ABSTRACT
During the COVID-19 pandemic, authorities in many places have implemented various countermeasures, including setting up a cordon sanitaire to restrict population movement. This paper proposes a bi-level programming model to deploy a limited number of parallel checkpoints at each entry link around the cordon sanitaire to achieve a minimum total waiting time for all travelers. At the lower level, it is a transportation network equilibrium with queuing for a fixed travel demand and given road network. The feedback process between trip distribution and trip assignment results in the predicted waiting time and traffic flow for each entry link. For the lower-level model, the method of successive averages is used to achieve a network equilibrium with queuing for any given allocation decision from the upper level, and the reduced gradient algorithm is used for traffic assignment with queuing. At the upper level, it is a queuing network optimization model. The objective is the minimization of the system's total waiting time, which can be derived from the predicted traffic flow and queuing delay time at each entry link from the lower-level model. Since it is a nonlinear integer programming problem that is hard to solve, a genetic algorithm with elite strategy is designed. An experimental study using the Nguyen-Dupuis road network shows that the proposed methods effectively find a good heuristic optimal solution. Together with the findings from two additional sensitivity tests, the proposed methods are beneficial for policymakers to determine the optimal deployment of cordon sanitaire given limited resources. © National Academy of Sciences: Transportation Research Board 2021.
ABSTRACT
Health care systems throughout the world are under pressure as a result of COVID-19. It is over two years since the first case was announced in China and health care providers are continuing to struggle with this fatal infectious disease in intensive care units and inpatient wards. Meanwhile, the burden of postponed routine medical procedures has become greater as the pandemic has progressed. We believe that establishing separate health care institutions for infected and non-infected patients would provide safer and better quality health care services. The aim of this study is to find the appropriate number and location of dedicated health care institutions which would only treat individuals infected by a pandemic during an outbreak. For this purpose, a decision-making framework including two multi-objective mixed-integer programming models is developed. At the strategic level, the locations of designated pandemic hospitals are optimized. At the tactical level, we determine the locations and operation durations of temporary isolation centers which treat mildly and moderately symptomatic patients. The developed framework provides assessments of the distance that infected patients travel, the routine medical services expected to be disrupted, two-way distances between new facilities (designated pandemic hospitals and isolation centers), and the infection risk in the population. To demonstrate the applicability of the suggested models, we perform a case study for the European side of Istanbul. In the base case, seven designated pandemic hospitals and four isolation centers are established. In sensitivity analyses, 23 cases are analyzed and compared to provide support to decision makers.
ABSTRACT
COVID-19 has shocked every system in the U.S., including transportation. In the first months of the pandemic, driving and transit use fell far below normal levels. Yet people still need to travel for essential purposes like medical appointments, buying groceries, and-for those who cannot work from home-to work. For some, the pandemic may exacerbate extant travel challenges as transit agencies reduce service hours and frequency. As travelers reevaluate modal options, it remains unclear how one mode-ride-hailing-fits into the transportation landscape during COVID-19. In particular, how does the number of ride-hail trips vary across neighborhood characteristics before versus during the pandemic? And how do patterns of essential trips pre-pandemic compare with those during COVID-19? To answer these questions, we analyzed aggregated Uber trip data before and during the first two months of the COVID-19 pandemic across four regions in California. We find that during these first months, ride-hail trips fell at levels commensurate with transit (82%), while trips serving identified essential destinations fell by less (62%). Changes in ride-hail use were unevenly distributed across neighborhoods, with higher-income areas and those with more transit commuters and higher shares of zero-car households showing steeper declines in the number of trips made during the pandemic. Conversely, neighborhoods with more older (aged 45+) residents, and a greater proportion of Black, Hispanic/Latinx, and Asian residents still appear to rely more on ride-hail during the pandemic compared with other neighborhoods. These findings further underscore the need for cities to invest in robust and redundant transportation systems to create a resilient mobility network.
ABSTRACT
Ridesplitting – a type of ride-hailing in which riders share vehicles with other riders – has become a common travel mode in some major cities. This type of shared ride option is currently provided by transportation network companies (TNCs) such as Uber, Lyft, and Via and has attracted increasing numbers of users, particularly before the COVID-19 pandemic. Previous findings have suggested ridesplitting can lower travel costs and even lessen congestion by reducing the number of vehicles needed to move people. Recent studies have also posited that ridesplitting should experience positive feedback mechanisms in which the quality of the service would improve with the number of users. Specifically, these systems should benefit from economies of scale and increasing returns to scale. This paper demonstrates evidence of their existence using trip data reported by TNCs to the City of Chicago between January and September 2019. Specifically, it shows that increases in the number of riders requesting or authorizing shared trips during a given time period is associated with shorter trip detours, higher rates of riders being matched together, lower costs relative to non-shared trips, and higher willingness for riders to share trips.
ABSTRACT
With the continuous development of the COVID-19 pandemic, the selection of locations for medical isolation areas has not always been optimal for the timely transportation of infected people, or those suspected of being infected. This has resulted in failure to control the rate of spread of infection cases in time. To address this problem, this paper proposes a co-evolutionary location-routing optimization (CELRO) model of medical isolation areas for use in major public health emergencies to develop a rapid location-routing scheme for epidemic isolation, including the selection of locations of medical isolation facilities per area and the optimal route per vehicle to each infected person. Specifically, this paper solves the following two sub-problems: (i) calculate the shortest transportation times and corresponding routes from any medical isolation area to any person infected or suspected of being infected, and (ii) calculate the location scheme for distribution of isolation areas. Different from previous studies, the vehicle operating characteristics and the interference of uncertainty of the traffic environment are considered in the proposed model. To find an appropriate scheme for location of medical isolation areas with the shortest travel times, a co-evolutionary clustering algorithm (CECA), which is a combination of some separated evolutionary programming operations, is proposed to solve the model. Various network sizes and uncertainty combinations are used to design some comparative tests, which aim to verify the effectiveness of the proposed model. In the experiment section, CELRO reduced travel time by at least 14% compared with other methods. This finding can provide an effective theoretical basis for optimizing the spatial layout of medical isolation areas or the location planning of new medical facilities. © National Academy of Sciences.
ABSTRACT
Ridepooling service options introduced by transportation network companies (TNCs) and microtransit companies provide opportunities to increase shared-ride trips in vehicles, thereby improving congestion and environmental factors. This paper reviews the existing literature available on ridepooling and related services, specifically focusing on pooling options available from on-demand transportation companies. The paper summarizes the existing knowledge on the use of pooled-ride services, factors in travel mode service options for customers, available policy and planning strategies to incentivize sharing vehicles, and effects of the COVID-19 pandemic on shared-ride travel. Overall, research shows that ridepooling options are more likely to be considered by public transit users who have lower household incomes, while ridesourcing users of upperclass backgrounds are less likely to consider moving to a shared-ride service. Travel time and trip cost are the most important factors for travelers determining whether to use a ridesplitting or microtransit service rather than a ride-alone ridesourced trip. Existing policy and planning tools targeting pooled travel or TNCs can be expanded on and specified for on-demand ridepooling services, such as offering better incentives to use shared vehicles and increased access to curb areas or travel lanes, but the most effective strategies will include increasing the user costs for parking or riding alone. © National Academy of Sciences.
ABSTRACT
In the aftermath of a disruptive event like the onset of the COVID-19 pandemic, it is important for policymakers to quickly understand how people are changing their behavior and their goals in response to the event. Choice modeling is often applied to infer the relationship between preference and behavior, but it assumes that the underlying relationship is stationary: that decisions are drawn from the same model over time. However, when observed decisions outcomes are non-stationary in time because, for example, the agent is changing their behavioral policy over time, existing methods fail to recognize the intent behind these changes. To this end, we introduce a non-parametric sequentially-valid online statistical hypothesis test to identify entities in the urban environment that ride-sourcing drivers increasingly sought out or avoided over the initial months of the COVID-19 pandemic. We recover concrete and intuitive behavioral patterns across drivers to demonstrate that this procedure can be used to detect behavioral trends as they are emerging.
ABSTRACT
COVID-19 has affected the lives and well-being of billions of citizens worldwide. While nondrug interventions have been partially effective in containing the COVID-19 epidemic, vaccination has become the most important factor in maintaining public health and reducing deaths. In this study, a model is proposed to overcome the difficulties in organising vaccination due to heterogeneous population distribution in cities and to optimise the vaccination process considering the available resources. The results of the model are of strategic importance for the control of the COVID-19. Considering the transportation structures, population and vaccine resources in the regions, a different number of clusters is formed for each city. Each cluster consists of several districts that share health resources. A hybrid approach consisting of mathematical modelling and k-means algorithm is proposed, and it reduced the difference between vaccination times of three different vaccination clusters to about 3.5 days. The results also showed that the vaccination process can be reduced from 108 days to 44 days, which meant a 40% improvement in speed for administering vaccines. In this case study, we presented a vaccination programme in which the average antibody rate of individuals does not fall below the critical-time threshold. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
ABSTRACT
Sudden infectious diseases and other malignant events cause excessive costs in the supply chain, particularly in the transportation sector. This issue, along with the uncertainty of the development of global epidemics and the frequency of extreme natural disaster events, continues to provoke discussion and reflection. However, transport systems involve interactions between different modes, which are further complicated by the reliable coupling of multiple modes. Therefore, for the vital subsystem of the supply chain-multimodal transport, in this paper, a heuristic algorithm considering node topology and transport characteristics in a multimodal transport network (MTN): the Reliability Oriented Routing Algorithm (RORA), is proposed based on the super-network and improved k-shell (IKS) algorithm. An empirical case based on the Yangtze River Delta region of China demonstrates that RORA enables a 16% reduction in the boundary value for route failure and a reduction of about 60.58% in the route cost increase compared to the typical cost-optimal algorithm, which means that RORA results in a more reliable routing solution. The analysis of network reliability also shows that the IKS values of the nodes are positively correlated with the reliability of the MTN, and nodes with different modes may have different transport reliabilities (highest for highways and lowest for inland waterways). These findings inform a reliability-based scheme and network design for multimodal transportation. Practical Applications: Recently, the COVID-19 epidemic and the frequency of natural disasters such as floods have prompted scholars to consider transport reliability. Therefore, efficient and reliable cargo transportation solutions are crucial for the sustainable development of multimodal transport in a country or region. In this paper, a new algorithm is designed to obtain a reliability-oriented optimal routing scheme for multimodal transport. Using actual data from the Yangtze River Delta region of China as an example for experimental analysis, we obtain that: (1) the proposed algorithm is superior in terms of efficiency, accuracy, and route reliability, which means that the new algorithm can quickly find more reliable routing solutions in the event of urban transport infrastructure failures;and (2) highway hubs have the greatest transport reliability. Conversely, inland waterway hubs are the least reliable. The influence of national highways and railways on the multimodal transport system is unbalanced. These findings provide decision support to transport policymakers on reliability. For example, transport investments should be focused on building large infrastructure and increasing transport capacity, strengthening the connectivity of inland waterway hubs to hubs with higher transport advantages, and leveraging the role of large hubs. © 2023 American Society of Civil Engineers.
ABSTRACT
Many studies have investigated the impact of mobility restriction policies on the change of intercity flows during the outbreak of COVID-19, whereas only a few have highlighted intracity flows. By using the mobile phone trajectory data of approximately three months, we develop an interrupted time series quasi-experimental design to estimate the abrupt and gradual effects of mobility intervention policies during the pandemic on intracity flows of 491 neighborhoods in Shenzhen, China, with a focus on the role of urban transport networks. The results show that the highest level of public health emergency response caused an abrupt decline by 4567 trips and a gradually increasing effect by 34 trips per day. The effectiveness of the second return-to-work order (RtW2) was found to be clearly larger than that of the first return-to-work order (RtW1) as a mobility restoration strategy. The causal effects of mobility intervention policies are heterogenous across zonal locations in varying urban transport networks. The declining effect of health emergency response and rebounding effect of RtW2 are considerably large in better-connected neighborhoods with metro transit, as well as in those close to the airport. These findings provide new insights into the identification of pandemic-vulnerable hotspots in the transport network inside the city, as well as of crucial neighborhoods with increased adaptability to mobility interventions during the onset and decline of COVID-19.
ABSTRACT
Introduction: Drivers of Transportation Network Companies (TNC) are an essential workforce that is affected by extreme weather events and high exposure risk to airborne infectious diseases due to their proximity with customers. The purpose of this study was to understand TNC drivers' professional experience during the COVID-19 pandemic and their opinions about climate change and the development of future pandemics. Methods: Open- and closed-ended responses were collected during TNC rides and analyzed with content analysis and descriptive statistics. Results: We found more participants believed in the COVID-19 pandemic compared to participants who believed in climate change. Overall, participants were less concerned about COVID-19 than climate change. However, several participants felt that the pandemic had a positive impact on the climate system, specifically by reducing air pollution from traffic. Few participants felt that climate change could lead to the development of future pandemics. Conclusions: The TNC essential workforce could be integral for identifying transportation and public health sectors solutions for addressing the occupational health needs of an essential workforce, and response to climate change and pandemics.
ABSTRACT
When COVID-19 suddenly broke out, the epidemic areas are short of basic emergency relief which need to be transported from surrounding areas. To make transportation both time-efficient and cost-effective, we consider a multimodal hub-and-spoke transportation network for emergency relief schedules. Firstly, we establish a mixed integer nonlinear programming (MINLP) model considering multi-type emergency relief and multimodal transportation. The model is a bi-objective one that aims at minimizing both transportation time consumption and transportation costs. Due to its NP-hardness, devising an efficient algorithm to cope with such a problem is challenging. This study thus employs and redesigns Grey Wolf Optimizer (GWO) to tackle it. To benchmark our algorithm, a real-world case is tested with three solution methods which include other two state-of-the-art meta-heuristics. Results indicate that the customized GWO can solve such a problem in a reasonable time with higher accuracy. The research could provide significant practical management insights for related government departments and transportation companies on designing an effective transportation network for emergency relief schedules when faced with the unexpected COVID-19 pandemic.
ABSTRACT
Ridepooling service options introduced by transportation network companies (TNCs) and microtransit companies provide opportunities to increase shared-ride trips in vehicles, thereby improving congestion and environmental factors. This paper reviews the existing literature available on ridepooling and related services, specifically focusing on pooling options available from on-demand transportation companies. The paper summarizes the existing knowledge on the use of pooled-ride services, factors in travel mode service options for customers, available policy and planning strategies to incentivize sharing vehicles, and effects of the COVID-19 pandemic on shared-ride travel. Overall, research shows that ridepooling options are more likely to be considered by public transit users who have lower household incomes, while ridesourcing users of upper-class backgrounds are less likely to consider moving to a shared-ride service. Travel time and trip cost are the most important factors for travelers determining whether to use a ridesplitting or microtransit service rather than a ride-alone ridesourced trip. Existing policy and planning tools targeting pooled travel or TNCs can be expanded on and specified for on-demand ridepooling services, such as offering better incentives to use shared vehicles and increased access to curb areas or travel lanes, but the most effective strategies will include increasing the user costs for parking or riding alone.
ABSTRACT
The impacts of COVID-19 on for-hire vehicle (FHV) (e.g., Uber/Lyft, often referred to as transportation network companies in other locations) and taxi use have been relatively understudied compared with transit and personal vehicles. This study analyzed and estimated the changes in ridership for taxis and FHVs in New York City during the COVID-19 pandemic to determine whether it had disproportional impacts on these competing modes, how these impacts varied over time and space, and the associated factors. Data supporting the analyses came from the Taxi and Limousine Commission, the COVID-19 Data Repository, Google's Community Mobility Reports, the American Community Survey, and the Primary Land Use Tax Lot Output. Temporal change was measured by the daily taxi/FHV ridership deviation from a defined baseline, which showed that COVID-19 more negatively impacted taxis than FHVs. Temporal moving average models were then employed, which showed that COVID-19 had different temporal impacts on taxis and FHVs in relation to the parameters’ significance, magnitude, and temporal correlation patterns. In general, taxi/FHV ridership dropped when people spent more time at home and the number of confirmed COVID-19 cases was greater. The spatial variation in taxi/FHV ridership was measured by the coefficient of variation. Spatial regression models indicated that the land use of a zone affected taxi/FHV ridership during the pandemic. In addition, a zone with more carless/car-free households, older persons, or more children enrolled in school was more likely to experience a decrease in taxi/FHV ridership. A zone with more workers who commuted by walking or taking transit (excluding taxis) in pre-COVID times was more likely to see a decrease in taxi/FHV ridership. A zone with more people working from home pre-COVID, was more likely to see an increase in FHV ridership. The models showed that COVID-19 had greater spatial impacts on taxis than FHVs. Based on these results, this study provides insights as to what factors affected ridership of the two competing travel modes and suggests actions that transportation authorities could take to reduce temporal and spatial impact disparities.
ABSTRACT
Transportation networks are sized to efficiently achieve some set of service objectives. Under particular circumstances, such as the COVID-19 pandemic, the demand for transportation can significantly change, both qualitatively and quantitatively, resulting in an over-capacitated and less efficient network. In this paper, we address this issue by proposing a framework for resizing the network to efficiently cope with the new demand. The framework includes a model to determine an optimal transportation sub-network that guarantees the following: (i) the minimal access time from any node of the urban network to the new sub-network has not excessively increased compared to that of the original transportation network;(ii) the delay induced on any itinerary by the removal of nodes from the original transportation network has not excessively increased;and (iii) the number of removed nodes from the transportation network is within a preset known factor. A solution is optimal if it induces a minimal global delay. We modelled this problem as a Mixed Integer Linear Program and applied it to the public bus transportation network of Lyon, France, in a case study. In order to respond to operational issues, the framework also includes a decision tool that helps the network planners to decide which bus lines to close and which ones to leave open according to specific trade-off preferences. The results on real data in Lyon show that the optimal sub-network from the MILP model can be used to feed the decision tool, leading to operational scenarios for network planners.
ABSTRACT
Problem, research strategy, and findings New transportation options like ride-hail can expand accessibility without the costs of car ownership. Ride-hail's potential is particularly salient for lower-income and zero-car households. We used interviews and a national (U.S.) survey to examine how and why lower-income travelers in the United States use ride-hail. Survey and interview responses provided a temporal snapshot and thus reflect, in part, travel challenges specific to COVID-19. Findings suggest that lower-income travelers, particularly those without personal cars, use ride-hail in ways distinct from those typically reported in broader travel surveys. Individuals without cars are more likely to use ride-hail, and use it more often, compared with people with cars, particularly to fill spatial and temporal gaps in public transit service and to access medical care and groceries. Costs and price unpredictability remain significant barriers limiting travelers' use of ride-hail services. Takeaway for practice This research demonstrates a latent need for car access among lower-income travelers. Substantial gaps in alternative modes pose challenges for travelers seeking reliable and timely transportation. Planners should invest in transit, biking, and walking to provide robust alternatives to car ownership. Such investments, however, take time. In the meantime, cities and agencies should consider subsidizing ride-hail trips to bridge existing gaps in the transportation network.
ABSTRACT
The transportation network design and frequency setting problem concerns the optimization of transportation systems comprising fleets of vehicles serving a set amount of passengers on a predetermined network (e.g., public transport systems). It has been a persistent focus of the transportation planning community while, its NP-hard nature continues to present obstacles in designing efficient, all-encompassing solutions. In this paper, we present a new approach based on an alternating-objective genetic algorithm that aims to find Pareto optimality between user and operator costs. Extensive computational experiments are performed on Mandl’s benchmark test and prove that the results generated by our algorithm are 5–6% improved in comparison to previously published results for Pareto optimality objectives both in regard to user and operator costs. At the same time, the methods presented are computationally inexpensive and easily run on office equipment, thus minimizing the need for expensive server infrastructure and costs. Additionally, we identify a wide variance in the way that similar computational results are reported and, propose a novel way of reporting benchmark results that facilitates comparisons between methods and enables a taxonomy of heuristic approaches to be created. Thus, this paper aims to provide an efficient, easily applicable method for finding Pareto optimality in transportation networks while highlighting specific limitations of existing research both in regards to the methods used and the way they are communicated.
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The epidemic that occurs in any part of today's rapidly globalizing world;It spreads very quickly with strong transportation networks between countries and causes a serious threat to all countries. Due to the rapid spread of the coronavirus and its effects on human health, it was declared a pandemic by the World Health Organization (WHO) on 11 March 2019. Early detection of the disease is very important to prevent the further spread of this epidemic. Thanks to the early diagnosis of positive cases, affected patients can be treated quickly. Serious lung damage can occur in people who contract this disease, which has been declared a pandemic by WHO. The helpful tool that can provide high accuracy and rapid diagnosis of COVID-19 infection is useful for specialist doctors. Doctors diagnose chest diseases with the help of X-Ray Chest X-Ray - (CXR) and Computed Tomography (CT) lung images. Preventive measures can be taken quickly and the diagnosis processes of the physicians can be shortened with an early diagnosis made before the doctor's control for those who are considered to have Covid-19 at the time of CXR images. Artificial intelligence methods are used in the field of health. In addition, these methods give successful results in the diagnosis of many diseases. The application of advanced machine learning and artificial intelligence techniques to images obtained with radiological imaging techniques can help in the accurate detection of Covid-19 disease. CNN techniques have often been used to diagnose Covid-19 disease. The basic features of the images are used to diagnose the Covid-19 disease. Some pre-trained models with a large number of learnable parameters such as AlexNet, GoogleNet, ResNet, VGGNET, and Inception have demonstrated the detection of Covid-19. © 2021 IEEE.
ABSTRACT
The COVID-19 pandemic has adversely affected the entire world. The effective implementation of vaccination strategy is critical to prevent the resurgence of the pandemic, especially during large-scale population migration. We establish a multiple patch coupled model based on the transportation network among the 31 provinces in China, under the combined strategies of vaccination and quarantine during large-scale population migration. Based on the model, we derive a critical quarantine rate to control the pandemic transmission and a vaccination rate to achieve herd immunity. Furthermore, we evaluate the influence of passenger flow on the effective reproduction number during the Chinese-Spring-Festival travel rush. Meanwhile, the spread of the COVID-19 pandemic is investigated for different control strategies, viz. global control and local control. The impact of vaccine-related parameters, such as the number, the effectiveness and the immunity period of vaccine, are explored. It is believed that the articulated models as well as the presented simulation results could be beneficial to design of feasible strategies for preventing COVID-19 transmission during the Chinese-Spring-Festival travel rush or the other future events involving large-scale population migration.
Subject(s)
COVID-19 , Quarantine , China/epidemiology , Holidays , Humans , Mathematical Concepts , Models, Biological , Pandemics/prevention & control , SARS-CoV-2 , Travel , VaccinationABSTRACT
In light of the long-term pressure and short-term impact of economic and technological globalization, regional and urban resilience has become an important issue in research. As a new organizational form of regional urban systems, the resilience of urban networks generated by flow space has emerged as a popular subject of research. By gathering 2017 data from the Baidu search index, the Tencent location service, and social statistics, this study constructs information, transportation, and economic networks among 344 cities in China to analyze the spatial patterns of urban networks and explore their structural characteristics from the perspectives of hierarchy and assortativity. Transmissibility and diversity were used to represent the resilience of the network structure in interruption scenarios (node failure and maximum load attack). The results show the following: The information, transportation, and economic networks of cities at the prefecture level and higher in China exhibit a dense pattern of spatial distribution in the east and a sparse pattern in the west;however, there are significant differences in terms of hierarchy and assortativity. The order of resilience of network transmissibility and diversity from strong to weak was information, economic, transportation. Transmissibility and diversity had nearly identical scores in response to the interruption of urban nodes. Moreover, a highly heterogeneous network was more likely to cause shocks to the network structure, owing to its cross-regional urban links in case of disturbance. We identified 12 dominant nodes and 93 vulnerable nodes that can help accurately determine the impetus behind network structure resilience. The capacity of regions for resistance and recovery can be improved by strengthening the construction of emergency systems and risk prevention mechanisms.