Walking and bicycling are lauded for their negative net carbon impact and for their health benefits. However, national crash statistics suggest that pedestrians are disproportionately harmed in any vehicle–pedestrian conflict situation. Although automated transportation in the future is anticipated to increase overall safety, multiple incidents involving automated vehicles have been reported recently, indicating that the technology needs more training on real-world scenarios and conflicts. This research is motivated by the need for contextual data and related levels of harm in potential conflict scenarios in mixed traffic and we use a national police reported crash dataset, CRSS, to address this need. Our study uses a new gradient boosting algorithm, XGBoost, to identify important features among a host of seemingly significant variables. We compare the performance of XGBoost with the more frequently used random forest method and find that XGBoost is more reliable and robust for handling an unbalanced and sparse dataset like crash data, and the features extracted are more aligned to findings from previous research on the topic. We also compare feature importance between NASS-GES and CRSS—two national crash databases with different sampling strategies but the same objective—and find that sampling strategy influences selection of feature importance. We further use the features extracted using XGBoost in a multiclass logistic regression to quantify the effect of these features on different levels of pedestrian injury. Our findings indicate that speed limit, light conditions, pre-crash movements, and location of pedestrian are important contributors to crash severity, along with driver distraction and impairment.


    Zugriff

    Download

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Modeling Pedestrian Injury Severity: A Case Study of Using Extreme Gradient Boosting Vs Random Forest in Feature Selection


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:
    Wu, Zhenxi (Autor:in) / Misra, Aditi (Autor:in) / Bao, Shan (Autor:in)


    Erscheinungsdatum :

    05.05.2023




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Analyzing Accident Injury Severity via an Extreme Gradient Boosting (XGBoost) Model

    Shubo Wu / Quan Yuan / Zhongwei Yan et al. | DOAJ | 2021

    Freier Zugriff

    Injury Severity Factors - Traffic Pedestrian Collisions

    Tharp,K.J. / Tsongos,N.G. / Univ.of Houston,US et al. | Kraftfahrwesen | 1977


    Injury Severity Factors-Traffic Pedestrian Collisions

    Tharp, K. J. / Tsongos, N. G. | SAE Technical Papers | 1977


    Temporal dynamics of pedestrian injury severity: A seasonally constrained random parameters approach

    Abdulrazaq, Mujeeb Abiola / Fan, Wei David | Elsevier | 2024

    Freier Zugriff

    Injury Severity Analysis in Vehicle-Pedestrian Crashes

    Chung, Younshik / Song, Tai-Jin / Kim, Juyoung | Springer Verlag | 2017