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Development of an algorithm for analysis of routes: Case studies using novice and older drivers.
Zhu, Siyao; Chirles, Theresa J; Keller, Joel A; Hellinger, Andrew; Xu, Yifang; Yenokyan, Gayane; Chang, Chia-Hsiu; Weast, Rebecca; Keller, Jeffrey N; Igusa, Takeru; Ehsani, Johnathon P.
Afiliación
  • Zhu S; College of Civil Engineering, Nanjing Tech University, Nanjing, Jiangsu 211800, China; Department of Civil and Systems Engineering, Johns Hopkins Whiting School of Engineering, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA. Electronic address: zhusiyao@njtech.edu.cn.
  • Chirles TJ; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD 21205, USA.
  • Keller JA; Department of Mathematics, Louisiana State University, Baton Rouge, LA 70803, USA.
  • Hellinger A; Center for Injury Research and Policy, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway, Baltimore, MD 21205, USA.
  • Xu Y; Department of Civil and Environmental Engineering, University of Tennessee, Knoxville. 851 Neyland Drive, Knoxville, TN 37996, USA.
  • Yenokyan G; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, 615 N. Wolfe Street, Baltimore MD 21205, USA.
  • Chang CH; Department of Civil and Systems Engineering, Johns Hopkins Whiting School of Engineering, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA.
  • Weast R; Insurance Institute for Highway Safety, 988 Dairy Rd Ruckersville, VA 22968, USA.
  • Keller JN; Pennington Biomedical Research Center, 6400 Perkins Road, Baton Rouge, LA 70820, USA.
  • Igusa T; Department of Civil and Systems Engineering, Johns Hopkins Whiting School of Engineering, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA.
  • Ehsani JP; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD 21205, USA; Center for Injury Research and Policy, Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway, Baltimore, MD 21205, USA.
J Safety Res ; 90: 319-332, 2024 Sep.
Article en En | MEDLINE | ID: mdl-39251289
ABSTRACT

INTRODUCTION:

This study addresses the lack of methods to quantify driver familiarity with roadways, which poses a higher risk of crashes.

METHOD:

We present a new approach to assessing driving route diversity and familiarity using data from the DrivingApp, a smartphone-based research tool that collects trip-level information, including driving exposure and global positioning system (GPS) data, from young novice drivers (15-19 years old) to older drivers (67-78 years old). Using these data, we developed a GPS data-based algorithm to analyze the uniqueness of driving routes. The algorithm creates same route trip (SRT) arrays by comparing each trip of an identified user, employing statistically determined thresholds for GPS coordinate proximity and trip overlap. The optimal thresholds were established using a General Linear Model (GLM) to examine distance, and repeated observations. The Adjusted Breadth-First Search method is applied to the SRT arrays to prevent double counting or trip omission. The resulting list is classified as geographically distinct routes, or unique routes (URs).

RESULTS:

Manual comparison of algorithm output with geographical maps yielded an overall precision of 0.93 and accuracy of 0.91. The algorithm produces two main outputs a measure of driving diversity (number of URs) and a measure of route-based familiarity derived from the Rescorla-Wagner model. To evaluate the utility of these measures, a Gaussian mixture model clustering algorithm was used on the young novice driver dataset, revealing two distinct groups the low-frequency driving group with lower route familiarity when having higher route diversity, whereas the high-frequency driving group with the opposite pattern. In the older driver group, there was a significant correlation found between the number of URs and Geriatric Depression Score, or walking gait speed. PRACTICAL APPLICATIONS These findings suggest that route diversity and familiarity could complement existing measures to understand driving safety and how driving behavior is related to physical and psychological outcomes.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Conducción de Automóvil / Algoritmos / Sistemas de Información Geográfica Límite: Adolescent / Adult / Aged / Female / Humans / Male Idioma: En Revista: J Safety Res Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Conducción de Automóvil / Algoritmos / Sistemas de Información Geográfica Límite: Adolescent / Adult / Aged / Female / Humans / Male Idioma: En Revista: J Safety Res Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos