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Xiang W, Wang Z, Pan X, Liu X, Yan X, Chen L. The balance between traffic control and economic development in tourist cities under the context of COVID-19: A case study of Xi'an, China. PLoS One 2024; 19:e0295950. [PMID: 38289928 PMCID: PMC10826945 DOI: 10.1371/journal.pone.0295950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 12/03/2023] [Indexed: 02/01/2024] Open
Abstract
Selecting an appropriate intensity of epidemic prevention and control measures is of vital significance to promoting the two-way dynamic coordination of epidemic prevention and control and economic development. In order to balance epidemic control and economic development and suggest scientific and reasonable traffic control measures, this paper proposes a SEIQR model considering population migration and the propagation characteristics of the exposed and the asymptomatic, based on the data of COVID-19 cases, Baidu Migration, and the tourist economy. Further, the factor traffic control intensity is included in the model. After determining the functional relationship between the control intensity and the number of tourists and the cumulative number of confirmed cases, the NSGA-II algorithm is employed to perform multi-objective optimization with consideration of the requirements for epidemic prevention and control and for economic development to get an appropriate traffic control intensity and suggest scientific traffic control measures. With Xi'an City as an example. The results show that the Pearson correlation coefficient between the predicted data of this improved model and the actual data is 0.996, the R-square in the regression analysis is 0.993, with a significance level of below 0.001, suggesting that the predicted data of the model are more accurate. With the continuous rise of traffic control intensity in different simulation scenarios, the cumulative number of cases decreases by a significant amplitude. While balancing the requirements for epidemic prevention and control and for tourist economy development, the model works out the control intensity to be 0.68, under which some traffic control measures are suggested. The model presented in this paper can be used to analyze the impacts of different traffic control intensities on epidemic transmission. The research results in this paper reveal the traffic control measures balancing the requirements for epidemic prevention and control and for economic development.
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Affiliation(s)
- Wang Xiang
- Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science and Technology, Changsha, Hunan, China
| | - Zezhi Wang
- Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science and Technology, Changsha, Hunan, China
| | - Xin Pan
- State Grid Hunan Electric Power Company Limited Economic & Technical Research Institute, Changsha, Hunan, China
- Hunan Key Laboratory of Energy Internet Supply-demand and Operation, Changsha, Hunan, China
| | - Xiaobing Liu
- School of System Science, Beijing Jiaotong University, Beijing, China
| | - Xuedong Yan
- Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing, China
| | - Li Chen
- School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan, China
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Xu Y, Wandelt S, Sun X. A distributionally robust optimization approach for airline integrated recovery under in-flight pandemic transmission risks. TRANSPORTATION RESEARCH. PART C, EMERGING TECHNOLOGIES 2023; 152:104188. [PMID: 37305559 PMCID: PMC10246463 DOI: 10.1016/j.trc.2023.104188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 04/27/2023] [Accepted: 05/24/2023] [Indexed: 06/13/2023]
Abstract
The COVID-19 pandemic has hit the airline industry hard, leading to heterogeneous epidemiological situations across markets, irregular flight bans, and increasing operational hurdles. Such a melange of irregularities has presented significant challenges to the airline industry, which typically relies on long-term planning. Given the growing risk of disruptions during epidemic and pandemic outbreaks, the role of airline recovery is becoming increasingly crucial for the aviation industry. This study proposes a novel model for airline integrated recovery problem under the risk of in-flight epidemic transmission risks. This model recovers the schedules of aircraft, crew, and passengers to eliminate possible epidemic dissemination while reducing airline operating costs. To account for the high uncertainty with respect to in-flight transmission rates and to prevent overfitting of the empirical distribution, a Wasserstein distance-based ambiguity set is utilized to formulate a distributionally robust optimization model. Aimed at tackling computation difficulties, a branch-and-cut solution method and a large neighborhood search heuristic are proposed in this study based on an epidemic propagation network. The computation results for real-world flight schedules and a probabilistic infection model suggest that the proposed model is capable of reducing the expected number of infected crew members and passengers by 45% with less than 4% increase in flight cancellation/delay rates. Furthermore, practical insights into the selection of critical parameters as well as their relationship with other common disruptions are provided. The integrated model is expected to enhance airline disruption management against major public health events while minimizing economic loss.
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Affiliation(s)
- Yifan Xu
- School of Electronic and Information Engineering, Beihang University, 100191, Beijing, China
- National Engineering Laboratory of Multi-Modal Transportation Big Data, 100191, Beijing, China
| | - Sebastian Wandelt
- School of Electronic and Information Engineering, Beihang University, 100191, Beijing, China
- National Engineering Laboratory of Multi-Modal Transportation Big Data, 100191, Beijing, China
| | - Xiaoqian Sun
- School of Electronic and Information Engineering, Beihang University, 100191, Beijing, China
- National Engineering Laboratory of Multi-Modal Transportation Big Data, 100191, Beijing, China
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Sun X, Wandelt S, Zhang A. COVID-19 pandemic and air transportation: Summary of Recent Research, Policy Consideration and Future Research Directions. TRANSPORTATION RESEARCH INTERDISCIPLINARY PERSPECTIVES 2022; 16:100718. [PMID: 36407295 PMCID: PMC9640395 DOI: 10.1016/j.trip.2022.100718] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/02/2022] [Accepted: 11/06/2022] [Indexed: 05/19/2023]
Abstract
The COVID-19 pandemic can be considered an unparalleled disruption to the aviation industry in the last century. Starting with an at-that-time inconceivable reduction in the number of flights from March 2020 to May 2020, the aviation industry has been trying to navigate through and out of the crisis. This process is accompanied with a significant number of scientific studies, reporting on the direct and indirect impact of the COVID-19 pandemic on aviation and vice versa. This paper reviews the impacts in context of the recent literature. We have collected nearly 200 well-published papers on the subject in the years 2021/2022 and dissected them into a framework of eight categories, built around: airlines, airports, passengers, workforce, markets, contagion, sustainability, and economics. We highlight the essence of findings in the literature and derive a set of future research directions and policy considerations which we deem important on the way towards pandemic-resilient aviation.
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Affiliation(s)
- Xiaoqian Sun
- National Key Laboratory of CNS/ATM, School of Electronic and Information Engineering, Beihang University, 100191 Beijing, China
| | - Sebastian Wandelt
- National Key Laboratory of CNS/ATM, School of Electronic and Information Engineering, Beihang University, 100191 Beijing, China
| | - Anming Zhang
- Sauder School of Business, University of British Columbia, Vancouver, BC, Canada
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Sun X, Wandelt S, Zhang A. Why are COVID-19 travel bubbles a tightrope walk? An investigation based on the trans-tasmanian case. COMMUNICATIONS IN TRANSPORTATION RESEARCH 2022. [PMCID: PMC9676165 DOI: 10.1016/j.commtr.2022.100089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Choi Y, Zou L, Dresner M. The effects of air transport mobility and global connectivity on viral transmission: Lessons learned from Covid-19 and its variants. TRANSPORT POLICY 2022; 127:22-30. [PMID: 36035455 PMCID: PMC9391984 DOI: 10.1016/j.tranpol.2022.08.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 07/13/2022] [Accepted: 08/15/2022] [Indexed: 05/12/2023]
Abstract
We investigate the impact of air travel mobility and global connectivity on viral transmission by tracing the announced arrival time of COVID-19 and its major variants in countries around the world. We find that air travel intensity to a country, "effective distance" as measured by international air traffic, is generally a significant predictor for the announced viral arrival time. The level of healthcare infrastructure in a country is less important at predicting the initial transmission and detection time of a virus. A policy variable, notably the percentage reduction of total inbound seats in response to a viral outbreak, is largely ineffective at delaying viral transmission and discovery time. These findings suggest that air network connectivity is a major contributor to the speed of viral transmission. However, government attempts to delay viral transmission by reducing air network connectivity after the virus is first discovered are largely ineffective.
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Affiliation(s)
- Youngran Choi
- David B. O'Maley College of Business, Embry-Riddle Aeronautical University, 1 Aerospace Boulevard, Daytona Beach, FL, 32114, USA
| | - Li Zou
- David B. O'Maley College of Business, Embry-Riddle Aeronautical University, 1 Aerospace Boulevard, Daytona Beach, FL, 32114, USA
| | - Martin Dresner
- Logistics, Business & Public Policy, R.H. Smith School of Business, University of Maryland, College Park, MD, 20742, USA
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Bozkaya E, Eriskin L, Karatas M. Data analytics during pandemics: a transportation and location planning perspective. ANNALS OF OPERATIONS RESEARCH 2022; 328:1-52. [PMID: 35935742 PMCID: PMC9342597 DOI: 10.1007/s10479-022-04884-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/15/2022] [Indexed: 06/15/2023]
Abstract
The recent COVID-19 pandemic once again showed the value of harnessing reliable and timely data in fighting the disease. Obtained from multiple sources via different collection streams, an immense amount of data is processed to understand and predict the future state of the disease. Apart from predicting the spatio-temporal dynamics, it is used to foresee the changes in human mobility patterns and travel behaviors and understand the mobility and spread speed relationship. During this period, data-driven analytic approaches and Operations Research tools are widely used by scholars to prescribe emerging transportation and location planning problems to guide policy-makers in making effective decisions. In this study, we provide a review of studies which tackle transportation and location problems during the COVID-19 pandemic with a focus on data analytics. We discuss the major data collecting streams utilized during the pandemic era, highlight the importance of rapid and reliable data sharing, and give an overview of the challenges and limitations on the use of data.
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Affiliation(s)
- Elif Bozkaya
- Department of Computer Engineering, National Defence University, Turkish Naval Academy, 34940 Tuzla, Istanbul Turkey
| | - Levent Eriskin
- Department of Industrial Engineering, National Defence University, Turkish Naval Academy, 34940 Tuzla, Istanbul Turkey
| | - Mumtaz Karatas
- Department of Industrial Engineering, National Defence University, Turkish Naval Academy, 34940 Tuzla, Istanbul Turkey
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Liu J, Ong GP, Pang VJ. Modelling effectiveness of COVID-19 pandemic control policies using an Area-based SEIR model with consideration of infection during interzonal travel. TRANSPORTATION RESEARCH. PART A, POLICY AND PRACTICE 2022; 161:25-47. [PMID: 35603124 PMCID: PMC9110328 DOI: 10.1016/j.tra.2022.05.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
This paper studies the effectiveness of several pandemic restriction measures adopted in Singapore during the COVID-19 outbreak. To this end, the classical Susceptible-Exposed-Infectious-Recovered (SEIR) model widely used to describe the dynamic process of epidemic propagation is extended to an area-based SEIR model with the consideration of exposure to infections during commute and quarantine. The proposed model considers infections within areas and infections occurred during the commute of individuals. A case study of the Singapore MRT system is presented to show the effectiveness of pandemic restriction policies implemented in Singapore, namely social distancing, work shift and Circuit Breaker (CB) and phase advisories. A long-term investigation of COVID-19 pandemic in Singapore is performed, and the disease transmission dynamics in 2020-2021 (which covers the first wave and second wave of COVID-19 pandemic in Singapore) is modelled.
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Affiliation(s)
- Jielun Liu
- Department of Civil & Environmental Engineering, National University of Singapore, 117576, Singapore
| | - Ghim Ping Ong
- Department of Civil & Environmental Engineering, National University of Singapore, 117576, Singapore
| | - Vincent Junxiong Pang
- Saw Swee Hock School of Public Health, National University of Singapore, 117549, Singapore
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Liu S, Yamamoto T. Role of stay-at-home requests and travel restrictions in preventing the spread of COVID-19 in Japan. TRANSPORTATION RESEARCH. PART A, POLICY AND PRACTICE 2022; 159:1-16. [PMID: 35309690 PMCID: PMC8920346 DOI: 10.1016/j.tra.2022.03.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 10/27/2021] [Accepted: 03/02/2022] [Indexed: 05/19/2023]
Abstract
COVID-19 is one of the worst global health crises in a century. Japan confirmed its first case of COVID-19 in mid-January and declared a state of emergency in April and May 2020, urging people to stay at home and reduce travel. Using Mobile Spatial Statistics (i.e., population statistics created from operational data of mobile terminal networks), we estimated daily intra- and inter-prefectural population mobility in the Tokyo Megalopolis Region, Japan in 2020. Then, we developed a compartmental model with population mobility to explore the role of stay-at-home requests and travel restrictions in preventing the spread of COVID-19. This model describes the COVID-19 pandemic through a susceptible-exposed-presymptomatic infectious-undocumented and documented infectious-removed (SEPIR) process and incorporates intra- and inter-prefectural population mobility into the transmission process. We found that people significantly reduced travel during the state of emergency, although stay-at-home requests and travel restrictions were recommended rather than mandatory. The reduction in population mobility, combined with other control measures, resulted in a substantial reduction in effective reproduction numbers to below 1, thus controlling the first wave of the pandemic. Moreover, the relationship between population mobility and COVID-19 transmission changed over time. The dampening of the second wave of the pandemic indicated that smaller reductions in population mobility could result in pandemic control, probably because of other social distancing behaviors. Our proposed model can be used to analyze the impact of different public health interventions, and our findings shed light on the effectiveness of soft containments in curbing the spread of COVID-19.
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Affiliation(s)
- Shasha Liu
- Institute of Materials and Systems for Sustainability, Nagoya University, Nagoya 4648603, Japan
| | - Toshiyuki Yamamoto
- Institute of Materials and Systems for Sustainability, Nagoya University, Nagoya 4648603, Japan
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Luo Q, Gee M, Piccoli B, Work D, Samaranayake S. Managing public transit during a pandemic: The trade-off between safety and mobility. TRANSPORTATION RESEARCH. PART C, EMERGING TECHNOLOGIES 2022; 138:103592. [PMID: 35340721 PMCID: PMC8937026 DOI: 10.1016/j.trc.2022.103592] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Revised: 01/27/2022] [Accepted: 01/28/2022] [Indexed: 05/12/2023]
Abstract
During a pandemic such as COVID-19, managing public transit effectively becomes a critical policy decision. On the one hand, efficient transportation plays a pivotal role in enabling the movement of essential workers and keeping the economy moving. On the other hand, public transit can be a vector for disease propagation due to travelers' proximity within shared and enclosed spaces. Without strategic preparedness, mass transit facilities are potential hotbeds for spreading infectious diseases. Thus, transportation agencies face a complex trade-off when developing context-specific operating strategies for public transit. This work provides a network-based analysis framework for understanding this trade-off, as well as tools for calculating targeted commute restrictions under different policy constraints, e.g., regarding public health considerations (limiting infection levels) and economic activity (limiting the reduction in travel). The resulting plans ensure that the traffic flow restrictions imposed on each route are adaptive to the time-varying epidemic dynamics. A case study based on the COVID-19 pandemic reveals that a well-planned subway system in New York City can sustain 88% of transit flow while reducing the risk of disease transmission by 50% relative to fully-loaded public transit systems. Transport policy-makers can exploit this optimization-based framework to address safety-and-mobility trade-offs and make proactive transit management plans during an epidemic outbreak.
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Affiliation(s)
- Qi Luo
- Department of Industrial Engineering, Clemson University, Clemson, SC, USA
| | - Marissa Gee
- Center for Applied Mathematics, Cornell University, Ithaca, NY, USA
| | - Benedetto Piccoli
- Department of Mathematical Sciences, Rutgers University, Camden, NJ, USA
| | - Daniel Work
- Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, TN, USA
| | - Samitha Samaranayake
- School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA
- Center for Applied Mathematics, Cornell University, Ithaca, NY, USA
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Luo Q, Gee M, Piccoli B, Work D, Samaranayake S. Managing public transit during a pandemic: The trade-off between safety and mobility. TRANSPORTATION RESEARCH. PART C, EMERGING TECHNOLOGIES 2022. [PMID: 35340721 DOI: 10.2139/ssrn.3757210] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
During a pandemic such as COVID-19, managing public transit effectively becomes a critical policy decision. On the one hand, efficient transportation plays a pivotal role in enabling the movement of essential workers and keeping the economy moving. On the other hand, public transit can be a vector for disease propagation due to travelers' proximity within shared and enclosed spaces. Without strategic preparedness, mass transit facilities are potential hotbeds for spreading infectious diseases. Thus, transportation agencies face a complex trade-off when developing context-specific operating strategies for public transit. This work provides a network-based analysis framework for understanding this trade-off, as well as tools for calculating targeted commute restrictions under different policy constraints, e.g., regarding public health considerations (limiting infection levels) and economic activity (limiting the reduction in travel). The resulting plans ensure that the traffic flow restrictions imposed on each route are adaptive to the time-varying epidemic dynamics. A case study based on the COVID-19 pandemic reveals that a well-planned subway system in New York City can sustain 88% of transit flow while reducing the risk of disease transmission by 50% relative to fully-loaded public transit systems. Transport policy-makers can exploit this optimization-based framework to address safety-and-mobility trade-offs and make proactive transit management plans during an epidemic outbreak.
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Affiliation(s)
- Qi Luo
- Department of Industrial Engineering, Clemson University, Clemson, SC, USA
| | - Marissa Gee
- Center for Applied Mathematics, Cornell University, Ithaca, NY, USA
| | - Benedetto Piccoli
- Department of Mathematical Sciences, Rutgers University, Camden, NJ, USA
| | - Daniel Work
- Department of Civil and Environmental Engineering, Vanderbilt University, Nashville, TN, USA
| | - Samitha Samaranayake
- School of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USA
- Center for Applied Mathematics, Cornell University, Ithaca, NY, USA
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