, S. S. (2007). New methods to identify and rank high pedestrian crash zones: An illustration. Accident Analysis and Prevention, 39(4), 800–811. https://doi.org/10.1016/j.aap.2006.12.001

    Article  Google Scholar 

  36. Delmelle, E. C., & Thill, J. C. (2008). Urban Bicyclists: Spatial Analysis of Adult and Youth Traffic Hazard Intensity. Transportation Research Record: Journal of the Transportation Research Board, 2074(1), 31–39. https://doi.org/10.3141/2074-04

    Article  Google Scholar 

  37. Anderson, T. K. (2009). Kernel density estimation and K-means clustering to profile road accident hotspots. Accident Analysis and Prevention, 41(3), 359–364. https://doi.org/10.1016/j.aap.2008.12.014

    Article  Google Scholar 

  38. Yalcin, G., & Duzgun, H. S. (2015). Spatial analysis of two-wheeled vehicles traffic crashes: Osmaniye in Turkey. KSCE Journal of Civil Engineering, 19(7), 2225–2232. https://doi.org/10.1007/s12205-015-0661-0

    Article  Google Scholar 

  39. Xie, Z., & Yan, J. (2008). Kernel Density Estimation of traffic accidents in a network space. Computers, Environment and Urban Systems, 32(5), 396–406. https://doi.org/10.1016/j.compenvurbsys.2008.05.001

    Article  Google Scholar 

  40. Okabe, A., Okunuki, K. I., & Shiode, S. (2006). The SANET toolbox: New methods for network spatial analysis. Transactions in GIS, 10(4), 535–550. https://doi.org/10.1111/j.1467-9671.2006.01011.x

    Article  Google Scholar 

  41. Okabe, A., Yomono, H., & Kitamura, M. (1995). Statistical Analysis of the Distribution of Points on a Network. Geographical Analysis, 27(2), 152–175. https://doi.org/10.1111/j.1538-4632.1995.tb00341.x

    Article  Google Scholar 

  42. Yamada, I., & Thill, J. C. (2004). Comparison of planar and network K-functions in traffic accident analysis. Journal of Transport Geography, 12(2), 149–158. https://doi.org/10.1016/j.jtrangeo.2003.10.006

    Article  Google Scholar 

  43. Lu, Y., & Chen, X. (2007). On the false alarm of planar K-function when analyzing urban crime distributed along streets. Social Science Research, 36(2), 611–632. https://doi.org/10.1016/j.ssresearch.2006.05.003

    Article  Google Scholar 

  44. Maduako, I., Wachowicz, M., & Hanson, T. (2019). STVG: An evolutionary graph framework for analyzing fast-evolving networks. J Big Data. https://doi.org/10.1186/s40537-019-0218-z

    Article  Google Scholar 

  45. Maduako, I. D., Wachowicz, M., & Hanson, T. (2019). Transit performance assessment based on graph analytics. Transportmetrica A: Transport Science, 15(2), 1382–1401. https://doi.org/10.1080/23249935.2019.1596991

    Article  Google Scholar 

  46. Ivarsson, T., Kollegger, A., Neubauer, P., Svensson, J., & Webber, J. (2014). The Neo4j manual v1.3. Neo-Technology.

    Google Scholar 

  47. Brin, S., & Page, L. (1998). The anatomy of a large scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30(7), 107–117.

    Article  Google Scholar 

  48. Hansen, D. L., Shneiderman, B., Smith, M. A., & Himelboim, I. (2020). Analyzing social media networks with NodeXL. Insights from a connected world (2nd ed., pp. 31–51). Elsevier.

    Book  Google Scholar 

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Authors and Affiliations

  1. Department of Geoinformatics and Surveying, Faculty of Environmental Studies, University of Nigeria Enugu Campus, Enugu, Nigeria

    Iyke Maduako, Elijah Ebinne, Victus Uzodinma & Emmanuel Chiemelu

  2. Office of Global Innovation, UNICEF, New York, NY, 10017, USA

    Iyke Maduako

  3. Department of Surveying and Geoinformatics, Faculty of Engineering, University of Lagos, Lagos, Nigeria

    Chukwuma Okolie

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  1. Iyke Maduako
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Maduako, I., Ebinne, E., Uzodinma, V. et al. Computing traffic accident high-risk locations using graph analytics. Spat. Inf. Res. 30, 497–511 (2022). https://doi.org/10.1007/s41324-022-00448-3

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