Temporal Changes of Traffic Accidents Based on COVID-19 Spread and Quarantine Policy Changes

Donghyeok Park; Seunghwan Kim; Juneyoung Park

doi:10.7470/jkst.2023.41.7.878

All Issue

2023 Vol.41, Issue 7 Preview Page

Article

Temporal Changes of Traffic Accidents Based on COVID-19 Spread and Quarantine Policy Changes COVID-19 확산 및 방역지침 변화에 따른 지역별 교통사고 시계열 분석

31 December 2023. pp. 878-891

PDF XML

Abstract

The World Health Organization declared COVID-19 a pandemic due to its rapid spread and global impact. Vaccines and treatments remain limited. Countries have prioritized non-pharmaceutical measures such as quarantine and containment to reduce virus transmission. COVID-19, which spreads predominantly in areas with high population density and mobility, has led to societal changes that have impacted road safety. Transportation research on the transmission of infectious diseases is actively conducted abroad, however, domestic research in this area remains scarce. Therefore, the aim of this study was to verify the relationship between the domestic COVID-19 pandemic and transportation. Then, time series prediction techniques are employed to forecast traffic crashes. This study conducted a correlation analysis to establish the link between the count of confirmed COVID-19 cases, transportation indicators reflecting population movement, and the frequency of traffic crashes. Then, ARIMA, a statistical theory-based technique, and LSTM, a heuristic approach, were employed as time series traffic crash prediction methods. As a result of the analysis, the mean average percentage error of LSTM was 15.08%, which showed that the prediction performance was superior to the statistical theory-based technique. In addition, through SHAP, an explainable artificial intelligence technique, it was confirmed that public transportation usage and highway truck traffic volume have a positive impact on regional time series crash prediction. Based on the study findings, the results can serve as a numeric benchmark to comprehend the influence of external elements, including extremely contagious diseases, on road safety, anticipate traffic incidents, and put into effect suitable safety measures in upcoming times.

Keywords

ARIMA

big data

COVID-19

crash prediction

LSTM

time-series analysis

COVID-19의 확산으로 인해 세계보건기구는 세계적인 대유행을 의미하는 팬데믹을 선언하였으며 각 국가들은 초기에 백신 및 치료제 등이 충분하지 않은 상황에서 비약학적 방역 대책과 봉쇄 정책을 추진하였다. 인구밀도와 이동량이 높은 지역을 중심으로 확산되는 특징이 있는 COVID-19는 사회에 변화를 일으켰으며 이러한 변화는 교통안전에 영향을 미쳤다. 해외에서는 감염병에 확산에 따른 교통 연구가 활발하게 이루어지는 반면 국내에서는 관련 연구가 부족한 실정이다. 따라서, 본 연구의 목적은 국내 COVID-19의 대유행 기간을 대상으로 시계열 예측 기법을 활용하여 교통사고를 예측하는 것이다. 본 연구는 COVID-19 확진자 수, 인구의 이동을 대표하는 교통 변수, 교통사고 빈도 간 상관성을 규명하기 위해 상관분석을 수행하였으며 통계적 이론에 기반한 ARIMA와 휴리스틱 방법론인 LSTM을 시계열 교통사고 예측 기법으로 활용하였다. 분석 결과, LSTM의 예측 오차율은 15.08%로 비교군에 비해 예측성능이 우수한 것으로 도출되었다. 또한, 설명가능한 인공지능 기법인 SHAP을 통해 대중교통 이용량과 고속도로 화물차 유‧출입량이 지역별 사고 시계열 예측에 긍정적인 영향을 미치는 것을 확인하였다. 연구의 결과물을 기반으로 향후 전파력이 강한 감염병과 같은 외부요인이 교통안전에 미치는 영향을 파악하고 교통사고를 예측하여 적절한 안전 조치를 수행할 수 있는 정량적 기준으로 활용할 수 있다.

키워드

ARIMA

빅 데이터

COVID-19

사고 예측

LSTM

시계열 분석

References

AASHTO (2010), Highway Safety Manual.

Arellana J., Marquez L., Cantillo V. (2020), COVID-19 Outbreak in Colombia: An Analysis of Its Impacts on Transport Systems, Journal of Advanced Transportation, 2020, Hindawi, 8867316. 10.1155/2020/8867316

Bhin M., Son S., Joh C. (2021), An Analysis of Spatial Characteristics of Change in the Number of Passengers at Individual Bus Stops under COVID-19, J. Korean Soc. Transp., 39(4), Korean Society of Transportation, 447-463. 10.7470/jkst.2021.39.4.447

Box G. E., Jenkins G. M., Reinsel G. C., Ljung G. M. (2015), Time series Analysis: Forecasting and Control, John Wiley & Sons.

Fernandes B., Silva F., Alaiz-Moretón H., Novais P., Analide C., Neves J. (2019), Traffic Flow Forecasting on Data-scarce Environments using ARIMA and LSTM Networks, In World Conference on Information Systems and Technologies, Springer International Publishing, 273-282. 10.1007/978-3-030-16181-1_26

Han H., Ryu G. (2022), A Study on the Changes of Population Movement and Traffic Accidents by COVID-19 Confirmed Patients, Transportation Technology and Policy, 19(4), Korean Society of Transportation, 6-10.

Han K., Kim D., Kang W., So J., Lee C. (2021), Analysis of the Current Status and Correlation of Traffic Demand according to the COVID-19 Indicator, J. Korea Inst. Intell. Transp. Syst., 20(6), The Korea Institute of Intelligent Transport Systems, 55-65. 10.12815/kits.2021.20.6.55

Hemphill J. F. (2003), Interpreting the Magnitudes of Correlation Coefficients, American Psychologist, 58(1), APA PsycNet, 78-79. 10.1037/0003-066X.58.1.7812674822

Hochreiter S., Schmidhuber J. (1997), Long Short-term Memory, Neural Computation, 9(8), Institute of Electrical and Electronics Engineers Inc., 1735-1780. 10.1162/neco.1997.9.8.17359377276

Hu S., Xiong C., Yang M., Younes H., Luo W., Zhang L. (2021), A Big-data Driven Approach to Analyzing and Modeling Human Mobility Trend under Non-pharmaceutical Interventions during COVID-19 Pandemic, Transportation Research Part C: Emerging Technologies, 124, Elsevier, 102955. 10.1016/j.trc.2020.10295533456212PMC7796660

Lee G., Choo S., Kim K., Joung J. (2021), Analysis of Factors Affecting Perceived Risk of COVID-19 Infection in Public Transportation, J. Korean Soc. Transp., 39(5), Korean Society of Transportation, 643-661. 10.7470/jkst.2021.39.5.643

Lee J., Liu H., Abdel-Aty M. (2023), Changes in Traffic Crash Patterns: Before and After the Outbreak of COVID-19 in Florida, Accident Analysis & Prevention, 190, Elsevier, 107187. 10.1016/j.aap.2023.10718737364361PMC10284453

Li J., Zhao Z. (2022), Impact of COVID-19 Travel-restriction Policies on Road Traffic Accident Patterns with Emphasis on Cyclists: A Case Study of New York City, Accident Analysis & Prevention, 167, Elsevier, 106586. 10.1016/j.aap.2022.10658635131653PMC8806026

Roche F. (2021), Assessing Subjective Criticality of Take-over Situations: Validation of Two Rating Scales, Accident Analysis & Prevention, 159, Elsevier, 106216. 10.1016/j.aap.2021.10621634144226

Patwary A. L., Khattak A. J. (2023), Crash Harm before and during the COVID-19 Pandemic: Evidence for Spatial Heterogeneity in Tennessee, Accident Analysis & Prevention, 183, Elsevier, 106988. 10.1016/j.aap.2023.10698836724654PMC9874053

Sekadakis M., Katrakazas C., Michelaraki E., Kehagia F., Yannis G. (2021), Analysis of the Impact of COVID-19 on Collisions, Fatalities and Injuries using Time Series Forecasting: The Case of Greece, Accident Analysis & Prevention, 162, Elsevier, 106391. 10.1016/j.aap.2021.10639134525414PMC8426576

Shin S., Choo S. (2022), Exploring the Influencing Factors on Change in Useof Sharing Mobility by the COVID-19 Pandemic: Focused on Residents in New Towns, J. Korean Soc. Transp., 40(2), Korean Society of Transportation, 230-244. 10.7470/jkst.2022.40.2.230

Vilone G., Longo L. (2021), Notions of Explainability and Evaluation Approaches for Explainable Artificial Intelligence, Information Fusion, 76, Elsevier, 89-106. 10.1016/j.inffus.2021.05.009

Won M., Jang D., Kim J., Choi J. (2021), Transport Policies Through Impact Analysis of Social Distancing Measures Responding to COVID-19 Pandemic, Korea Transport Institute General Research.

Xie W., Wang J., Ragland D. R. (2016), Utilizing the Eigenvectors of Freeway Loop Data Spatiotemporal Schematic for Real Time Crash Prediction, Accident Analysis & Prevention, 94, Elsevier, 59-64. 10.1016/j.aap.2016.05.01327258946

Information

Publisher :Korean Society of Transportation
Publisher(Ko) :대한교통학회
Journal Title :Journal of Korean Society of Transportation
Journal Title(Ko) :대한교통학회지
Volume : 41
No :7
Pages :878-891
Received Date : 2023-10-05
Revised Date : 2023-11-03
Accepted Date : 2023-12-13
DOI :https://doi.org/10.7470/jkst.2023.41.7.878

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

All Issue