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.

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.

The prevention of crowding inside buses, trams and trains is an important component of on-board passenger comfort and is central to the provision of good public transport services. In light of the COVID-19 pandemic and the associated significant reduction in public transport patronage and, more importantly, in passenger confidence, the avoidance of crowds by passengers and operators alike becomes even more critical. This is where the provision of information on on-board comfort becomes a necessity. The present study, therefore, proposes a new Kalman filter based estimation scheme for on-board comfort levels, employing historical and current (same-day) non-exhaustive Automatic Passenger Counting data, as well as Automatic Vehicle Locating measurements. The accuracy and reliability of the estimation is, then, evaluated through application to the tramway network of the French city of Nantes. The results suggest that the proposed method is able to deliver good estimation accuracy, both in terms of absolute passenger numbers, but also, more crucially, in terms of on-board comfort Levels of Service.

The ongoing COVID-19 pandemic has caused the death of millions of people, and PCR testing is widely used as the gold standard method to detect the infections to restrict the outbreak. Through the interviews conducted with people from the field in South Korea, the UK, and Turkey, we have found that there are numerous testing strategies worldwide. Those testing strategies include drive-through and home delivery testing capabilities, local test sites, and mobile test centers. Our primary motivation is to propose a generic model based on the best practices in the UK and South Korea. Also, we aim to present a case study on Turkey for the implementation of vital procedures and increase their availability. This paper represents a study on how to construct a temporary testing logistics system during the initial phases of pandemics to increase the availability of PCR testing with the primary objective of maximizing total sample collection. The design also considers minimizing the maximum walking distance to increase the convenience of sample collection for the people living in the neighborhoods. The proposed system consists of temporary testing centers and a central laboratory. Temporary testing centers perform direct tours to the potential areas to collect samples and bring the collected sample to the designated central laboratories located at central hospitals. Moreover, to represent the non-linear inheritance of the pandemic progress within a population, we consider diminishing sample potentials over time and coverage. This new problem is defined as an extension of the Selective Vehicle Routing Problem and Covering Tour Problem. We propose a mathematical model and four two-stage math-heuristic algorithms to determine the location and routing of the temporary testing centers and their lengths of stay at each visited location. The performances of the proposed solution methodologies are tested on two data sets. The first set is constructed by the confirmed cases of the districts of Seoul, Korea, and by the interview of health personnel of H+ Yangji Hospital COVID-19 semi-mobile booth application, and the second set is constructed by 99 hospital/health centers from distinct neighborhoods of 22 districts of Istanbul, Turkey. The Pareto set of optimum solutions is generated based on total sample collection and maximum walking distance. Finally, sensitivity analyses on some design parameters are conducted.

While advancements in vehicular and wireless communication technologies are shaping the future of our transportation system, emergency medical services (EMS) are not receiving enough research attention. Their operations are still plagued by response delays that can often be life-threatening. Dispatching and redeployment systems identify the best practices regarding the allocation of the resources to emergencies and stations. Yet, the existing systems are unfortunately insufficient, and there is a growing need to embrace new technological solutions. This research introduces a smart system for EMS by leveraging the modular vehicle technology initially developed for transit systems. The proposed system relies on the design of vehicular modules that can couple and decouple to transfer patients from one module to another during transport. A fleet of medical transport vehicles is deployed to cooperate with the life support vehicles by providing, for example, transport and hospital admission tasks, thus allowing life support vehicles to answer pending emergency calls earlier. This is especially useful when there is a large demand for EMS (e.g. under the COVID-19 pandemic or other disasters such as the recent explosion in Beirut). This paper introduces a mathematical programming model to determine the optimal assignment decisions in a deterministic setting. This work is a proof of concept that demonstrates the applicability of the modular vehicle technology to EMS, evaluating the upper bound EMS performance that can be ultimately reached. A sensitivity analysis is conducted to provide insights and recommendations that are useful when selecting the weighting coefficients for the optimization function, to ensure a more efficient implementation of the modular vehicle technology for EMS. Also, the results of a comparative analysis show that the proposed system can adapt and offer larger benefits, in terms of response times and times to hospital, as demand increases and/or resources become more limited.

Adaptive Cruise Control (ACC) systems have been expected to solve many problems of motorway traffic. Now that they are widespread, it is observed that the majority of existing systems are string unstable. Therefore, small perturbations in the speed profile of a vehicle are amplified for the vehicles following upstream, with negative impacts on traffic flow, fuel consumption, and safety. Increased headway settings provide more stable flow but at the same time it deteriorates the capacity. Substantial research has been carried out in the past decade on utilizing connectivity to overcome this trade-off. However, such connectivity solutions have to overcome several obstacles before deployment and there is the concrete risk that motorway traffic flow will considerably deteriorate in the meanwhile. As an alternative solution, the paper explores multianticipation without inter-vehicle communication, taking advantage of the recent advancements in the field of RADAR sensing. An analytical study is carried out, based on the most widely used model and parameter settings used to simulate currently available commercial ACC systems, comparing the transfer functions and step responses for the nominal and the multianticipative formulations. Then, a microsimulation framework is employed to validate our claim on different speed profiles. Analytical results demonstrate that multianticipation enhances stability without impacting traffic flow. On the contrary, the simulation study indicates that the multianticipative-ACC can produce higher road capacity even in the presence of external disturbances and for a wide range of calibrated parameters. Finally, optimality conditions for the tuning of the headway policy are derived from a Pareto optimization.

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.

Contact tracing is an effective measure by which to prevent further infections in public transportation systems. Considering the large number of people infected during the COVID-19 pandemic, digital contact tracing is expected to be quicker and more effective than traditional manual contact tracing, which is slow and labor-intensive. In this study, we introduce a knowledge graph-based framework for fusing multi-source data from public transportation systems to construct contact networks, design algorithms to model epidemic spread, and verify the validity of an effective digital contact tracing method. In particular, we take advantage of the trip chaining model to integrate multi-source public transportation data to construct a knowledge graph. A contact network is then extracted from the constructed knowledge graph, and a breadth-first search algorithm is developed to efficiently trace infected passengers in the contact network. The proposed framework and algorithms are validated by a case study using smart card transaction data from transit systems in Xiamen, China. We show that the knowledge graph provides an efficient framework for contact tracing with the reconstructed contact network, and the average positive tracing rate is over 96%.

During the COVID-19 pandemic, the collapse of the public transit ridership led to significant budget deficits due to dramatic decreases in fare revenues. Additionally, public transit agencies are facing challenges of reduced vehicle capacity due to social distancing requirements, additional costs of cleaning and protective equipment, and increased downtime for vehicle cleaning. Due to these constraints on resources and budgets, many transit agencies have adopted essential service plans with reduced service hours, number of routes, or frequencies. This paper studies the resiliency during a pandemic of On-Demand Multimodal Transit Systems (ODMTS), a new generation of transit systems that combine a network of high-frequency trains and buses with on-demand shuttles to serve the first and last miles and act as feeders to the fixed network. It presents a case study for the city of Atlanta and evaluates ODMTS for multiple scenarios of depressed demand and social distancing representing various stages of the pandemic. The case study relies on an optimization pipeline that provides an end-to-end ODMTS solution by bringing together methods for demand estimation, network design, fleet sizing, and real-time dispatching. These methods are adapted to work in a multimodal setting and to satisfy practical constraints. In particular, a limit is imposed on the number of passenger transfers, and a new network design model is introduced to avoid the computational burden stemming from this constraint. Real data from the Metropolitan Atlanta Rapid Transit Authority (MARTA) is used to conduct the case study, and the results are evaluated with a high-fidelity simulation. The case study demonstrates how ODMTS provide a resilient solution in terms of cost, convenience, and accessibility for this wide range of scenarios.

Household travel survey data is a critical input to travel behavior modeling, and it also can be used to generate trip schedules for activity-based traffic simulation. With emerging information and communication technology (ICT) tools like smartphones, the collection of passive datasets for travelers' real-time information becomes available. Smartphone GPS survey apps have emerged to be a popular tool for conducting household travel surveys. Most existing studies employ high-frequency smartphone GPS data and collect accurate activity information. However, their study periods are still rather short, ranging from a few days to a few weeks. For a long-term GPS survey, the issues of missing activity information and sparse GPS data are inevitable and must be addressed carefully. This paper uses 7-month low-frequency smartphone GPS data collected from over 2000 participants, who report 5 most frequently visited locations weekly. The essential goal is to develop a synthetic model of daily activity-location scheduling to capture data with both known and unknown activities. To handle missing activity data, this research develops a new probabilistic approach, which measures the probability of visiting a place by three scores, global visit score (GVS), temporal visit score (TVS), and periodical visit score (PVS). Three different levels of activity-location schedule are modeled respectively. The first level handles only those data with known activities, while data with unknown activities are disregarded. The second takes unknown activities into account but combines all types of them into a single category. The third one models each location with unknown activities separately. These models are able to generate activity-location schedule in different levels of detail for activity-based traffic simulator. After developing activity-location schedule models, both individual and aggregated validation processes are performed with simulation. The validation result shows that the simulated proportion of activity types and activity duration are close to the survey data, indicating the effectiveness of the proposed approaches. This research sheds a light on building sustainable and long-term travel survey using GPS data with missing activity information. In addition, this study will be valuable to model infectious disease transmission, e.g. COVID-19 and assess health risk in urban areas.

During the coronavirus disease 2019 pandemic, the activity engagement and travel behavior of city residents have been impacted by government restrictions, such as temporary city-wide lockdowns, the closure of public areas and public transport suspension. Based on multiple heterogeneous data sources, which include aggregated mobility change reports and household survey data, this paper proposes a machine learning approach for dynamic activity chain pattern estimation with improved interpretability for examining behavioral pattern adjustments. Based on historical household survey samples, we first establish a computational graph-based discrete choice model to estimate the baseline travel tour parameters before the pandemic. To further capture structural deviations of activity chain patterns from day-by-day time series, we define the activity-oriented deviation parameters within an interpretable utility-based nested logit model framework, which are further estimated through a constrained optimization problem. By incorporating the long short-term memory method as the explainable module to capture the complex periodic and trend information before and after interventions, we predict day-to-day activity chain patterns with more accuracy. The performance of our model is examined based on publicly available datasets such as the 2017 National Household Travel Survey in the United States and the Google Global Mobility Dataset throughout the epidemic period. Our model could shed more light on transportation planning, policy adaptation and management decisions during the pandemic and post-pandemic phases.

In this paper, we propose a novel approach to model spatial heterogeneity for epidemic spreading, which combines the relevance of transport proximity in human movement and the excellent estimation accuracy of deep neural network. We apply this model to investigate the effects of various transportation networks on the heterogeneous propagation of COVID-19 in China. We further apply it to predict the development of COVID-19 in China in two scenarios, i.e., i) assuming that different types of traffic restriction policies are conducted and ii) assuming that the epicenter of the COVID-19 outbreak is in Beijing, so as to illustrate the potential usage of the model in generating various policy insights to help the containment of the further spread of COVID-19. We find that the most effective way to prevent the coronavirus from spreading quickly and extensively is to control the routes linked to the epicenter at the beginning of the pandemic. But if the virus has been widely spread, setting restrictions on hub cities would be much more efficient than imposing the same travel ban across the whole country. We also show that a comprehensive consideration of the epicenter location is necessary for disease control.

The advent of COVID-19 is a sensible reminder of the vulnerability of our society to pandemics. We need to be better prepared for finding ways to stem such outbreaks. Except from social distancing and wearing face masks, restricting the movement of people is one important measure necessary to control the spread. Such decisions on the lock-down/reduction of movement should be made in an informed way and, accordingly, modeled as an optimization problem. We propose the Early-stage Transportation Lock-down and Quarantine Problem (TLQP), which can help to decide which parts of the transportation infrastructure of a country should be restricted in early stages. On top of the network-based Susceptible-Exposed-Infectious-Recovered (SEIR) model, we establish a decision recommendation framework, which considers the lock-down of cross-border traffic, internal traffic, and movement inside individual populations. The combinatorial optimization problem aims to find the best set of actions which minimize the social cost of a lock-down. Given the inherent intractability of this problem, we develop a highly-efficient heuristic based on the Effective Distance (ED) path and the Cost-Effective Lazy Forward (CELF) algorithm. We perform and report experiments on the global spread of COVID-19 and show how individual countries may protect their population by taking appropriate measures against the threatening pandemic. We believe that our study contributes to the orchestration of measures for dealing with current and future epidemic outbreaks.

During the unprecedented coronavirus disease 2019 (COVID-19) challenge, non-pharmaceutical interventions became a widely adopted strategy to limit physical movements and interactions to mitigate virus transmissions. For situational awareness and decision-support, quickly available yet accurate big-data analytics about human mobility and social distancing is invaluable to agencies and decision-makers. This paper presents a big-data-driven analytical framework that ingests terabytes of data on a daily basis and quantitatively assesses the human mobility trend during COVID-19. Using mobile device location data of over 150 million monthly active samples in the United States (U.S.), the study successfully measures human mobility with three main metrics at the county level: daily average number of trips per person; daily average person-miles traveled; and daily percentage of residents staying home. A set of generalized additive mixed models is employed to disentangle the policy effect on human mobility from other confounding effects including virus effect, socio-demographic effect, weather effect, industry effect, and spatiotemporal autocorrelation. Results reveal the policy plays a limited, time-decreasing, and region-specific effect on human movement. The stay-at-home orders only contribute to a 3.5%-7.9% decrease in human mobility, while the reopening guidelines lead to a 1.6%-5.2% mobility increase. Results also indicate a reasonable spatial heterogeneity among the U.S. counties, wherein the number of confirmed COVID-19 cases, income levels, industry structure, age and racial distribution play important roles. The data informatics generated by the framework are made available to the public for a timely understanding of mobility trends and policy effects, as well as for time-sensitive decision support to further contain the spread of the virus.

The corona pandemic significantly changes the processes of aircraft and passenger handling at the airport. In our contribution, we focus on the time-critical process of aircraft boarding, where regulations regarding physical distances between passengers will significantly increase boarding time. The passenger behavior is implemented in a field-validated stochastic cellular automata model, which is extended by a module to evaluate the transmission risk. We propose an improved boarding process by considering that most of the passengers are travel together and should be boarded and seated as a group. The NP-hard seat allocation of groups with minimized individual interactions between groups is solved with a genetic algorithm. Then, the improved seat allocation is used to derive an associated boarding sequence aiming at both short boarding times and low risk of virus transmission. Our results show that the consideration of groups will significantly contribute to a faster boarding (reduction of time by about 60%) and less transmission risk (reduced by 85%) compared to the standard random boarding procedures applied in the pandemic scenario.

Passenger contact in public transit (PT) networks can be a key mediate in the spreading of infectious diseases. This paper proposes a time-varying weighted PT encounter network to model the spreading of infectious diseases through the PT systems. Social activity contacts at both local and global levels are also considered. We select the epidemiological characteristics of coronavirus disease 2019 (COVID-19) as a case study along with smart card data from Singapore to illustrate the model at the metropolitan level. A scalable and lightweight theoretical framework is derived to capture the time-varying and heterogeneous network structures, which enables to solve the problem at the whole population level with low computational costs. Different control policies from both the public health side and the transportation side are evaluated. We find that people's preventative behavior is one of the most effective measures to control the spreading of epidemics. From the transportation side, partial closure of bus routes helps to slow down but cannot fully contain the spreading of epidemics. Identifying "influential passengers" using the smart card data and isolating them at an early stage can also effectively reduce the epidemic spreading.

In recent years, there has been a rapid growth of smart apps that could interact with users and implement personalized rewards to coordinate and change user behavior. Understanding user behavior is an enabling factor for the success of these promising apps. However, existing statistical models for modeling user behavior encounter limitations. Choice models based on Random Utility Maximization (RUM) commonly assume that the data collection is independent with the human behavior. However, when users interact with the apps, the real potential and also the real challenge for modeling user behavior is that the apps not merely are data collection tools, but also change users' behaviors. In this work, we model the user behavior as a graphical model, examine our hypothesis that existing choice models are not suitable, and develop an interesting computational strategy using max-margin formulation to overcome the learning challenge of the our proposed graphical model that is named the Latent Decision Threshold (LDT) model.

The intercontinental liner shipping services transport containers between two continents and they are crucial for the profitability of a global liner shipping company. In the daily operations of an intercontinental liner shipping service, however, container slot bookings from customers can be freely cancelled during a booking period, which causes loss of revenue and low utilization of ship capacity. Though a pain-point of the liner shipping industry, the container slot cancellation problem has not yet been well investigated in the literature. To fill this research gap, this study aims to estimate the probability for the cancellation of container slot booking in the long haul transports of the intercontinental liner shipping service by considering the primary influential factors of cancellation behavior. To achieve the objective, a container slot booking data-driven model is developed by means of a time-to-event modeling technique. To incorporate the effect of booking region on the cancellation probability, we introduce the frailty term in the model to capture the regionality of the container shipping market. Our case study with real slot booking data shows that the developed model performs well in forecasting the loaded containers of the slot booking requests. In addition, we shed light on how the internal factors of slot booking and external factors of shipping market influence the probability of cancellation.

The use of smartphone applications (apps) to acquire real time and readily available journey planning information is becoming instinctive behavior by public transport (PT) users. Through the apps, a passenger not only seeks a path from origin to destination, but a satisfactory path that caters to the passenger's preferences at the desired time of travel. Essentially, apps attempt to provide a means of personalized PT service. As the implications of the Covid-19 pandemic take form and infiltrate human and environmental interactions, passenger preference personalization will likely include avoiding risks of infection or contagious contact. The personal preferences are enabled by multiple attributes associated with alternative PT routes. For instance, preferences can be connected to attributes of time, cost, and convenience. This work establishes a personalized PT service, as an adjustment to current design frameworks, by integrating user app experience with operators' data sources and operations modeling. The work proceeds to focus on its key component: the personalized route guidance methodology. In addition to using the existing shortest path or weighted shortest path method, this study develops a novel, lexicographical shortest path method, considering a just noticeable difference (JND). The method adopts lexicographical ordering to capture passenger preferences for different PT attributes following Ernst Weber's law of human perception threshold. However, a direct application of Weber's law violates the axiom of transitivity required for an implementable algorithm, and thus, a revised method is developed with proven algorithms for ranking different paths. The differences between the three route-guidance methods and the effects of the JND perception threshold on the order of the alternative PT routes are demonstrated with an example. The developments were examined in a case study by simulation on the Copenhagen PT network. The results show that using the JND method reduces the value/cost of the most important attributes. Identical robust results are attained when JND parameters are not specified and default values are used. The latter may apply for the future with a mixture of specified and default preference input values. Finally, the computation time indicates a favorable potential for real-life applications. It is believed that the consideration of human threshold perception will encourage decision makers to establish new criteria to comply with this.

Crowding is one of the most common problems for public transportation systems worldwide, and extreme crowding can lead to passengers being left behind when they are unable to board the first arriving bus or train. This paper combines existing data sources with an emerging technology for object detection to estimate the number of passengers that are left behind on subway platforms. The methodology proposed in this study has been developed and applied to the subway in Boston, Massachusetts. Trains are not currently equipped with automated passenger counters, and farecard data is only collected on entry to the system. An analysis of crowding from inferred origin-destination data was used to identify stations with high likelihood of passengers being left behind during peak hours. Results from North Station during afternoon peak hours are presented here. Image processing and object detection software was used to count the number of passengers that were left behind on station platforms from surveillance video feeds. Automatically counted passengers and train operations data were used to develop logistic regression models that were calibrated to manual counts of left behind passengers on a typical weekday with normal operating conditions. The models were validated against manual counts of left behind passengers on a separate day with normal operations. The results show that by fusing passenger counts from video with train operations data, the number of passengers left behind during a day's rush period can be estimated within of their actual number.

Passively-generated data, such as GPS data and cellular data, bring tremendous opportunities for human mobility analysis and transportation applications. Since their primary purposes are often non-transportation related, the passively-generated data need to be processed to extract trips. Most existing trip extraction methods rely on data that are generated via a single positioning technology such as GPS or triangulation through cellular towers (thereby called single-sourced data), and methods to extract trips from data generated via multiple positioning technologies (or, multi-sourced data) are absent. And yet, multi-sourced data are now increasingly common. Generated using multiple technologies (e.g., GPS, cellular network- and WiFi-based), multi-sourced data contain high variances in their temporal and spatial properties. In this study, we propose a "Divide, Conquer and Integrate" (DCI) framework to extract trips from multi-sourced data. We evaluate the proposed framework by applying it to an app-based data, which is multi-sourced and has high variances in both location accuracy and observation interval (i.e. time interval between two consecutive observations). On a manually labeled sample of the app-based data, the framework outperforms the state-of-the-art SVM model that is designed for GPS data. The effectiveness of the framework is also illustrated by consistent mobility patterns obtained from the app-based data and an externally collected household travel survey data for the same region and the same period.