Spatial insights into micro-mobility safety: establishing optimal buffers for scooter crash predictions

Establishing comparison events/crashes is among the key challenges in safety analysis. This study proposes a spatial consideration for predicting scooter crashes using Utah's five years of crash data. It involves creating buffers ranging from 5 to 250 ft from the point of the scooter crash to o...

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Main Authors: Boniphace Kutela, Meshack P. Mihayo, Emmanuel Kidando, Tumlumbe Juliana Chengula, Sia M. Lyimo
Format: Article
Language:English
Published: Maximum Academic Press 2024-12-01
Series:Digital Transportation and Safety
Subjects:
Online Access:https://www.maxapress.com/article/doi/10.48130/dts-0024-0016
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author Boniphace Kutela
Meshack P. Mihayo
Emmanuel Kidando
Tumlumbe Juliana Chengula
Sia M. Lyimo
author_facet Boniphace Kutela
Meshack P. Mihayo
Emmanuel Kidando
Tumlumbe Juliana Chengula
Sia M. Lyimo
author_sort Boniphace Kutela
collection DOAJ
description Establishing comparison events/crashes is among the key challenges in safety analysis. This study proposes a spatial consideration for predicting scooter crashes using Utah's five years of crash data. It involves creating buffers ranging from 5 to 250 ft from the point of the scooter crash to obtain comparison crashes. The appropriate variables were selected based on the literature and engineering judgment. The Binary Logistic Regression was then applied to determine the appropriate buffer based on the consistency in the direction and magnitude of the impact of predictor variables. Results indicate that three variables, the junction type, lighting condition, and weather condition, are susceptible to changes in the direction of impact. Moreover, the study findings reveal that as the buffer distance increases, the magnitude of the impact of the variables decreases. Based on the results, a buffer of less than 50 ft is deemed appropriate for various analyses due to consistency in direction and the magnitude of impact. Further, the study findings show that intersections, dark-lighted conditions, summer season, and right-turning movements are more likely to be associated with scooter crashes. These findings can be crucial to transportation agencies and practitioners in improving the safety of scooter riders.
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spelling doaj-art-1b0ed4eb6faa41bcaadc7acbef6cbaa72025-08-20T02:27:19ZengMaximum Academic PressDigital Transportation and Safety2837-78422024-12-013418419010.48130/dts-0024-0016dts-0024-0016Spatial insights into micro-mobility safety: establishing optimal buffers for scooter crash predictionsBoniphace Kutela0Meshack P. Mihayo1Emmanuel Kidando2Tumlumbe Juliana Chengula3Sia M. Lyimo4Texas A&M Transportation Institute, 701 N Post Oak Ln #430, Houston, TX 77024, USADepartment of Civil and Environmental Engineering, Cleveland State University, 2121 Euclid Avenue, Cleveland, OH 44115, USADepartment of Civil and Environmental Engineering, Cleveland State University, 2121 Euclid Avenue, Cleveland, OH 44115, USASouth Carolina State University, 300 College Avenue, Orangeburg, SC 29117, USAProgressive AE, 1811 4 Mile Rd NE, Grand Rapids, MI 49525, USAEstablishing comparison events/crashes is among the key challenges in safety analysis. This study proposes a spatial consideration for predicting scooter crashes using Utah's five years of crash data. It involves creating buffers ranging from 5 to 250 ft from the point of the scooter crash to obtain comparison crashes. The appropriate variables were selected based on the literature and engineering judgment. The Binary Logistic Regression was then applied to determine the appropriate buffer based on the consistency in the direction and magnitude of the impact of predictor variables. Results indicate that three variables, the junction type, lighting condition, and weather condition, are susceptible to changes in the direction of impact. Moreover, the study findings reveal that as the buffer distance increases, the magnitude of the impact of the variables decreases. Based on the results, a buffer of less than 50 ft is deemed appropriate for various analyses due to consistency in direction and the magnitude of impact. Further, the study findings show that intersections, dark-lighted conditions, summer season, and right-turning movements are more likely to be associated with scooter crashes. These findings can be crucial to transportation agencies and practitioners in improving the safety of scooter riders.https://www.maxapress.com/article/doi/10.48130/dts-0024-0016scooter safetymicro-mobilityspatial analysisoptimal buffer
spellingShingle Boniphace Kutela
Meshack P. Mihayo
Emmanuel Kidando
Tumlumbe Juliana Chengula
Sia M. Lyimo
Spatial insights into micro-mobility safety: establishing optimal buffers for scooter crash predictions
Digital Transportation and Safety
scooter safety
micro-mobility
spatial analysis
optimal buffer
title Spatial insights into micro-mobility safety: establishing optimal buffers for scooter crash predictions
title_full Spatial insights into micro-mobility safety: establishing optimal buffers for scooter crash predictions
title_fullStr Spatial insights into micro-mobility safety: establishing optimal buffers for scooter crash predictions
title_full_unstemmed Spatial insights into micro-mobility safety: establishing optimal buffers for scooter crash predictions
title_short Spatial insights into micro-mobility safety: establishing optimal buffers for scooter crash predictions
title_sort spatial insights into micro mobility safety establishing optimal buffers for scooter crash predictions
topic scooter safety
micro-mobility
spatial analysis
optimal buffer
url https://www.maxapress.com/article/doi/10.48130/dts-0024-0016
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AT meshackpmihayo spatialinsightsintomicromobilitysafetyestablishingoptimalbuffersforscootercrashpredictions
AT emmanuelkidando spatialinsightsintomicromobilitysafetyestablishingoptimalbuffersforscootercrashpredictions
AT tumlumbejulianachengula spatialinsightsintomicromobilitysafetyestablishingoptimalbuffersforscootercrashpredictions
AT siamlyimo spatialinsightsintomicromobilitysafetyestablishingoptimalbuffersforscootercrashpredictions