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Investigating the Effect of COVID-19 on Driver Behavior and Road Safety: A Naturalistic Driving Study in Malaysia

Author

Listed:
  • Ward Ahmed Al-Hussein

    (Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Wenshuang Li

    (Faculty of Business and Economics, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Lip Yee Por

    (Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Chin Soon Ku

    (Department of Computer Science, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia)

  • Wajdi Hamza Dawod Alredany

    (Department of Mathematics, Dhofar University, Salalah 211, Oman)

  • Thanakamon Leesri

    (School of Community Health Nursing, Institute of Nursing, Suranaree University of Technology, 111 University Ave., Muang, Nakhon Ratchasima 30000, Thailand)

  • Huda Hussein MohamadJawad

    (College of Information Technology, Universiti Tenaga Nasional, Jalan IKRAM-UNITEN, Kajang 43000, Malaysia)

Abstract

The spread of the novel coronavirus COVID-19 resulted in unprecedented worldwide countermeasures such as lockdowns and suspensions of all retail, recreational, and religious activities for the majority of 2020. Nonetheless, no adequate scientific data have been provided thus far about the impact of COVID-19 on driving behavior and road safety, especially in Malaysia. This study examined the effect of COVID-19 on driving behavior using naturalistic driving data. This was accomplished by comparing the driving behaviors of the same drivers in three periods: before COVID-19 lockdown, during COVID-19 lockdown, and after COVID-19 lockdown. Thirty people were previously recruited in 2019 to drive an instrumental vehicle on a 25 km route while recording their driving data such as speed, acceleration, deceleration, distance to vehicle ahead, and steering. The data acquisition system incorporated various sensors such as an OBDII reader, a lidar, two ultrasonic sensors, an IMU, and a GPS. The same individuals were contacted again in 2020 to drive the same vehicle on the same route in order to capture their driving behavior during the COVID-19 lockdown. Participants were approached once again in 2022 to repeat the procedure in order to capture their driving behavior after the COVID-19 lockdown. Such valuable and trustworthy data enable the assessment of changes in driving behavior throughout the three time periods. Results showed that drivers committed more violations during the COVID-19 lockdown, with young drivers in particular being most affected by the traffic restrictions, driving significantly faster and performing more aggressive steering behaviors during the COVID-19 lockdown than any other time. Furthermore, the locations where the most speeding offenses were committed are highlighted in order to provide lawmakers with guidance on how to improve traffic safety in those areas, in addition to various recommendations on how to manage traffic during future lockdowns.

Suggested Citation

  • Ward Ahmed Al-Hussein & Wenshuang Li & Lip Yee Por & Chin Soon Ku & Wajdi Hamza Dawod Alredany & Thanakamon Leesri & Huda Hussein MohamadJawad, 2022. "Investigating the Effect of COVID-19 on Driver Behavior and Road Safety: A Naturalistic Driving Study in Malaysia," IJERPH, MDPI, vol. 19(18), pages 1-18, September.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:18:p:11224-:d:908743
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    References listed on IDEAS

    as
    1. Ward Ahmed Al-Hussein & Miss Laiha Mat Kiah & Lip Yee Por & Bilal Bahaa Zaidan, 2021. "Investigating the Effect of Social and Cultural Factors on Drivers in Malaysia: A Naturalistic Driving Study," IJERPH, MDPI, vol. 18(22), pages 1-18, November.
    2. Wang, Wei & Miao, Wei & Liu, Yongdong & Deng, Yiting & Cao, Yunfei, 2022. "The impact of COVID-19 on the ride-sharing industry and its recovery: Causal evidence from China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 128-141.
    3. Ward Ahmed Al-Hussein & Lip Yee Por & Miss Laiha Mat Kiah & Bilal Bahaa Zaidan, 2022. "Driver Behavior Profiling and Recognition Using Deep-Learning Methods: In Accordance with Traffic Regulations and Experts Guidelines," IJERPH, MDPI, vol. 19(3), pages 1-23, January.
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    Cited by:

    1. Hengyi Zhang & Yusheng Ci & Yikang Huang & Lina Wu, 2024. "The Effect of the COVID-19 Pandemic on the Distribution of Traffic Accident Hotspots in New York City," Sustainability, MDPI, vol. 16(8), pages 1-21, April.

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