Methodology for Identifying Hazardous Driving Longitudinal Events on E-Scooter Using GPS Trajectory Data

Nayeon Kim; Shinhye Joo; Heechan Kang; Sanghoon Kim; Tai-Jin Song

doi:10.7470/jkst.2022.40.5.735

All Issue

2022 Vol.40, Issue 5 Preview Page Next Page

Article

Methodology for Identifying Hazardous Driving Longitudinal Events on E-Scooter Using GPS Trajectory Data 개별주행궤적 데이터 기반 전동킥보드 종방향 위험주행상황 식별 방법론 개발

31 October 2022. pp. 735-749

PDF XML

Abstract

E-Scooter sharing service is the most popular service that facilitates users short or first-and-last mile trips in South Korea. While the number of the service uses is rapidly increasing, various problems are occurring due to insufficient legal systems, especially for traffic accident. However, jurisdictions are trying to improve traffic safety with a focus on regulations without analyzing the cause of accidents in terms of scientific approach. This study develops a methodology for identifying where and how the E-scooter users occur hazardous driving events. GPS-based trajectory data are used for developing the methodology because GPS is embedded on each E-scooter device. Four hazardous longitudinal events are eventually identified in a city. A statistical analysis was conducted assuming that the hazardous condition would be different for each human factor by residential, commercial, and station area. It is our hope that this study provides fundamental information to improve traffic safety on the service.

Keywords

e-scooter

GPS trajectory data

hazardous driving events

personal mobility (pm)

traffic safety

최근 국내에서 First mile/Last mile을 보완할 수단으로 전동킥보드의 공유서비스가 가장 인기있는 수단으로 급부상하였다. 전동킥보드 공유서비스의 이용 건수가 급증함에 따라 법적 제도 미흡으로 인한 다양한 문제가 발생하고 있다. 특히 교통사고의 경우 규제 중심의 교통안전 개선으로 과학적 접근 측면에서 사고 원인을 분석하지 않아 근본적인 문제해결이 불가능한 실정이다. 본 연구는 전동킥보드 이용자가 어디에서, 어떻게 위험한 주행 상황이 발생하는지를 식별하기 위한 방법론을 개발한다. GPS 장치는 각 전동킥보드에 장착되어 있어 우선적으로 본 연구에서는 GPS기반 주행궤적 데이터를 활용한 위험주행 상황 식별 방법론을 개발하였다. 도시환경에서 네 가지 종방향 위험주행 상황이 최종적으로 식별되었다. 위험주행 상황이 발생할 조건은 주거지역, 상업지역, 역세권 등의 토지이용특성별, 이용자의 인적요인별로 상이할 것으로 가정하고 통계분석을 실시하였다. 본 연구가 전동킥보드 서비스의 교통안전을 향상시키기 위한 기초연구로 활용될 것으로 기대한다.

키워드

전동킥보드

주행궤적 데이터

위험주행 상황

퍼스널 모빌리티(pm)

교통안전

References

Arvin R., Khattak A. J. (2020), Driving Impairments and Duration of Distractions: Assessing Crash Risk by Harnessing Microscopic Naturalistic Driving Data, Accid Anal Prev, 146, 105733. 10.1016/j.aap.2020.10573332916552

Carlsson A., Lundälv J. (2019), Acute Injuries Resulting from Accidents Involving Powered Mobility Devices (PMDs): Development and Outcomes of PMD-related Accidents in Sweden, Traffic Injury Prevention, 20(5), 484-491. 10.1080/15389588.2019.160691031180723

Chen C., Zhao X., Zhang Y., Rong J., Liu X. (2019), A Graphical Modeling Method for Individual Driving Behavior and Its Application in Driving Safety Analysis Using GPS Data, Transportation Research Part F: Traffic Psychology and Behaviour, 63, 118-34. 10.1016/j.trf.2019.03.017

Cheng A. S. K., Liu K. P. Y., Tulliani N. (2015), Relationship between Driving-Violation Behaviours and Risk Perception in Motorcycle Accidents, Hong Kong Journal of Occupational Therapy, 25, 32-38. 10.1016/j.hkjot.2015.06.001

Cicchino J. B., Kulie P. E., McCarthy M. L. (2021), Severity of E-scooter Rider Injuries Associated with Trip Characteristics, J Safety Res, 76, 256-61. 10.1016/j.jsr.2020.12.01633653557

Eriksson J., Niska A., Forsman A. (2022), Injured Cyclists with Focus on Single-bicycle Crashes and Differences in Injury Severity in Sweden, Accid Anal Prev, 165, 106510. 10.1016/j.aap.2021.10651034896906

Frigge M., Hoaglin D., Iglewicz B. (1989), Some Implementations of the Boxplot, The American Statistician, 43(1), 50-54. 10.1080/00031305.1989.10475612

Han D., Kim E., Ji M. (2020), Analysis of Severity Factors in Personal Mobility (PM) Traffic Accidents. J. Korean Soc. Transp.. 38(3), Korean Society of Transportation, 232-247. 10.7470/jkst.2020.38.3.232

Han S., Lee C., Yun I., Yoon Y., Na J. (2021), Analysis of PM (Personal Mobility) Traffic Accident Caracteristics and Cause of Death, The Journal of The Korea Institute of Intelligent Transport Systems, 20(1), 100-118. 10.12815/kits.2021.20.1.100

Hong C., Kim H., Yun S., Jung M. (2003), Using the Sample IQR for Calculating Sample Size, Korean Journal of Applied Statistics, 16(1), The Korean Statistical Society, 181-193. 10.5351/KJAS.2003.16.1.181

Kim H., Lee H., Lee C., So J. (2022), Analysis of Neighbourhood Environmental Factors Affecting Personal Mobility Accidents, J. Korean Soc. Transp., 40(2), Korean Society of Transportation, 205-217. 10.7470/jkst.2022.40.2.205

Kim S., Song T.-J., Rouphail N. M., Aghdashi S., Amaro A., Gonçalves G. (2016), Exploring the Association of Rear-end Crash Propensity and Micro-scale Driver Behavior, Safety Science, 89, 45-54. 10.1016/j.ssci.2016.05.016

Oh C., Jeong E., Kang K., Kang Y. (2013), Hazardous Driving Event Detection and Analysis System in Vehicular Networks, Transportation Research Record: Journal of the Transportation Research Board, 2381, 9-19. 10.3141/2381-02

O’Hern S., Estgfaeller N., Stephens A. N., Useche S. A. (2021), Bicycle Rider Behavior and Crash Involvement in Australia, Int J Environ Res Public Health, 18. 10.3390/ijerph1805237833804479PMC7967758

Posirisuk P., Baker C., Ghajari M. (2022), Computational Prediction of Head-ground Impact Kinematics in E-scooter Falls, Accid Anal Prev, 167. 106567. 10.1016/j.aap.2022.10656735033967

Shin H. (2021), Enhancing Strategies for Next Generation Personal Mobility, Korea Transport Institute.

TAAS (2021), http://taas.koroad.or.kr

Vanlaar W., Simpson H., Robertson R. (2008), A Perceptual Map for Understanding Concern about Unsafe Driving Behaviours, Accid Anal Prev, 40, 1667-73. 10.1016/j.aap.2008.05.00918760094

Xu W., Wang J., Fu T., Gong H., Sobhani A. (2022), Aggressive Driving Behavior Prediction Considering Driver’s Intention Based on Multivariate-temporal Feature Data, Accid Anal Prev, 164. 106477. 10.1016/j.aap.2021.10647734813934

Zhou D., Chang M., Gu G., Sun X., Xu H., Wang W., Wang T. (2022), Analysis of Risky Driving Behavior of Urban Electric Bicycle Drivers for Improving Safety, Sustainability, 14. 10.3390/su14031243

Zhu Y., Jiang M., Yamamoto T. (2022), Analysis on the Driving Behavior of Old Drivers by Driving Recorder GPS Trajectory Data, Asian Transport Studies, 8. 10.1016/j.eastsj.2022.100063

Information

Publisher :Korean Society of Transportation
Publisher(Ko) :대한교통학회
Journal Title :Journal of Korean Society of Transportation
Journal Title(Ko) :대한교통학회지
Volume : 40
No :5
Pages :735-749
Received Date : 2022-06-29
Revised Date : 2022-07-25
Accepted Date : 2022-10-13
DOI :https://doi.org/10.7470/jkst.2022.40.5.735

Journal of Korean Society of Transportation ISSN:1229-1366(Print) 2234-4217(Online) 대한교통학회지

All Issue