A Review of COVID-19-Related Literature on Freight Transport: Impacts, Mitigation Strategies, Recovery Measures, and Future Research Directions
Abstract
:1. Introduction
2. Review Methodology
2.1. Bibliometric Analysis
2.2. Scientometric Analysis
2.3. Systematic Review
3. Results and Discussion of the Scientometric Analysis
3.1. Keyword Analysis
3.2. Co-Authorship Analysis
3.3. Source Citation Analysis
4. Results and Discussion of the Systematic Review
4.1. COVID-19-Related Impact
4.1.1. Impacts of COVID-19
- Demand for freight transport services:
- Capacity shortage
- Operating cost and prices of transport services
- Delivery performance
- Emissions
4.1.2. Transport Mode
4.1.3. Research Methods
4.1.4. Research Gaps and Trends for Future Research
- Since most reviewed studies focus on the peak period of the COVID-19 pandemic (January–June 2020), more research is needed to analyze the impacts of COVID-19 on each transport mode, starting from the beginning of the so-called “new norm” (July 2020). This will identify the strengths and weaknesses of each transport mode under the different “new norm” policies adopted to reopen society. It is also of great importance to qualify the analysis with some statistics showing how different “new norm” policies affect the KPIs of the individual transport modes. This will support policymakers and practitioners in learning from past events and in formulating better policies for unexpected future pandemics.
- There is a lack of studies investigating the impacts of COVID-19 on freight transport in developing countries. Therefore, further research may evaluate the impacts of COVID-19 on freight transport in developing countries and compare them with those in developed countries. Additionally, it may be relevant to compare different control measures implemented by these countries and to assess the impacts of these measures on the performances of freight transport.
- Many studies report severe pressure exerted by the pandemic on city logistics [6,8,11,27]. Therefore, future research may examine the resilience of specific city logistics initiatives, e.g., smart lockers, collection points, etc., during the early and “new norm” periods of the pandemic, where new buying habits of consumers emerge and will need more resilient and efficient urban logistics. This will help policymakers and practitioners in defining better initiatives and relaxing resisting regulations for possibly occurring waves of the pandemic.
- Some studies propose prediction models for evaluating the impacts of COVID-19 on freight transport performances during 2020 [36,39,54]. Future research may extend these studies by comparing the predicted results with the actual results. Furthermore, it is relevant to develop a better understanding of the main factors that may lead to any deviations between both results. This is expected to guide researchers in developing more robust prediction models for future unexpected pandemics.
- Most studies evaluate the economic impact of the pandemic on freight transport while very few studies study the social and environmental impacts [5,28,38,56]. Therefore, future research may be directed at measuring the social and environmental impacts of the pandemic on individual freight transport modes in general, and urban freight deliveries in particular. Furthermore, existing studies use only traffic data in estimating the carbon emissions from freight transport during the pandemic. Hence, future research may give a complete picture by calculating the reduction in CO2 emissions concerned with the utilization of different transport facilities and infrastructure.
- Most studies provide pieces of evidence of the impacts of COVID-19 based on data analysis from a macro perspective [5,6,27,28,29,38,45,56]. Therefore, future research may adopt various methods based on empirical data analysis from a micro perspective to develop more managerial insights by answering several questions of interest, for example:
- What are the short- and long-term impacts of the pandemic on different KPIs of logistics companies?
- What are the causal relationships among the various impacts of the COVID-19 pandemic?
- How do the impacts of the COVID-19 pandemic vary among logistics companies handling different freight or serving different industrial sectors?
- How can logistics companies deal with the pandemic and to what extent do support policies from the governments help them to alleviate the impact of COVID-19?
- Further avenues for future research may address developing simulation models to evaluate the long-term effects of different pandemic control policies on freight transport modes. This can have a significant value for policymakers, since the pandemic is still ongoing and will probably have multiple waves caused by new virus variants. In developing models, researchers can greatly benefit from the findings of the literature on the early stage of the pandemic.
4.2. COVID-19-Related Mitigation Strategies
4.2.1. Mitigation Strategies
- Usage of autonomous delivery vehicles (ADVs):
- Deployment of drone delivery
- Relaxing existing regulations
- Utilization of mobile warehouse
- Usage of large ships
- Application of quantity discount
- Capacity augmentation
- Mixed strategies
4.2.2. Transport Mode and Research Methods
4.2.3. Research Gaps and Trends for Future Research Directions
- There is a low user acceptance of ADVs in Germany [63]. Indeed, ADVs are still immature; therefore, the results presented in these studies are largely premature. Accordingly, more research is required to check the user acceptance of ADVs after the maturation phase. In addition, limited factors, such as age, gender, and citizen income, are considered in evaluating the user acceptance of ADVs [63]. To generalize these findings, more factors, such as delivery distance and time, need to be investigated. Besides Germany, user acceptance of ADVs has only been evaluated in Portland, USA [67]. The investigation is limited to a metropolitan area with small data size, restricting the knowledge of public acceptance of ADVs. In this connection, considering multiple US states with larger data set is imperative to test the public acceptance of ADVs. In addition, making the comparison between ADVs and drones is suggested to see the advantages and disadvantages of each transportation means.
- Robots have been shown to help in minimizing the contact between customers and drivers of traditional vehicles [10]. Here, the only drawback may include overlooking some features of the robots, such as energy consumption and operational cost, which are likely to limit a large-scale deployment of robots. Accordingly, these features should be incorporated in future research to arrive at a better assessment of the applicability of robots.
- Utilization of ADVs with robots is also asserted to minimize greenhouse gas emissions [64]. However, some considerations which are known to affect gas emissions, such as traffic and weather conditions, should be incorporated into future studies to accurately determine greenhouse gas emissions.
- The viability of using existing drone infrastructure, based on time and cost measures, has been verified in Australia [69]. However, many factors, such as legal visibility and opinion of policymakers, have not been considered. For a better verification process, these factors should be investigated in future research.
- Although drones have been used to transport medical supplies in Japan during traffic blockages, it has only been during favorable weather conditions, such as low wind speed and no rain [71]. Therefore, it is imperative to investigate the usage of drones in different weather conditions to further evaluate their applicability.
- Similarly, drones can successfully transport medical goods without the need of specialized infrastructure in Spain [70]. However, this application is accomplished only on a small scale. To enable a large-scale deployment, more investigation is needed.
- The deployment of mobile warehouses has been evaluated by Srivatsa Srinivas and Marathe [11]. Indeed, this study lacks an accurate estimation of product demand. For this purpose, we suggest using data analytic techniques to accurately estimate the product demand. In addition, this study overlooks the dynamic routing and parking optimization of the mobile warehouse. Therefore, investigating this overlooked optimization problem during COVID-19 could be another research direction.
- The deployment of large ships is efficient in satisfying food demand [73]. The main pitfalls of the study include ignoring fluctuations in demand, sea weather conditions, and sailing time of ships, that are typically experienced in real practice. In this connection, it is suggested to develop a stochastic model while considering all uncertain factors to accurately capture this reality.
- Applying quantity discounts appears in the study by Shaban, Chan and Chung [74] while considering deterministic demand. Since demand usually fluctuates, it will be more reliable to consider the stochastic demand while investigating quantity discounts. The planning horizon of the study is quite limited, around one month, which limits the generalization of the gained results. To avoid this situation, it is recommended to study a longer planning horizon (i.e., one year).
- Most studies have presented mitigation strategies for “road” mode, overlooking air and sea modes. Accordingly, more research is required to develop different mitigation strategies for air and sea modes.
4.3. Recoverability Measures Related to COVID-19
4.3.1. Recovery Measures
4.3.2. Research Methods and Transport Modes
4.3.3. Research Gaps and Trends for Future Research
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- IATA. Air Cargo Capacity Crunch: Demand Plummets but Capacity Disappears Even Faster. 2020. Available online: https://www.iata.org/en/pressroom/pr/2020-06-02-01/ (accessed on 19 December 2021).
- Maneenop, S.; Kotcharin, S. The impacts of COVID-19 on the global airline industry: An event study approach. J. Air Transp. Manag. 2020, 89, 101920. [Google Scholar] [CrossRef] [PubMed]
- IRU. COVID-19 Impact on the Road Transport Industry. 2020. Available online: https://www.itf-oecd.org/sites/default/files/docs/covid-19_impact_on_the_road_transport_industry_-_june_2021.pdf (accessed on 20 October 2021).
- Millefiori, L.M.; Braca, P.; Zissis, D.; Spiliopoulos, G.; Marano, S.; Willett, P.K.; Carniel, S. COVID-19 impact on global maritime mobility. Sci. Rep. 2021, 11, 18039. [Google Scholar] [CrossRef] [PubMed]
- Bartle, J.R.; Lutte, R.K.; Leuenberger, D.Z. Sustainability and air freight transportation: Lessons from the global pandemic. Sustainability 2021, 13, 3738. [Google Scholar] [CrossRef]
- Munawar, H.S.; Khan, S.I.; Qadir, Z.; Kouzani, A.Z.; Mahmud, M.A.P. Insight into the impact of COVID-19 on Australian transportation sector: An economic and community-based perspective. Sustainability 2021, 13, 1276. [Google Scholar] [CrossRef]
- Michail, N.A.; Melas, K.D. Shipping markets in turmoil: An analysis of the COVID-19 outbreak and its implications. Transp. Res. Interdiscip. Perspect. 2020, 7, 100178. [Google Scholar] [CrossRef]
- Yang, S.; Ning, L.; Jiang, T.; He, Y. Dynamic impacts of COVID-19 pandemic on the regional express logistics: Evidence from China. Transp. Policy 2021, 111, 111–124. [Google Scholar] [CrossRef]
- Tianming, G.; Erokhin, V.; Arskiy, A.; Khudzhatov, M. Has the COVID-19 pandemic affected maritime connectivity? An estimation for China and the polar silk road countries. Sustainability 2021, 13, 3521. [Google Scholar] [CrossRef]
- Chen, C.; Demir, E.; Huang, Y.; Qiu, R. The adoption of self-driving delivery robots in last mile logistics. Transp. Res. Part E Logist. Transp. Rev. 2021, 146, 102214. [Google Scholar] [CrossRef]
- Srinivas, S.S.; Marathe, R.R. Moving towards “mobile warehouse”: Last-mile logistics during COVID-19 and beyond. Transp. Res. Interdiscip. Perspect. 2021, 10, 100339. [Google Scholar] [CrossRef]
- Mamani, H.; Chick, S.E.; Simchi-Levi, D. A Game-Theoretic Model of International Influenza Vaccination Coordination. Manag. Sci. 2013, 59, 1650–1670. [Google Scholar] [CrossRef]
- Anparasan, A.; Lejeune, M. Analyzing the response to epidemics: Concept of evidence-based Haddon matrix. J. Humanit. Logist. Supply Chain Manag. 2017, 7, 266–283. [Google Scholar] [CrossRef]
- Büyüktahtakın, İ.E.; des-Bordes, E.; Kıbış, E.Y. A new epidemics–logistics model: Insights into controlling the Ebola virus disease in West Africa. Eur. J. Oper. Res. 2018, 265, 1046–1063. [Google Scholar] [CrossRef]
- Parvin, H.; Beygi, S.; Helm, J.E.; Larson, P.S.; van Oyen, M.P. Distribution of Medication Considering Information, Transshipment, and Clustering: Malaria in Malawi. Prod. Oper. Manag. 2018, 27, 774–797. [Google Scholar] [CrossRef]
- Dasaklis, T.K.; Pappis, C.P.; Rachaniotis, N.P. Epidemics control and logistics operations: A review. Int. J. Prod. Econ. 2012, 139, 393–410. [Google Scholar] [CrossRef]
- Queiroz, M.M.; Ivanov, D.; Dolgui, A.; Wamba, S.F. Impacts of epidemic outbreaks on supply chains: Mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Ann. Oper. Res. 2020, 291, 504–518. [Google Scholar] [CrossRef]
- Yuen, K.F.; Wang, X.; Ma, F.; Li, K.X. The Psychological Causes of Panic Buying Following a Health Crisis. Int. J. Environ. Res. Public Health 2020, 17, 3513. [Google Scholar] [CrossRef]
- Chowdhury, P.; Paul, S.K.; Kaisar, S.; Moktadir, M.A. COVID-19 pandemic related supply chain studies: A systematic review. Transp. Res. Part E Logist. Transp. Rev. 2021, 148, 102271. [Google Scholar] [CrossRef]
- Borca, B.; Putz, L.M.; Hofbauer, F. Crises and Their Effects on Freight Transport Modes: A Literature Review and Research Framework. Sustainability 2021, 13, 5740. [Google Scholar] [CrossRef]
- Hussein, M.; Eltoukhy, A.E.E.; Karam, A.; Shaban, I.A.; Zayed, T. Modelling in off-site construction supply chain management: A review and future directions for sustainable modular integrated construction. J. Clean. Prod. 2021, 310, 127503. (In English) [Google Scholar] [CrossRef]
- Hofmann, M.; Chisholm, A. Text Mining and Visualization: Case Studies Using Open-Source Tools; CRC Press: Boca Raton, FL, USA, 2016. [Google Scholar]
- Meyer, T. Decarbonizing road freight transportation—A bibliometric and network analysis. Transp. Res. Part D Transp. Environ. 2020, 89, 102619. [Google Scholar] [CrossRef]
- Eltoukhy, A.E.E.; Shaban, I.A.; Chan, F.T.S.; Abdel-Aal, M.A.M. Data Analytics for Predicting COVID-19 Cases in Top Affected Countries: Observations and Recommendations. Int. J. Environ. Res. Public Health 2020, 17, 7080. [Google Scholar] [CrossRef]
- Van Eck, N.J.; Waltman, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2010, 84, 523–538. [Google Scholar] [CrossRef] [PubMed]
- Smith, M.L. Regulating law enforcement’s use of drones: The need for state legislation. Harv. J. Legis. 2015, 52, 423. [Google Scholar] [CrossRef]
- Villa, R.; Monzón, A. Mobility restrictions and e-commerce: Holistic balance in madrid centre during COVID-19 lockdown. Economies 2021, 9, 57. [Google Scholar] [CrossRef]
- Arellana, J.; Márquez, L.; Cantillo, V. COVID-19 Outbreak in Colombia: An Analysis of Its Impacts on Transport Systems. J. Adv. Transp. 2020, 2020, 8867316. [Google Scholar] [CrossRef]
- Bartuska, L.; Masek, J. Changes in road traffic caused by the declaration of a state of emergency in the czech republic-a case study. Transp. Res. Procedia 2021, 53, 321–328. [Google Scholar] [CrossRef]
- Cruz, C.O.; Sarmento, J.M. The impact of COVID-19 on highway traffic and management: The case study of an operator perspective. Sustainability 2021, 13, 5320. [Google Scholar] [CrossRef]
- Cui, Q.; He, L.; Liu, Y.; Zheng, Y.; Wei, W.; Yang, B.; Zhou, M. The impacts of COVID-19 pandemic on China’s transport sectors based on the CGE model coupled with a decomposition analysis approach. Transp. Policy 2021, 103, 103–115. [Google Scholar] [CrossRef]
- Gray, R.S. Agriculture, transportation, and the COVID-19 crisis. Can. J. Agric. Econ. 2020, 68, 239–243. [Google Scholar] [CrossRef]
- Ho, S.J.; Xing, W.; Wu, W.; Lee, C.C. The impact of COVID-19 on freight transport: Evidence from China. MethodsX 2021, 8, 101200. [Google Scholar] [CrossRef]
- Li, T. A SWOT analysis of China’s air cargo sector in the context of COVID-19 pandemic. J. Air Transp. Manag. 2020, 88, 101875. [Google Scholar] [CrossRef] [PubMed]
- Liu, W.; Liang, Y.; Bao, X.; Qin, J.; Lim, M.K. China’s logistics development trends in the post COVID-19 era. Int. J. Logist. Res. Appl. 2020, 25, 1–12. [Google Scholar] [CrossRef]
- Meng, F.; Gong, W.; Liang, J.; Li, X.; Zeng, Y.; Yang, L. Impact of different control policies for COVID-19 outbreak on the air transportation industry: A comparison between China, the U.S. And Singapore. PLoS ONE 2021, 16, e0248361. [Google Scholar] [CrossRef] [PubMed]
- Narasimha, P.T.; Jena, P.R.; Majhi, R. Impact of COVID-19 on the Indian seaport transportation and maritime supply chain. Transp. Policy 2021, 110, 191–203. [Google Scholar] [CrossRef]
- Nižetić, S. Impact of coronavirus (COVID-19) pandemic on air transport mobility, energy, and environment: A case study. Int. J. Energy Res. 2020, 44, 10953–10961. [Google Scholar] [CrossRef]
- Stanivuk, T. Impact of SARS-CoV-2 virus on Maritime Traffic in the Port of Ploce. European Transport. 2021, 82, 13. [Google Scholar] [CrossRef]
- Statista. COVID-19: Traffic Reduction in Selected Countries Worldwide; Statista: Hamburg, Germany, 2020. [Google Scholar]
- Jin, L.; Chen, J.; Chen, Z.; Sun, X.; Yu, B. Impact of COVID-19 on China’s international liner shipping network based on AIS data. Transp. Policy 2022, 121, 90–99. [Google Scholar] [CrossRef]
- Li, Q.; Bai, Q.; Hu, A.; Yu, Z.; Yan, S. How Does COVID-19 Affect Traffic on Highway Network: Evidence from Yunnan Province, China. J. Adv. Transp. 2022, 2022, 7379334. [Google Scholar] [CrossRef]
- Fang, D.; Guo, Y. Flow of goods to the shock of COVID-19 and toll-free highway policy: Evidence from logistics data in China. Res. Transp. Econ. 2022, 93, 101185. [Google Scholar] [CrossRef]
- Aftab, R.; Naveed, M.; Hanif, S. An analysis of COVID-19 implications for SMEs in Pakistan. J. Chin. Econ. Foreign Trade Stud. 2021, 14, 74–88. [Google Scholar] [CrossRef]
- Hobbs, J.E. Food supply chains during the COVID-19 pandemic. Can. J. Agric. Econ. 2020, 68, 171–176. [Google Scholar] [CrossRef]
- Loske, D. The impact of COVID-19 on transport volume and freight capacity dynamics: An empirical analysis in German food retail logistics. Transp. Res. Interdiscip. Perspect. 2020, 6, 100165. [Google Scholar] [CrossRef]
- Shih, W. COVID-19 And Global Supply Chains: Watch Out For Bullwhip Effects. Forbes, 21 February 2020. [Google Scholar]
- Xu, Z.; Elomri, A.; Kerbache, L.; el Omri, A. Impacts of COVID-19 on Global Supply Chains: Facts and Perspectives. IEEE Eng. Manag. Rev. 2020, 48, 153–166. [Google Scholar] [CrossRef]
- Hilmola, O.P.; Lähdeaho, O.; Henttu, V.; Hilletofth, P. COVID-19 pandemic: Early implications for north european manufacturing and logistics. Sustainability 2020, 12, 8315. [Google Scholar] [CrossRef]
- IATA. Industry Losses to Top $84 Billion in 2020; International Air Transport Association: Montreal, QC, Canada, 2020. [Google Scholar]
- IATA. Fuel Price Monitor; International Air Transport Association: Montreal, QC, Canada, 2021. [Google Scholar]
- Grzelakowski, A. The COVID 19 pandemic–challenges for maritime transport and global logistics supply chains. TransNav Int. J. Mar. Navig. Saf. Sea Transp. 2022, 16, 71–77. [Google Scholar] [CrossRef]
- ATRI; OOIDA. COVID-19 Impact on Trucking Industry; American Transportation Research Institute: Arlington, VA, USA, 2020. [Google Scholar]
- Koyuncu, K.; Tavacioğlu, L.; Gökmen, N.; Arican, U.Ç. Forecasting COVID-19 impact on RWI/ISL container throughput index by using SARIMA models. Marit. Policy Manag. 2021, 48, 1096–1108. [Google Scholar] [CrossRef]
- Özden, A.T.; Celik, E. Analyzing the service quality priorities in cargo transportation before and during the COVID-19 outbreak. Transp. Policy 2021, 108, 34–46. [Google Scholar] [CrossRef]
- Han, P.; Cai, Q.; Oda, T.; Zeng, N.; Shan, Y.; Lin, X.; Liu, D. Assessing the recent impact of COVID-19 on carbon emissions from China using domestic economic data. Sci. Total Environ. 2021, 750, 141688. [Google Scholar] [CrossRef]
- Statista. COVID-19 Impact Retail E-Commerce Site Traffic; Statista: Hamburg, Germany, 2020. [Google Scholar]
- OECD. E-Commerce in the Time of COVID-19; Organisation for Economic Co-operation and Development: Paris, France, 2020. [Google Scholar]
- IATA. Keeping Air Cargo Flying; International Air Transport Association: Montreal, QC, Canada, 2020. [Google Scholar]
- Ahart, M. Canada Issues Hours-of-Service Exemption for COVID-19 Relief. Omnitracks, 26 March 2020. [Google Scholar]
- Statista. Lowest Crude Oil Prices due to COVID-19 2020; Statista: Hamburg, Germany, 2020. [Google Scholar]
- Carrington, D. UK Road Travel Falls to 1955 Levels as COVID-19 Lockdown Takes Hold. The Guardian, 3 April 2020. [Google Scholar]
- Kapser, S.; Abdelrahman, M.; Bernecker, T. Autonomous delivery vehicles to fight the spread of COVID-19—How do men and women differ in their acceptance? Transp. Res. Part A Policy Pract. 2021, 148, 183–198. [Google Scholar] [CrossRef]
- Li, L.; He, X.; Keoleian, G.A.; Kim, H.C.; De Kleine, R.; Wallington, T.J.; Kemp, N.J. J. Life Cycle Greenhouse Gas Emissions for Last-Mile Parcel Delivery by Automated Vehicles and Robots. Environ. Sci. Technol. 2021, 55, 11360–11367. [Google Scholar] [CrossRef]
- Liu, T.; Liao, Q.; Gan, L.; Ma, F.; Cheng, J.; Xie, X.; Liu, M. The Role of the Hercules Autonomous Vehicle During the COVID-19 Pandemic: An Autonomous Logistic Vehicle for Contactless Goods Transportation. IEEE Robot. Autom. Mag. 2021, 28, 48–58. [Google Scholar] [CrossRef]
- Ozkan, O.; Atli, O. Transporting COVID-19 testing specimens by routing unmanned aerial vehicles with range and payload constraints: The case of Istanbul. Transp. Lett. 2021, 13, 482–491. [Google Scholar] [CrossRef]
- Pani, A.; Mishra, S.; Golias, M.; Figliozzi, M. Evaluating public acceptance of autonomous delivery robots during COVID-19 pandemic. Transp. Res. Part D Transp. Environ. 2020, 89, 102600. [Google Scholar] [CrossRef]
- Alsamhi, S.H.; Lee, B.; Guizani, M.; Kumar, N.; Qiao, Y.; Liu, X. Blockchain for decentralized multi-drone to combat COVID-19 and future pandemics: Framework and proposed solutions. Trans. Emerg. Telecommun. Technol. 2021, 32, e4255. [Google Scholar] [CrossRef]
- Kunovjanek, M.; Wankmüller, C. Containing the COVID-19 pandemic with drones—Feasibility of a drone enabled back-up transport system. Transp. Policy 2021, 106, 141–152. [Google Scholar] [CrossRef]
- Quintanilla García, I.; Vera Vélez, N.; Alcaraz Martínez, P.; Vidal Ull, J.; Fernández Gallo, B. A Quickly Deployed and UAS-Based Logistics Network for Delivery of Critical Medical Goods during Healthcare System Stress Periods: A Real Use Case in Valencia (Spain). Drones 2021, 5, 13. [Google Scholar] [CrossRef]
- Yakushiji, K.; Fujita, H.; Murata, M.; Hiroi, N.; Hamabe, Y.; Yakushiji, F. Short-Range Transportation Using Unmanned Aerial Vehicles (UAVs) during Disasters in Japan. Drones 2020, 4, 68. [Google Scholar] [CrossRef]
- Bathke, H.; Münch, C.; Heiko, A.; Hartmann, E. Building Resilience Through Foresight: The Case of Maritime Container Shipping Firms. IEEE Trans. Eng. Manag. 2022, 69, 1–23. [Google Scholar] [CrossRef]
- Pasha, J.; Dulebenets, M.A.; Fathollahi-Fard, A.M.; Tian, G.; Lau, Y.Y.; Singh, P.; Liang, B. An integrated optimization method for tactical-level planning in liner shipping with heterogeneous ship fleet and environmental considerations. Adv. Eng. Inform. 2021, 48, 101299. [Google Scholar] [CrossRef]
- Shaban, I.A.; Chan, F.T.S.; Chung, S.H. A novel model to manage air cargo disruptions caused by global catastrophes such as COVID-19. J. Air Transp. Manag. 2021, 95, 102086. [Google Scholar] [CrossRef]
- Aloui, A.; Hamani, N.; Delahoche, L. Designing a Resilient and Sustainable Logistics Network under Epidemic Disruptions and Demand Uncertainty. Sustainability 2021, 13, 14053. [Google Scholar] [CrossRef]
- Schofer, J.L.; Mahmassani, H.S.; Ng, M.T.M. Resilience of U.S. Rail Intermodal Freight during the COVID-19 Pandemic. Res. Transp. Bus. Manag. 2022, 43, 100791. [Google Scholar] [CrossRef]
- Simić, V.; Lazarević, D.; Dobrodolac, M. Picture fuzzy WASPAS method for selecting last-mile delivery mode: A case study of Belgrade. Eur. Transp. Res. Rev. 2021, 13, 43. [Google Scholar] [CrossRef]
- Shavarani, S.M.; Nejad, M.G.; Rismanchian, F.; Izbirak, G. Application of hierarchical facility location problem for optimization of a drone delivery system: A case study of Amazon prime air in the city of San Francisco. Int. J. Adv. Manuf. Technol. 2018, 95, 3141–3153. [Google Scholar] [CrossRef]
- EUR-Lex. Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:52020XC0408(04) (accessed on 10 September 2022).
- Gnap, J.; Senko, Š.; Kostrzewski, M.; Brídziková, M.; Cződörová, R.; Říha, Z. Research on the Relationship between Transport Infrastructure and Performance in Rail and Road Freight Transport—A Case Study of Japan and Selected European Countries. Sustainability 2021, 13, 6654. [Google Scholar] [CrossRef]
- Tardivo, A.; Zanuy, A.C.; Martín, C.S. COVID-19 Impact on Transport: A Paper from the Railways’ Systems Research Perspective. Transp. Res. Rec. 2021, 2675, 367–378. [Google Scholar] [CrossRef]
- Van Tatenhove, J.P.M. COVID-19 and European maritime futures: Different pathways to deal with the pandemic. Marit. Stud. 2021, 20, 63–74. [Google Scholar] [CrossRef]
- Guo, J.; Zhu, X.; Liu, C.; Ge, S. Resilience Modeling Method of Airport Network Affected by Global Public Health Events. Math. Probl. Eng. 2021, 2021, 6622031. [Google Scholar] [CrossRef]
- Oyenuga, A. Perspectives on the impact of the COVID-19 pandemic on the global and African maritime transport sectors, and the potential implications for Africa’s maritime governance. WMU J. Marit. Aff. 2021, 20, 215–245. [Google Scholar] [CrossRef]
- Rivero Gutiérrez, L.; De Vicente Oliva, M.A.; Romero-Ania, A. Managing Sustainable Urban Public Transport Systems: An AHP Multicriteria Decision Model. Sustainability 2021, 13, 4614. [Google Scholar] [CrossRef]
- Akyurek, E.; Bolat, P. Port state control at European Union under pandemic outbreak. Eur. Transp. Res. Rev. 2020, 12, 66. [Google Scholar] [CrossRef]
- Notteboom, T.; Pallis, T.; Rodrigue, J.-P. Disruptions and resilience in global container shipping and ports: The COVID-19 pandemic versus the 2008–2009 financial crisis. Marit. Econ. Logist. 2021, 23, 179–210. [Google Scholar] [CrossRef]
- Gudmundsson, S.V.; Cattaneo, M.; Redondi, R. Forecasting temporal world recovery in air transport markets in the presence of large economic shocks: The case of COVID-19. J. Air Transp. Manag. 2021, 91, 102007. [Google Scholar] [CrossRef]
- Cheong, M.L.; Chong, U.M.; Nguyen, A.N.; Ang, S.Y.; Djojosaputro, G.P.; Adiprasetyo, G.; Gadong, K.L. Singapore Airlines: Profit Recovery and Aircraft Allocation Models during the COVID-19 Pandemic; Singapore Management University: Singapore, 2021. [Google Scholar]
- Fu, X.; Qiang, Y.; Liu, X.; Jiang, Y.; Cui, Z.; Zhang, D.; Wang, J. Will multi-industry supply chains’ resilience under the impact of COVID-19 pandemic be different? A perspective from China’s highway freight transport. Transp. Policy 2022, 118, 165–178. [Google Scholar] [CrossRef]
- Dwivedi, A.; Shardeo, V.; Patil, A. Analysis of recovery measures for sustainable freight transportation. J. Asia Bus. Stud. 2022, 16, 495–514. [Google Scholar] [CrossRef]
- Nguyen, H.K. Application of Mathematical Models to Assess the Impact of the COVID-19 Pandemic on Logistics Businesses and Recovery Solutions for Sustainable Development. Mathematics 2021, 9, 1977. [Google Scholar] [CrossRef]
Analysis | Total Number of (---) | Threshold Criteria Minimum Number of | Number of (---) Meet the Threshold | Number of Connected (---) | ||
---|---|---|---|---|---|---|
Occurrence | Documents | Citation | ||||
Co-occurrence of (keywords) | 604 | 3 | -- | -- | 42 | 42 |
Co-authorship between (countries) | 40 | -- | 2 | 0 | 18 | 14 |
Citation of (sources) | 36 | -- | 1 | 0 | 36 | 13 |
Keyword | Links | Occurrences | |
---|---|---|---|
1 | COVID-19 | 38 | 52 |
2 | Freight transport | 20 | 14 |
3 | Supply chains | 8 | 10 |
4 | China | 25 | 9 |
5 | Epidemic | 19 | 8 |
6 | Viral disease | 26 | 8 |
7 | Sustainability | 10 | 8 |
8 | Air transportation | 15 | 6 |
9 | Logistics | 14 | 5 |
10 | Ships | 9 | 5 |
11 | Transportation policy | 15 | 5 |
12 | Airline industry | 9 | 4 |
13 | Cargo | 12 | 4 |
14 | Human | 15 | 4 |
15 | Transportation system | 9 | 4 |
Source | Documents | Citations | Normalized Citations | Avg. Citations | |
---|---|---|---|---|---|
1 | Canadian Journal of Agricultural Economics | 2 | 367 | 9.1039 | 183.5 |
2 | Transportation Research Interdisciplinary Perspectives | 3 | 90 | 3.7687 | 30 |
3 | International Journal of Advanced Manufacturing Technology | 1 | 79 | 1 | 79 |
4 | Transport Policy | 7 | 49 | 8.7427 | 7 |
5 | Sustainability (Switzerland) | 10 | 39 | 5.4223 | 3.9 |
6 | Science of the Total Environment | 1 | 37 | 6.6017 | 37 |
7 | IEEE Engineering Management Review | 1 | 30 | 0.7442 | 30 |
8 | International Journal of Logistics Research and Applications | 1 | 20 | 0.4961 | 20 |
9 | Ocean and Coastal Management | 1 | 10 | 1.7842 | 10 |
Impacts of COVID-19 | Studies per Impact | References |
---|---|---|
Demand for freight transport services | 24 | [5,6,7,8,9,11,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43] |
Capacity shortage | 10 | [5,6,32,34,35,44,45,46,47,48] |
Operating transport cost | 13 | [5,6,7,8,20,27,31,32,34,35,49,50,51,52] |
Delivery performance | 9 | [8,9,32,47,48,53,54,55] |
CO2 emissions | 4 | [5,28,38,56] |
Transport Mode (s) | Number of Studies per Transport Mode (s) | References |
---|---|---|
Road | 10 | [11,27,29,30,35,41,42,43,46,56] |
Air | 5 | [5,6,34,36,38] |
Seaport | 8 | [7,9,33,37,39,40,52,54] |
Air and road | 1 | [28] |
Road and rail | 1 | [45] |
Road, sea, and rail | 1 | [20] |
All modes (General) | 6 | [31,32,44,48,49,55] |
Main Method Category | Number of Studies per Main Method Category | Specific Method | Number of Studies per Specific Method | References |
---|---|---|---|---|
Empirical methods | 8 | Case studies | 3 | [30,34,44] |
Questionnaires and interviews | 5 | [32,35,44,49,53] | ||
Quantitative methods | 21 | Secondary data analysis | 15 | [5,6,27,28,29,38,40,41,43,45,47,50,51,52,56] |
Regression models | 4 | [7,8,9,33] | ||
Forecasting models | 3 | [36,39,54] | ||
Analytical models | 4 | [11,31,42,55] | ||
Literature review methods | 1 | Systematic review | 1 | [20] |
Mixed methods | 2 | Questionnaires and second data analysis | 1 | [37] |
Review and second data analysis | 1 | [48] |
COVID-19-Related Mitigation Strategies | Impact to Be Mitigated | Studies per Strategy | References |
---|---|---|---|
Usage of autonomous delivery vehicles | Driver capacity shortage, emissions | 6 | [10,63,64,65,66,67] |
Deployment of drone delivery | Driver capacity shortage, delivery performance, emissions | 5 | [68,69,70,71,72] |
Relaxing existing regulations | Capacity shortage and operating cost | 3 | [8,59,60] |
Utilization of mobile warehouses | Delivery performance | 1 | [11] |
Engagement of large ships | Container capacity shortage | 1 | [73] |
Application of quantity discounts | Demand for freight transport service | 1 | [74] |
Capacity augmentation | Driver capacity shortage, delivery performance | 2 | [75,76] |
Mixed strategies | Driver capacity shortage, delivery performance, emissions | 1 | [77] |
Transport Mode (s) | Number of Studies per Transport Mode (s) | References |
---|---|---|
Road | 14 | [8,10,11,60,63,64,65,66,67,68,69,70,71,75,76,77] |
Air | 2 | [59,74] |
Seaport and waterways | 2 | [72,73] |
Main Method Category | Number of Studies per Main Method Category | Specific Method | Number of Studies per Specific Method | References |
---|---|---|---|---|
Empirical methods | 10 | Case studies | 4 | [59,64,70,71] |
Questionnaires and interviews | 6 | [8,60,63,67,72,76] | ||
Quantitative methods | 10 | Optimization | 5 | [10,65,66,68,73] |
Simulation optimization | 2 | [69,75] | ||
Analytical model | 1 | [11] | ||
Fuzzy method | 1 | [77] | ||
Game theory | 1 | [74] |
Recovery Measure | Study per Measure | References |
---|---|---|
Quality | 2 | [80,81] |
Efficiency | 3 | [6,82,90] |
Performance | 1 | [83] |
Sustainability | 5 | [5,84,85,86,91] |
Capacity | 2 | [87,90,92] |
Recovery rate | 3 | [88,89,90] |
Main Method Category | Number of Studies per Main Method Category | Specific Method | Transportation Mode | References | ||
---|---|---|---|---|---|---|
Road and Railways | Seaports and Waterways | Air | ||||
Empirical methods | 3 | Framework study | ✓ | [5] | ||
Scenario-based research | ✓ | [82] | ||||
Index-based evaluation | ✓ | [90] | ||||
Quantitative methods | 5 | Forecasting | ✓ | ✓ | ✓ | [88,89,92] |
Comparative analysis | ✓ | ✓ | ✓ | [86,91] | ||
Mixed methods | 8 | AHP multi-criteria decision making | ✓ | ✓ | [83,85] | |
Correlation analysis | ✓ | [80] | ||||
Data acquisition | ✓ | ✓ | ✓ | [5,6,81,84,87] |
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Karam, A.; Eltoukhy, A.E.E.; Shaban, I.A.; Attia, E.-A. A Review of COVID-19-Related Literature on Freight Transport: Impacts, Mitigation Strategies, Recovery Measures, and Future Research Directions. Int. J. Environ. Res. Public Health 2022, 19, 12287. https://doi.org/10.3390/ijerph191912287
Karam A, Eltoukhy AEE, Shaban IA, Attia E-A. A Review of COVID-19-Related Literature on Freight Transport: Impacts, Mitigation Strategies, Recovery Measures, and Future Research Directions. International Journal of Environmental Research and Public Health. 2022; 19(19):12287. https://doi.org/10.3390/ijerph191912287
Chicago/Turabian StyleKaram, Ahmed, Abdelrahman E. E. Eltoukhy, Ibrahim Abdelfadeel Shaban, and El-Awady Attia. 2022. "A Review of COVID-19-Related Literature on Freight Transport: Impacts, Mitigation Strategies, Recovery Measures, and Future Research Directions" International Journal of Environmental Research and Public Health 19, no. 19: 12287. https://doi.org/10.3390/ijerph191912287