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Geospatial and machine learning techniques for wicked social science problems: analysis of crash severity on a regional highway corridor

Author

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  • Meysam Effati

  • Jean-Claude Thill

  • Shahin Shabani

Abstract

The contention of this paper is that many social science research problems are too “wicked” to be suitably studied using conventional statistical and regression-based methods of data analysis. This paper argues that an integrated geospatial approach based on methods of machine learning is well suited to this purpose. Recognizing the intrinsic wickedness of traffic safety issues, such approach is used to unravel the complexity of traffic crash severity on highway corridors as an example of such problems. The support vector machine (SVM) and coactive neuro-fuzzy inference system (CANFIS) algorithms are tested as inferential engines to predict crash severity and uncover spatial and non-spatial factors that systematically relate to crash severity, while a sensitivity analysis is conducted to determine the relative influence of crash severity factors. Different specifications of the two methods are implemented, trained, and evaluated against crash events recorded over a 4-year period on a regional highway corridor in Northern Iran. Overall, the SVM model outperforms CANFIS by a notable margin. The combined use of spatial analysis and artificial intelligence is effective at identifying leading factors of crash severity, while explicitly accounting for spatial dependence and spatial heterogeneity effects. Thanks to the demonstrated effectiveness of a sensitivity analysis, this approach produces comprehensive results that are consistent with existing traffic safety theories and supports the prioritization of effective safety measures that are geographically targeted and behaviorally sound on regional highway corridors. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Meysam Effati & Jean-Claude Thill & Shahin Shabani, 2015. "Geospatial and machine learning techniques for wicked social science problems: analysis of crash severity on a regional highway corridor," Journal of Geographical Systems, Springer, vol. 17(2), pages 107-135, April.
    • Handle: RePEc:kap:jgeosy:v:17:y:2015:i:2:p:107-135
    DOI: 10.1007/s10109-015-0210-x
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    References listed on IDEAS

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    1. Paiva Neto, José B. & Santos, Narciso F. & Orrico Filho, Romulo D., 2025. "Paths to prosperity: How transport networks and income accessibility shape retail location," Journal of Transport Geography, Elsevier, vol. 128(C).
    2. George Grekousis, 2025. "Geographical-XGBoost: a new ensemble model for spatially local regression based on gradient-boosted trees," Journal of Geographical Systems, Springer, vol. 27(2), pages 169-195, April.
    3. Ulak, Mehmet Baran & Ozguven, Eren Erman & Spainhour, Lisa & Vanli, Omer Arda, 2017. "Spatial investigation of aging-involved crashes: A GIS-based case study in Northwest Florida," Journal of Transport Geography, Elsevier, vol. 58(C), pages 71-91.

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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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