ABSTRACT
In order to better understand the impact of COVID-19 on the free-floating bike-sharing (FFBS) system and the potential role of FFBS played in the pandemic period, this study explores the impact mechanism of travel frequency of FFBS users before and after the pandemic. Using the online questionnaire collected in Nanjing, China, we first analyze the changes of travel frequency, travel distance, and travel duration in these two periods. Then, two ordered logit models are applied to explore the contributing factors of the weekly trip frequency of FFBS users before and after COVID-19. The results show that: (1) While the overall travel duration and travel distance of FFBS users decreased after the pandemic, the trip frequency of FFBS users increased as the travel duration increased. (2) Since COVID-19, attitude perception variables of the comfort level and the low travel price have had significantly positive impacts on the weekly trip frequency of FFBS users. (3) Respondents who use FFBS as a substitution for public transport are more likely to travel frequently in a week after the outbreak of COVID-19. (4) The travel time in off-peak hours of working days, weekends, and holidays has a significantly positive correlation with the trip frequency of FFBS users. Finally, several relevant policy recommendations and management strategies are proposed for the operation and development of FFBS during the similar disruptive public health crisis.
ABSTRACT
The world has experienced an unprecedented global health crisis since 2020, the COVID-19 pandemic, which inflicted massive burdens on countries' healthcare systems. During the peaks of the pandemic, the shortages of intensive care unit (ICU) beds illustrated a critical vulnerability in the fight. Many individuals suffering the effects of COVID-19 had difficulty accessing ICU beds due to insufficient capacity. Unfortunately, it has been observed that many hospitals do not have enough ICU beds, and the ones with ICU capacity might not be accessible to all population strata. To remedy this going forward, field hospitals could be established to provide additional capacity in helping emergency health situations such as pandemics; however, location selection is a crucial decision ultimately for this purpose. As such, we consider finding new field hospital locations to serve the demand within certain travel-time thresholds, while accounting for the presence of vulnerable populations. A multi-objective mathematical model is proposed in this paper that maximizes the minimum accessibility and minimizes the travel time by integrating the Enhanced 2-Step Floating Catchment Area (E2SFCA) method and travel-time-constrained capacitated p-median model. This is performed to decide on the locations of field hospitals, while a sensitivity analysis addresses hospital capacity, demand level, and the number of field hospital locations. Four counties in Florida are selected to implement the proposed approach. Findings can be used to identify the ideal location(s) of capacity expansions concerning the fair distribution of field hospitals in terms of accessibility with a specific focus on vulnerable strata of the population.
ABSTRACT
This study analyzes the effect of the restrictions in traffic movement enforced in order to combat the spread of coronavirus on air quality and travel time reliability under heterogeneous and laneless traffic conditions. A comparative analysis was conducted to examine quantity of pollutants, average travel time distributions (TTD), and their associated travel time reliability (TTR) metrics during the COVID-19 pandemic, postpandemic, and during partial restrictions. Pollutants data (PM2.5, NO2, and NOX) and travel time data for selected locations from Chennai City in India were collected for a sample period of one week using Wi-Fi sensors and state-run air quality monitoring stations. It was observed that the average quantity of PM2.5, NO2, and NOX were increased by 433.1%, 681.4%, and 99.2%, respectively, during the postlockdown period. Correlation analysis also indicated that all considered air pollutants are moderately correlated to Wi-Fi hits, albeit to varied degrees. From the analysis, it was also found that average TTD mean and interquartile range values were increased by 47.2% and 105.2%. In addition, the buffer time index, planning time index, travel index, and capacity buffer index associated with these TTD metrics were increased by 148.1%, 63.7%, 42.8%, and 202.9%, respectively, soon after relaxing travel restrictions. © 2023 American Society of Civil Engineers.
ABSTRACT
This paper presents a new method to quantify the potential user time savings if the urban bus is given preferential treatment, changing from mixed traffic to an exclusive bus lane, using a big data approach. The main advantage of the proposal is the use of the high amount of information that is automatically collected by sensors and management systems in many different situations with a high degree of spatial and temporal detail. These data allow ready adjustment of calculations to the specific reality measured in each case. In this way, we propose a novel methodology of general application to estimate the potential passenger savings instead of using simulation or analytical methods already present in the literature. For that purpose, in the first place, a travel time prediction model per vehicle trip has been developed. It has been calibrated and validated with a historical series of observations in real-world situations. This model is based on multiple linear regression. The estimated bus delay is obtained by comparing the estimated bus travel time with the bus travel time under free-flow conditions. Finally, estimated bus passenger time savings would be obtained if an exclusive bus lane had been implemented. An estimation of the passenger's route in each vehicle trip is considered to avoid average value simplifications in this calculation. A case study is conducted in A Coruña, Spain, to prove the methodology's applicability. The results showed that 18.7% of the analyzed bus trips underwent a delay exceeding 3 min in a 2,448 m long corridor, and more than 33,000 h per year could have been saved with an exclusive bus lane. Understanding the impact of different factors on transit and the benefits of a priority bus system on passengers can help city councils and transit agencies to know which investments to prioritize given their limited budget.
ABSTRACT
The COVID-19 pandemic continues to have an unprecedented impact on people's lives and the economy worldwide. Vaccines are the strongest evidence-based defense against the spread of the disease. The release of COVID-19 vaccines to the general public created policy challenges associated with how to best allocate vaccines among different sub-regions. In the United States, after vaccines became widely available for all eligible adults, policymakers faced objectives such as (i) achieving an equitable allocation to reduce populations' travel times to get vaccinated and (ii) effectively allocating vaccine doses to minimize waste and unmet need. This problem was further exacerbated by the underlying factors of population vaccine hesitancy and sub-regions' varying capacity levels to administer vaccines to eligible and willing populations. Although simple to implement, commonly used pro rata policies do not capture the complexities of this problem. We propose two alternatives to simple pro rata policies. The first alternative is based on a Mixed-Integer Linear Programming Model that minimizes the maximum travel duration of patients and aims to achieve an equitable and effective allocation of vaccines to sub-regions while considering capacity and vaccine hesitancy. A second alternative is a heuristic approach that may be more palatable for policymakers who (i) are not familiar with mathematical modeling, (ii) are reluctant to use black-box models, and (iii) prefer algorithms that are easy to understand and implement. We demonstrate the results of our model through a case study based on real data from the state of Alabama and show that substantial improvements in travel time-based equity are achievable through capacity improvements in a small subset of counties. We perform additional computational experiments that compare the proposed methods in terms of several metrics and demonstrate the promising performance of our model and proposed heuristic. We find that while our mathematical model can achieve equitable and effective vaccine allocation, the proposed heuristic performs better if the goal is to minimize average travel duration. Finally, we explore two model extensions that aim to (i) lower vaccine hesitancy by allocating vaccines, and (ii) prioritize vaccine access for certain high-risk sub-populations.
ABSTRACT
The Covid-19 pandemic has had a significant influence on the usage of public transport, in this case, a decrease in the use of public transport services. The decreased use of public transport was because the government created a new social system called social distance, followed by the Enforcement of Restrictions on Community Activities (PPKM). This study aims to perform utility and probability modelling and determine the most significant factor in influencing the preferences of the community in using public transport during the Covid-19 pandemic and the implementation of the PPKM level 3 policy in Padang City. Primary data was collected using online and offline questionnaires. The questionnaire used the expressed preference technique on eight alternatives with three attributes and two levels. The attributes include the difference in travel time, the cost of travel, and health protocols. The best model was Y(AUK-AUO) = 0.656 + (- 0.038) X1 + (- 0.056) X2 + 0.644 X3 with R-Square = 0.174 referring to all characteristics as a whole influence 17.4% of Y. According to the sensitivity test results, the travel cost difference is the most influencing variable in people's decisions to use traditional public transportation during the covid-19 pandemic and the implementation of the PPKM level 3 policy.
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
Inspired by the global supply chain disruptions caused by the COVID-19 pandemic, we study optimal procurement and inventory decisions for a pharmaceutical supply chain over a finite planning horizon. To model disruption, we assume that the demand for medical drugs is uncertain and shows spatiotemporal variability. To address demand uncertainty, we propose a two-stage optimization framework, where in the first stage, the total cost of pre-positioning drugs at distribution centers and its associated risk is minimized, while the second stage minimizes the cost of recourse decisions (e.g., reallocation, inventory management). To allow for different risk preferences, we propose to capture the risk of demand uncertainty through the expectation and worst-case measures, leading to two different models, namely (risk-neutral) stochastic programming and (risk-averse) robust optimization. We consider a finite number of scenarios to represent the demand uncertainty, and to solve the resulting models efficiently, we propose L-shaped decomposition-based algorithms. Through extensive numerical experiments, we illustrate the impact of various parameters, such as travel time, product's shelf life, and waste due to transportation and storage, on the supply chain resiliency and cost, under optimal risk-neutral and risk-averse policies. These insights can assist decision makers in making informed choices. © 2023 Elsevier Ltd
ABSTRACT
MaaS (Mobility as a Service) itself has come into common use, and these developments have attracted keen interest from the industry in recent years. MaaS can be applied as a solution to deal with the current situation by considering the social distance. However, due to the time-share mechanism, personal assets are monopolized by specific users for a long time that cannot be shared with other users at the same time. Thus, the sharing economy companies in the tourism industry (e.g., Airbnb Experience and Huber) are in a dilemma of low productivity and high cost. In this research, we propose a new travel guide sharing service that considers the concept of social distance and user preferences. The user side only needs to select simple conditions such as travel time and the number of POIs (Point of Interest) that she/he plans to visit, meanwhile, the guide side simply inputs the POIs that she/he can guide. Furthermore, by analyzing these basic information, our proposed system can recommend the tour guides, scenic spots, and route planning to provide a real-time tour guide plan, which addressed the user's preferences and reduced the face-to-face communication to users in advance. To verify the effectiveness of our proposed method, we also ask 68 users to evaluate our system and analyze the results of questionnaires. © 2023 IEEE Computer Society. All rights reserved.
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
Coronavirus first appeared in January 2020 and has spread dramatically in most parts of the world. In addition to exerting enormous impacts on public health and well-being, it has also affected a broad spectrum of industries and sectors, including transportation. Countries around the world have imposed restrictions on travel and participation in activities due to the outbreak of the virus. Many countries have adopted social distancing rules requiring people to maintain a safe distance. Therefore, the pandemic has accelerated the transition into a world in which online education, online shopping, and remote working are becoming increasingly prevalent. Every aspect of our life has witnessed a series of new rules, habits, and behaviours during this period, and our travel choices or behaviours are no exception. Some of these changes can be permanent or have long-lasting effects. To control this situation, these changes must first be recognised in various aspects of transportation in order to provide policies for similar situations in the future. In this regard, this study seeks to examine how transportation sectors have changed in the first waves of the pandemic. Iran has been selected as the case study in this paper. This research is divided into two parts. The first part focuses on the effects of the Coronavirus pandemic on rural transportation in Iran. This is followed by assessing the impacts of the virus on urban transportation in Tehran (the capital of Iran). The behaviour of more than 700 travellers in terms of trip purpose, travel time, and mode choice is evaluated using a questionnaire. Results indicate that the number of passengers has reduced dramatically in rural transportation systems. In such systems, considerations such as keeping social distancing, disinfection of passengers and their luggage, and unemployment of a group of personnel working in the transportation industry have been more evident. In urban transportation, education trips have dropped the most. This might relate to an increase in online teaching and health concerns. The same pattern can be seen in the passengers who used bicycles, public taxis, and other public transportation systems. Finally, during the pandemic, drivers' speed has increased, which justifies the need for traffic calming for drivers. © 2022 Warsaw University of Technology. All rights reserved.
ABSTRACT
This paper describes two studies that aimed to explore the impacts of pedestrianisation or road closures on traffic displacement, travel behaviour and the phenomenon of ‘disappearing traffic'. The first study surveyed residents whose travel routes were affected by a small-scale localised pedestrianisation scheme in the centre of a town. The second measured the traffic impacts of a temporary closure of a strategic bridge in a city centre. In the first case, the pedestrianisation produced no change in the modal shares of travel of residents. Drivers continued to drive to the same locations by longer routes. In the second case, the closure caused some traffic displacement and increased journey times but also reduced traffic volumes in both the immediate area and across the city. This paper concludes by discussing the remaining knowledge gaps on disappearing traffic, made more pressing by the decisions of authorities to reallocate road space during the coronavirus disease 2019 crisis.
ABSTRACT
In this paper, we propose a solution for optimizing the routes of Mobile Medical Units (MMUs) in the domain of vehicle routing and scheduling. The generic objective is to optimize the distance traveled by the MMUs as well as optimizing the associated cost. These MMUs are located at a central depot. The idea is to provide improved healthcare to the rural people of India. The solution is obtained in two stages: preparing a mathematical model with the most suitable parameters, and then in the second phase, implementing an algorithm to obtain an optimized solution. The solution is focused on multiple parameters, including the number of vans, number of specialists, total distance, total travel time, and others. The solution is further supported by Reinforcement Learning, explaining the best possible optimized route and total distance traveled.
ABSTRACT
The rapid outbreak of COVID-19 pandemic invoked scientists and researchers to prepare the world for future disasters. During the pandemic, global authorities on healthcare urged the importance of disinfection of objects and surfaces. To implement efficient and safe disinfection services during the pandemic, robots have been utilized for indoor assets. In this paper, we envision the use of drones for disinfection of outdoor assets in hospitals and other facilities. Such heterogeneous assets may have different service demands (e.g., service time, quantity of the disinfectant material etc.), whereas drones have typically limited capacity (i.e., travel time, disinfectant carrying capacity). To serve all the facility assets in an efficient manner, the drone to assets allocation and drone travel routes must be optimized. In this paper, we formulate the capacitated vehicle routing problem (CVRP) to find optimal route for each drone such that the total service time is minimized, while simultaneously the drones meet the demands of each asset allocated to it. The problem is solved using mixed integer programming (MIP). As CVRP is an NP-hard problem, we propose a lightweight heuristic to achieve sub-optimal performance while reducing the time complexity in solving the problem involving a large number of assets. © 2022 IEEE.
ABSTRACT
This study investigated the performance of rural public bus transport services in Jordan Valley during COVID-19. Jordan Valley consists of three brigades;Southern Shouneh, Deir Alla, and Northern Shouneh. The performance measures included availability, comfort and convenience, waiting time, mobility, productivity, and safety for the external and internal bus routes. The names, number of buses, and fares for bus routes were obtained from Land Transport Regulatory Commission of Jordan (LTRC). The field survey consisted of interviews with passengers and drivers in addition to direct field observations. The average waiting time for both the minibuses and microbuses at off-peak hours was found twice and half the waiting time at peak hours. The minimum and maximum values of the average speed varied between 40 to 100 km/h for the external routes and between 30-90 km/h for the internal routes. As a productivity measure, the average operating ratio for the internal routes was found 2.09 and 1.38 for the external routes. 60% of the microbuses obliged to the stated fare in comparison to minibuses in which all of them obliged to the stated fare. It was found that 40% of the external bus routes were within the range of overall Level of Service (LOS) between C & D, 26.67% within the range of LOS between B & D, 13.33% within the range of LOS between B & C, 13.33% within the range between C & E, and 6.67% within the range between D & E. Also, it was found that 60% of internal bus routes were within the range of LOS between C & D, 20% within the range of LOS between C & E, and 20% within the range of LOS C. The developed regression models between the average perception waiting time as dependent variable and travel time as independent variable were found significant at α-level < 0.05, with r2 = 0.505 at peak periods and r2 = 0.673 at off-peak period.
ABSTRACT
Leisure trips have become more important in an era where people are increasingly concerned with quality of life. Leisure trips are unique in that they are not as strict as mandatory trips, while, at the same time, they have wider characteristics because of their flexibility. Research on leisure trips from developing countries is still under-represented as there is still a focus on commuting trips. This study aims to identify factors that influence the mode of transportation choice for leisure trips by domestic travelers who live in cities surrounding Bandung, Indonesia. Data were collected using stated-preference self-report questionnaires distributed to locals who have the intention to travel for leisure in Bandung in the future. Based on responses from 305 respondents with a total number of 1220 observations, a multinomial logit model was estimated. It was found that trains and buses were selected more often by locals than other modes of transportation, including private cars, for leisure trips. Our model showed that locals considered travel time and travel costs as the most significant factors in selecting the mode of transportation for their leisure trips. Besides the existence of online transportation—hailing rides through mobile apps—as an alternative, this study also reveals payment method to be a unique consideration of locals when travelling leisurely in this digital era.
ABSTRACT
The spread of COVID-19 has a great impact on public transport which is closely related to social life. As an essential carrier of the cities, metro has become an important object of concern during the epidemic. Due to the high infection risk of COVID-19 in public space, it is necessary to quantitatively evaluate and perform corresponding epidemic control measures on reducing public health risks in metro station. In this paper, three strategies of passenger rescheduling, i.e. controlling the flows of inbound and outbound passengers in the station, setting route guidance in the crucial areas and shortening the interval time of train, are simulated and analyzed based on Anylogic. The performances of different strategies are characterized and evaluated by the important parameters, which include local passengers' density, inbound and outbound time. Finally, the optimization experiments based on an objective function are carried out to obtain the best strategy combination considering passengers' health safety and travel efficiency. The crucial areas with high density are obtained from the simulation results of the initial model. The three independent strategies are helpful in reducing the maximum passengers' density and average travel time. The optimization results of strategy combination and the specific parameters of each strategy are obtained by the final simulation experiment. The research findings are important reference to enhance the present health risk management level and provide specific measures of passenger organization in metro station under COVID-19. © 2023 Elsevier Ltd
ABSTRACT
The COVID-19 pandemic has led to an urgent need in emerging economies to quickly identify vulnerable populations that do not live within access of a health facility for testing and vaccination. This access information is critical to prioritize investments in mobile and temporary clinics. To meet this need, the World Bank team sought to develop an open-source methodology that could be quickly and easily implemented by government health departments, regardless of technical and data collection capacity. The team explored use of readily available open-source and licensable data, as well as non-intensive computational methodologies. By bringing together population data from Facebook's Data for Good program, travel-time calculations from Mapbox, road network and point-of-interest data from the OpenStreetMap (OSM), and the World Bank's open-source GOSTNets network routing tools, we created a computational framework that supports efficient and granular analysis of road-based access to health facilities in two pilot locations—Indonesia and the Philippines. Our findings align with observed health trends in these countries and support identification of high-density areas that lack sufficient road access to health facilities. Our framework is easy to replicate, allowing health officials and infrastructure planners to incorporate access analysis in pandemic response and future health access planning. © National Academy of Sciences: Transportation Research Board 2022.
ABSTRACT
Epidemic outbreaks, such as the one generated by the coronavirus disease, have raised the need for more efficient healthcare logistics. One of the challenges that many governments have to face in such scenarios is the deployment of temporary medical facilities across a region with the purpose of providing medical services to their citizens. This work tackles this temporary-facility location and queuing problem with the goals of minimising costs, the expected completion time, population travel time, and waiting time. The completion time for a facility depends on the numbers assigned to those facilities as well as stochastic arrival times. This work proposes a learnheuristic algorithm to solve the facility location and population assignment problem. Firstly a machine learning algorithm is trained using data from a queuing model (simulation module). The learnheuristic then constructs solutions using the machine learning algorithm to rapidly evaluate decisions in terms of facility completion and population waiting times. The efficiency and quality of the algorithm is demonstrated by comparison with exact and simulation-only (simheuristic) methodologies. A series of experiments are performed which explore the trade-offs between solution cost, completion time, population travel time, and waiting time. © 2023 The Operational Research Society.
ABSTRACT
Bus operators have to make trade-offs between transporting more passengers and maintaining social distancing to reduce ridership congregation amid Corona Virus Disease 2019 (COVID-19) outbreak. The traditional bus boarding mode could easily lead passengers fully occupy the bus available capacity at one stop, and it would prevent subsequent passengers from boarding. It is crucial to establish a new operating mode and strategy to ensure all passengers have opportunities to ride and to collaboratively optimise the bus timetable. In this paper, the boarding limit strategy that considers the fairness of passenger boarding probability is proposed to address the inequitable problem with minimise the passenger travel time and the number of stranded passengers. The coupling relationship between bus dwell time and passenger flow is used to collaboratively optimise the bus timetable. Case studies are conducted to illustrate the performance of the boarding limit strategy in improving passenger boarding equity. © 2023 Hong Kong Society for Transportation Studies Limited.