Abstract
Globally, COVID-19 pandemic has had an impact on human life, every aspect of social and economic sectors including transportation system and operations. This study examined the driving and customer handling behavior of public transportation operators before and during the outbreak of COVID-19 pandemic in Ethiopia. Mixed research, pre-/post-study design, protection motivation theory, and binary logistic regression models were used to guide the development, quantification, and analysis of the causal relationships of pandemic-related constructs on driving and customer handling behaviors. Driving behaviors were examined in terms of harsh speeding and braking concerning the time period before and during the COVID-19 pandemic outbreak in both Hawassa and Addis Ababa city. The level of friendly handling and care of public transportation operators to customers could operationalize and measure customer handling. Data were collected through surveys and interviews with various modes of public transportation operators. Accordingly, it is found that factors related to new COVID-19 pandemic and response measure mainly infection risk fear; transport restrictions were the most significant factors impacting driving behavior during the pandemic. During the pandemic, driving frequencies and intentions, as well as driving decisions, were significantly influenced and reduced, compared to pre-pandemic scenario. Harsh driving behaviors such as harsh speeding, harsh braking, and wrong-side driving became more frequent but customer handling behaviors were also predominantly unfriendly. The performance of protection motivation theory was relevant to inform, guide the study, and understand the actual impacts. Thus, policymakers must learn from the harsh lessons of COVID-19 pandemic and make bold investments in preparedness, prevention, and response, including adaptive and pandemic-sensitive strategies and customer-oriented strategies.
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Data availability
The dataset is available upon request to the author.
References
Ahmed MM, Khan MN, Das A, Dadvar SE (2022) Global lessons learned from naturalistic driving studies to advance traffic safety and operation research: a systematic review. Accid Anal Prev. https://doi.org/10.1016/j.aap.2022.106568
Aletta F, Brinchi S, Carrese S, Gemma A, Guattari C, Mannini L, Patella SM (2020) Analyzing urban trafc volumes and mapping noise emissions in Rome (Italy) in the context of containment measures for the COVID-19 disease. Noise Mapp 7(1):114–122
Aloi A, Alonso B, Benavente J, Cordera R, Echániz E, González F, Ladisa C, Lezama-Romanelli R, López-Parra Á, Mazzei V, Perrucci L, Prieto-Quintana D, Rodríguez AG, Sañudo R (2020) Effects of the COVID-19 lockdown on urban mobility: empirical evidence from the city of Santander (Spain). Sustainability 12(9):3870. https://doi.org/10.3390/su12093870
Awad-Núñez S, Julio R, Gómez J, Moya-Gómez B, González JL (2021) Post-COVID-19 travel behaviour patterns: impact on the willingness to pay of users of public transport and shared mobility services in Spain. Eur Transp Res Rev. https://doi.org/10.1186/s12544-021-00476-4
Bärgman J (2016) Methods for analysis of naturalistic driving data in driver behavior research. https://www.semanticscholar.org/paper/Methods-for-Analysis-of-Naturalistic-Driving-Data-B%C3%A4rgman/e7edd55fc2e26fc8d7a6ba2a9a1ea6f847aa6353
Betteley C (2020) Mask wearing “risks isolating” deaf people. https://www.bbc.com/news/uk-wales-52659083
Bifulco GN, Galante F, Pariota L, Spena MT, Del Gais P (2014) Data collection for traffic and drivers’ behaviour studies: a large-scale survey. Procedia Soc Behav Sci 111:721–730. https://doi.org/10.1016/j.sbspro.2014.01.106
Boer H, Seydel ER (2005) Protection motivation theory. In: Conner M, Norman P (eds) Predicting health behaviour, 2nd edn. Open University Press, Maidenhead, pp 81–126
Bucsky P (2020) Modal share changes due to COVID-19: the case of Budapest. Transp Res Interdiscip Perspect. https://doi.org/10.1016/j.trip.2020.100141
Carter D (2020) Effects of COVID-19 shutdown on crashes and travel in NC
Chen A, Lu Y (2021) Protective behavior in ride-sharing through the lens of protection motivation theory and usage situation theory. Int J Inf Manag. https://doi.org/10.1016/j.ijinfomgt.2021.102402
Cox DN, Koster A, Russell CG (2004) Predicting intentions to consume functional foods and supplements to offset memory loss using an adaptation of protection motivation theory. Appetite 43:55–64. https://doi.org/10.1016/j.appet.2004.02.003
Davis JD, Babulal GM, Papandonatos GD, Burke EM, Rosnick CB, Ott BR, Roe CM (2020) Evaluation of naturalistic driving behavior using in-vehicle monitoring technology in preclinical and early Alzheimer’s disease. Front Psychol. https://doi.org/10.3389/fpsyg.2020.596257
Dong X, Xie K, Yang H (2022) How did COVID-19 impact driving behaviors and crash Severity? A multi-group structural equation modeling. Accid Anal Prev. https://doi.org/10.1016/j.aap.2022.106687
de Haas M, Faber R, Hamersma M (2020) How COVID-19 and the Dutch ‘intelligent lockdown’ change activities, work and travel behaviour: evidence from longitudinal data in the Netherlands. Transp Res Interdiscip Perspect. https://doi.org/10.1016/j.trip.2020.100150
Dutta B, Vasudevan V (2020) Insight into driver behavior during overtaking maneuvers in disorderly traffic: an instrumented vehicle study. Transp Res Procedia 48:719–733. https://doi.org/10.1016/j.trpro.2020.08.074
Eby DW (2004) Driving, risky. In: Elsevier eBooks, p 627–632. https://doi.org/10.1016/b0-12-657410-3/00697-8
Eby DW, Charlton JL, Eby DW, Bogard SE, Langford JW, Koppel SN, Kolenic GE, Marshall S, Man-Son-Hing M (2013) Self-regulation of driving by older adults: comparison of self-report and objective driving data. Transp Res Part F-Traffic Psychol Behav 20:29–38. https://doi.org/10.1016/j.trf.2013.05.001
Gao J, Davis GA (2017) Using naturalistic driving study data to investigate the impact of driver distraction on driver’s brake reaction time in freeway rear-end events in car-following situation. J Saf Res 63:195–204. https://doi.org/10.1016/j.jsr.2017.10.012
Gao J, An Z, Bai X (2019) A new representation method for probability distributions of multimodal and irregular data based on uniform mixture model. Ann Oper Res. https://doi.org/10.1007/s10479-019-03236-9
Hale T, Angrist N, Goldszmidt R, Kira B, Petherick A, Phillips T, Webster S, Cameron-Blake E, Hallas L, Majumdar S, Tatlow H (2021) A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nat Hum Behav 5:529–538. https://doi.org/10.1038/s41562-021-01079-8
Harbeck E, Glendon I, Hine T (2018) Young driver perceived risk and risky driving: A theoretical approach to the “fatal five.” Transport Res F: Traffic Psychol Behav 58:392–404. https://doi.org/10.1016/j.trf.2018.06.018
Heath R, Mansuri Gh, Rijkers B, Seitz WH, Sharma D (2020) Measuring employment: experimental evidence from Urban Ghana. World Bank Policy Research Working Paper No. 9263. https://ssrn.com/abstract=3616472
Hotle S, Murray-Tuite P, Singh K (2020) Influenza risk perception and travel-related health protection behavior in the US: insights for the aftermath of the COVID-19 outbreak. Transp Res Interdiscip Perspect 5:100127. https://doi.org/10.1016/j.trip.2020.100127
Howard GS (1980) Response-shift bias: a problem in evaluating interventions with pre/post-self-reports. Eval Rev 4(1):93–106. https://doi.org/10.1177/0193841X8000400105
Katrakazas C, Michelaraki E, Sekadakis M, Yannis G (2020) A descriptive analysis of the effect of the COVID-19 pandemic on driving behavior and road safety. Transp Res Interdiscip Perspect. https://doi.org/10.1016/j.trip.2020.100186
Katrakazas C, Michelaraki E, Sekadakis M, Ziakopoulos A, Kontaxi A, Yannis G (2021) Identifying the impact of the COVID-19 pandemic on driving behavior using naturalistic driving data and time series forecasting. J Saf Res 78:189–202. https://doi.org/10.1016/j.jsr.2021.04.007
Kim K (2021) Impacts of COVID-19 on transportation: SUMMARY and synthesis of interdisciplinary research. Transp Res Interdiscip Perspect. https://doi.org/10.1016/j.trip.2021.100305
Klatt J, Taylor-Powell E (2005) Using the retrospective post-then-pre design. Quick tips, #27. University of Wisconsin-Extension, Madison
Klauer SG, Neale VL, Dingus TA, Ramsey D, Sudweeks J (2005) Driver inattention: a contributing factor to crashes and near-crashes. Proc Hum Factors Ergon Soc Annu Meet 49(22):1922–1926. https://doi.org/10.1177/154193120504902208
Lee J, Porr A, Miller H (2020) Evidence of increased vehicle speeding in Ohio’s major cities during the COVID-19 pandemic. Findings. https://doi.org/10.32866/001c.12988
Lei Z, Ukkusuri SV (2022) Understanding the recovery of On-Demand Mobility Services in the COVID-19 era. J Big Data Anal Transp 4(1):1–21. https://doi.org/10.1007/s42421-022-00051-w
Liang L, Wu G (2022) Effects of COVID-19 on customer service experience: Can employees wearing facemasks enhance customer-perceived service quality? J Hosp Tour Manag 50:10–20. https://doi.org/10.1016/j.jhtm.2021.12.004
Linares-Rendón F, Garrido-Cumbrera M (2021) Impact of the COVID-19 pandemic on urban mobility: a systematic review of the literature. J Transp Health 22:101225
Lisheng J, Baicang G, Yuying J, Qiang H (2021) Analysis on the influencing factors of driving behaviours based on theory of planned behaviour. Adv Civ Eng 2021:6687674. https://doi.org/10.1155/2021/6687674
Mao H, Guo F, Deng X, Doerzaph ZR (2021) Decision-adjusted driver risk predictive models using kinematics information. Accid Anal Prev 156:106088. https://doi.org/10.1016/j.aap.2021.106088
Masello L, Castignani G, Sheehan B, Guillen M, Murphy F (2023) Using contextual data to predict risky driving events: a novel methodology from explainable artificial intelligence. Accid Anal Prev 184:106997. https://doi.org/10.1016/j.aap.2023.106997
Mashrur SM, Wang K, Loa P, Hossain S, Habib KN (2022) Application of protection motivation theory to quantify the impact of pandemic fear on anticipated postpandemic public transportation usage. Transp Res Rec. https://doi.org/10.1177/03611981211065439
Michelaraki E, Sekadakis M, Katrakazas C, Ziakopoulos A, Yannis G (2021) A four-country comparative overview of the impact of COVID-19 on traffic safety behavior. In: 10th international Congress on Transportation Research, Rhodes, Greece
Ozbilen B, Slagle KM, Akar G (2021) Perceived risk of infection while traveling during the COVID-19 pandemic: insights from Columbus, OH. Transp Res Interdiscip Perspect 10:100326. https://doi.org/10.1016/j.trip.2021.100326
Parady G, Taniguchi A, Takami K (2020) Travel behavior changes during the COVID-19 pandemic in Japan: analyzing the effects of risk perception and social influence on going-out self-restriction. Transp Res Interdiscip Perspect 7:100181. https://doi.org/10.1016/j.trip.2020.100181
Przybyłowski A, Stelmak S, Suchanek M (2021) Mobility behaviour in view of the impact of the COVID-19 pandemic—public transport users in Gdansk case study. Sustainability 13(1):364. https://doi.org/10.3390/su13010364
Rivera D (2004) The use of a proposed modified model of planned behavior to predict the beef consumption of young adult college students. https://ttu-ir.tdl.org/items/d69e1b31-406e-464a-8e58-167772a3bcc1
Rogers RW (1975) A protection motivation theory of fear appeals and attitude change. J Psychol 91(1):93–114. https://doi.org/10.1080/00223980.1975.9915803
Rogers RW (1983) Cognitive and physiological processes in fear appeals and attitude change: a revised theory of protection motivation. In: Cacioppo JT, Petty RE (eds) Social psychophysiology: a sourcebook. Guilford Press, New York, pp 153–176
Saladié Ò, Bustamante E, Gutiérrez A (2020) COVID-19 lockdown and reduction of traffic accidents in Tarragona province, Spain. Transp Res Interdiscip Perspect. https://doi.org/10.1016/j.trip.2020.100218
Seacrist T, Sahani R, Chingas G, Douglas EC, Graci V, Loeb H (2020) Efficacy of automatic emergency braking among risky drivers using counterfactual simulations from the SHRP 2 naturalistic driving study. Saf Sci 128:104746. https://doi.org/10.1016/j.ssci.2020.104746
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. Accid Anal Prev. https://doi.org/10.1016/j.aap.2021.106391
Sekadakis M, Katrakazas C, Michelaraki E, Ζιακόπουλος Α, Yannis G (2023) COVID-19 and driving behavior: Which were the most crucial influencing factors? Data Sci Transp. https://doi.org/10.1007/s42421-023-00078-7
Shannon K, Crago AL, Baral S, Bekker L, Kerrigan D, Decker MR, Poteat T, Wirtz AL, Weir BW, Boily M, Butler J, Strathdee SA, Beyrer C (2018) The global response and unmet actions for HIV and sex workers. Lancet 392(10148):698–710. https://doi.org/10.1016/s0140-6736(18)31439-9
Sharif A, Reza Khavarian-Garmsir A (2020) The COVID-19 pandemic: impacts on cities and major lessons for urban planning, design, and management. Sci Total Environ 749:1–3
Shilling F, Waetjen D (2020) Impact of COVID19 mitigation on numbers and costs of California Traffic Crashes
Simons-Morton BG, Klauer SG, Ouimet MC, Guo F, Albert PS, Lee SE, Ehsani JP, Pradhan AK, Dingus TA (2015) Naturalistic teenage driving study: findings and lessons learned. J Saf Res 54:41.e29-41.e44. https://doi.org/10.1016/j.jsr.2015.06.010
Singh H, Kathuria A (2021) Analyzing driver behavior under naturalistic driving conditions: A review. Accid Anal Prev 150:105908. https://doi.org/10.1016/j.aap.2020.105908
Sudman S, Bradburn N, Schwarz N (1996) Thinking about answers: The application of cognitive processes to survey methodology. Jossey-Bass, San Francisco, CA
Tareke KM (2023) How the driving behaviors and customer handling of public transportation operators have been impacted by the COVID-19 pandemic in Addis Ababa, Ethiopia: the perspective of protection motivation theory? Front Sustain Cities. https://doi.org/10.3389/frsc.2023.1140838
Tenenhaus M, Vinzi VE, Chatelin Y-M, Lauro C (2005) Pls path modeling. Comput Stat Data Anal 48(1):159–205. https://doi.org/10.1016/j.csda.2004.03.005
Train K (2003) Discrete choice methods with simulation. Cambridge University Press, Cambridge. https://doi.org/10.1017/cbo9780511753930
United Nation Development Program, UNDP (2020) COVID-19 pandemic. UNDP, s.l.
van Schagen I, Sagberg F (2012) The potential benefits of naturalistic driving for road safety research: theoretical and empirical considerations and challenges for the future. Procedia Soc Behav Sci 48:692–701. https://doi.org/10.1016/j.sbspro.2012.06.1047
Vanlaar WGM, Woods-Fry H, Barrett H, Lyon C, Brown S, Wicklund C, Robertson RD (2021) The impact of COVID-19 on road safety in Canada and the United States. Accid Anal Prev. https://doi.org/10.1016/j.aap.2021.106324
Wagner E (2020) Examination of the traffic safety environment during the second quarter of 2020: Special report. rosap.ntl.bts.gov. https://doi.org/10.21949/1525982
Wang K, Liu Y, Mashrur SM, Loa P, Habib KN (2021) COVID-19 influenced households’ interrupted travel schedules (COVHITS) survey: lessons from the Fall 2020 cycle. Transp Policy 112:43–62. https://doi.org/10.1016/j.tranpol.2021.08.009
World Bank (2015) Enhancing Urban Resilience: Addis Ababa, Ethiopia. Available online at: https://documents1.worldbank.org/curated/en/559781468196153638/pdf/Addis-Ababa-Enhancing-Urban-Resilience-city-strength-resilient-cities-program.pdf (accessed April 16, 2022)
World Health Organization, WHO (2020) Coronavirus disease (COVID-19) pandemic. Infodemic Management, s.l.
Wu G, Liang L, Gursoy D (2021) Effects of the new COVID-19 normal on customer satisfaction: Can facemasks level off the playing field between average-looking and attractive-looking employees? Int J Hosp Manag. https://doi.org/10.1016/j.ijhm.2021.102996
Yadegaridehkordi E, Nizam MH, Nasir N, Noor FBM, Shuib L, Badie N (2018) Predicting the adoption of cloud-based technology using fuzzy analytic hierarchy process and structural equation modelling approaches. Appl Soft Comput 66:77–89. https://doi.org/10.1016/j.asoc.2017.12.051
Zheng D, Luo Q, Ritchie BW (2021) Afraid to travel after COVID-19? self-protection, coping and resilience against pandemic ‘travel fear.’ Tour Manage 83:104261. https://doi.org/10.1016/j.tourman.2020.104261
Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, Zhao X, Huang B, Shi W, Lu R, Niu P, Zhan F, Ma X, Wang D, Xu W, Wu G, Gao GF, Tan W (2020) A novel Coronavirus from patients with pneumonia in China, 2019. N Engl J Med 382(8):727–733
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The authors would like to acknowledge Professor Samson Kassahun regarding the guides about where and how to publish articles. Besides, the support of Ethiopian Civil Service University through a partial finance as staff development program for data collection is gratefully acknowledged.
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Tareke, K.M. Driving Behavior and Customer Handling of Urban Public Transportation Drivers and Operators Before and After the COVID-19 Outbreak in Ethiopia, 2022. Transp. in Dev. Econ. 10, 3 (2024). https://doi.org/10.1007/s40890-023-00190-x
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DOI: https://doi.org/10.1007/s40890-023-00190-x