Cyber-Physical Modeling of Intelligent Traffic Controllers Using Simulator for Urban Mobility (SUMO)

Transportation problems have always been a global concern. The challenges in traffic congestion were easily observed during pre-pandemic times. However, traffic congestion still persists even during the COVID-19 pandemic (2020 and present) where there has been less number of vehicles because of travel restrictions. The emergence of wireless communication technologies and intelligent transportation systems (ITS) pave the way for solving some of the problems found in the transportation industry. Subsequently, traffic control systems are used at various intersections to manage the flow of traffic and reduce car collisions. However, some intersections are better off without these traffic control systems. The proposed study will analyze a T-junction road in five different setups using different types of traffic controllers. The simulation tool used is SUMO. The study found that an adaptive or vehicle-actuated traffic controller is the ideal method for regulating traffic flow in a T-junction with a one-way or two-way main road. It was observed in the simulation that it reduced the potential car collisions in the non-TL junction. However, the average speed and completion time of the road network was affected by the method. © 2022 IEEE.

Optimal Deployment of Cordon Sanitaire with Available Testing Capacity

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.

Pausing the Pandemic: Understanding and Managing Traveler and Community Spread of COVID-19 in Hawaii.

In the absence of a vaccine, nonpharmaceutical interventions such as social distancing and travel reductions were the only strategies for slowing the spread of the COVID-19 pandemic. Using survey data from Hawaii (n = 22,200) collected in March through May of 2020 at the onset of the pandemic, the differences between traveler spreaders who brought the disease into the state and community spreaders were investigated. In addition to describing the demographic attributes and comparing them with attributes of those who were vulnerable to COVID-19, logit models explaining travel behaviors were developed and tested. Traveler spreaders were likely to be male, younger, and returning students. Community spreaders were more likely to be male, essential workers, first responders, and medical personnel at the highest risk of exposure. Using spatial statistics, clusters and hotspot locations of high-risk individuals were mapped. As transportation researchers are in a position to combine their critical analytical capabilities and experience with relevant databases on mobility and the spread of infectious diseases, this analysis could support efforts to respond to and slow the spread of the pandemic.

Ten Takeaways from the COVID-19 Pandemic for Transportation Planners.

The COVID-19 pandemic has created significant challenges but also unprecedented opportunities for transportation researchers and practitioners. In this article, the major lessons and gaps in knowledge for those working in the transportation sector are identified, including the following: (1) integration between public health and transportation; (2) technology to support contact tracing and tracking of travelers; (3) focus on vulnerable, at-risk operators, patrons, and underserved members of society; (4) re-engineering of travel demand models to support social distancing, quarantine, and public health interventions; (5) challenges with Big Data and information technologies; (6) trust relationships between the general public, government, private sector, and others in disaster management; (7) conflict management during disasters; (8) complexities of transdisciplinary knowledge and engagement; (9) demands for training and education; and (10) transformative change to support community resilience. With a focus on transportation planning and community resilience, the lessons from the pandemic need to be shared and customized for different systems, services, modalities, and users. While many of the interventions during the pandemic have been based on public health, the management, response, recovery, adaptation, and transformation of transportation systems resulting from the crisis require multi-disciplinary, multi-jurisdictional communications and coordination, and resource sharing. Further research to support knowledge to action is needed.

Bioprotection of Transportation and Facilities from SARS-CoV-2 (COVID-19).

The recent COVID-19 pandemic has led to a nearly world-wide shelter-in-place strategy. This raises several natural concerns about the safe relaxing of current restrictions. This article focuses on the design and operation of heating ventilation and air conditioning (HVAC) systems in the context of transportation. Do HVAC systems have a role in limiting viral spread? During shelter-in-place, can the HVAC system in a dwelling or a vehicle help limit spread of the virus? After the shelter-in-place strategy ends, can typical workplace and transportation HVAC systems limit spread of the virus? This article directly addresses these and other questions. In addition, it also summarizes simplifying assumptions needed to make meaningful predictions. This article derives new results using transform methods first given in Ginsberg and Bui. These new results describe viral spread through an HVAC system and estimate the aggregate dose of virus inhaled by an uninfected building or vehicle occupant when an infected occupant is present within the same building or vehicle. Central to these results is the derivation of a quantity called the "protection factor"-a term-of-art borrowed from the design of gas masks. Older results that rely on numerical approximations to these differential equations have long been lab validated. This article gives the exact solutions in fixed infrastructure for the first time. These solutions, therefore, retain the same lab validation of the older methods of approximation. Further, these exact solutions yield valuable insights into HVAC systems used in transportation.

Vehicle Design Strategies to Reduce the Risk of COVID-19 Transmission in Shared and Pooled Travel: Inventory, Typology, and Considerations for Research and Implementation

The global COVID-19 pandemic has given rise to a plethora of ideas for modifying and redesigning public transportation and shared mobility vehicles to protect workers and riders from contracting the disease while traveling. This research seeks to inventory these strategies, and to organize and distill them in a way that enables researchers, policymakers, and public transport and mobility service operators to more systematically and efficiently evaluate them. Through literature search and analysis, the COVID-19 risk-mitigating vehicle design (CRVD) typology was developed, articulating 12 categories of strategies (e.g., Seating Configuration, Barriers) and 12 mechanisms (e.g., physical distancing, physical separation) by which the strategies may reduce COVID-19 spread. A secondary contribution of this research is to gather opinions of experts in fields related to COVID-19 and its transmission, about the identified CRVD strategies and mitigation mechanisms. The typology and expert opinions serve as a launching point for further innovation and research to evaluate the effectiveness of CRVD strategies and their relationship to user preferences and travel behavior, within and beyond the current context. Public transport and shared mobility service operators can use the CRVD typology as a reference, in conjunction with industry guidance and emerging research on strategy effectiveness, to aid decision-making in their continued response to the pandemic as well as for future planning.

Analysis of Public Transportation in Montevideo, Uruguay During the COVID-19 Pandemic

This article presents the analysis of the demand and the characterization of mobility using public transportation in Montevideo, Uruguay, during the COVID-19 pandemic. A urban data-analysis approach is applied to extract useful insights from open data from different sources, including mobility of citizens, the public transportation system, and COVID cases. The proposed approach allowed computing significant results to determine the reduction of trips caused by each wave of the pandemic, the correlation between the number of trips and COVID cases, and the recovery of the use of the public transportation system. Overall, results provide useful insights to quantify and understand the behavior of citizens in Montevideo, regarding public transportation during the COVID-19 pandemic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Do Residents Living in Transit-Oriented Development Station Catchment Areas Travel More Sustainably? The Impacts of Life Events

Transit-oriented development (TOD) is an urban designed model aimed at attracting more sustainable travellers. However, not all TOD projects succeed in maintaining a high rate of sustainable travel behaviour. To examine the impacts of TOD on residents' travel behaviour, this paper applies binary logistic regression to analyse survey data for 1,298 residents living in the TOD areas in Hangzhou collected in 2020. The results show that socioeconomic characteristics, built environment factors, and travel attitudes play important roles in influencing their travel mode choices. Furthermore, the number of children in households and higher levels of car ownership significantly influence residents' sustainable travel behaviours. However, it appears that only a limited number of factors can convince car users to shift to sustainable modes of travel, such as their workplace being accessible by metro and attitudes towards changes in accessibility. This research study contributes to the existing literature in terms of enhancing the understanding of travel mode choice behaviours, particularly with regard to people who live near public transport infrastructure, as well as formulating evidence-based TOD policies to achieve more sustainable transport systems.

India's public transportation system: the repercussions of COVID-19

The COVID-19 pandemic has left scars on the Indian public transportation system. In order to regain its original momentum, policymakers will need to assess the barriers hindering the effectiveness of the public transportation sector. In this regard, this article analyzes the various factors affecting the public transportation sector in India and determines their interrelationships. The research is presented in three steps. First, we review the literature to identify the factors that affect the public transportation system in India. Next, we propose an integrated model of grey-DEMATEL and ANP, grey-DANP, to calculate the priority ranking and weight of the factors. The grey-DEMATEL method is used to find the interrelationships among the factors, while ANP determines the local and global weights of the factors to form a priority order. Then, we present the interrelationships in the form of influential relation maps. Furthermore, we provide a sensitivity analysis to enhance the credibility of our study. The paper reveals that governmental regulations are the most influential factors in India's public transportation system. The transportation authorities and policymakers must also focus on improving the financial stability and enhancing the customer's trust in the public transportation system. The framework provided in this paper can be applied to other countries where similar hindrances in the public transportation system have been caused by COVID-19. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Urban Transportation Concept and Sustainable Urban Mobility in Smart Cities: A Review

In order to create a sustainable future for the urban environment in s=Smart cities, it is necessary to develop a concept of urban transport, partially reduce the use of traditional transport, primarily cars, as well as the environmental pressure on society, which is essential to move to a sustainable urban future. In the latest discussions on the future of the urban transport system, the quality of the environment, and the possibility of its improvement are discussed, this issue became especially relevant with the onset of the pandemic, when the lockdowns were introduced. The problem of sustainable transport in urban areas has been recognized in academic studies, searching for appropriate models and solutions. The article presents the latest literature review and illustrates the newest trends with several examples. VOS Viewer software has been used to classify the different keywords, according to their co-citation, following clustering techniques. By analyzing the research conducted by other researchers, it has been possible to structure the ecosystem and trends in the Urban Transportation Concept, also mentioning likely future trends. Based on the literature analysis of the Sustainable Urban Transport, the authors of the study found that a large group of researchers deal with technical solutions and innovative business models, while the essential behavioral aspects are examined in less detail. Extensive literature analysis allowed the authors to select several solutions to achieve the transformation towards sustainable transportation in urban areas: new vehicle technologies and their environmental factors' analysis, geographic information systems, the analytic hierarchy process method, the time series analysis of road traffic accidents using multiplicative models, electrification and use of Friedman Analysis of Variance by Ranks, as well as innovations in sharing mobility.

Experiences from the international frontlines: An exploration of the perceptions of airport employees during the COVID-19 pandemic.

The aviation industry is one of the sectors that has been heavily impacted by the pandemic. While the major body of literature has focused on passenger experience and behaviour, this study focuses on airport employees instead-their experiences, perceptions, and preferences following the emergence of COVID-19. More than 1000 participants from 4 major airports-Amsterdam Airport Schiphol, Singapore Changi Airport, Taipei Taoyuan Airport, and Zurich Airport-representing over 10 different occupations, have provided a variety of sentiments about the airport as an employment ecosystem in the wake of COVID-19. Quantitatively and qualitatively surveying four different airports enabled a cross-border analysis of the results to identify interesting geographic contrasts, as well as global themes, among the responses. Regional differences regarding, the feeling of preparedness, confidence in measures, and optimism are presented. A significant difference in confidence in non-pharmaceutical measures between employees from Asian and European airports is shown. Wants and needs such as better physical/IT workplace infrastructure and more flexibility regarding job scope and hours are pointed out. The results of this research provide insights for future airport employee experience research by outlining areas to study in greater detail. Furthermore, practical implications for airport stakeholders and companies arising from the challenges experienced by the workforce are laid out to provide guidance to prepare for similar circumstances in the future and navigate the aftermath of and recovery from the pandemic.

Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review

Management of crowd information in public transportation (PT) systems is crucial, both to foster sustainable mobility, by increasing the user's comfort and satisfaction during normal operation, as well as to cope with emergency situations, such as pandemic crises, as recently experienced with coronavirus disease (COVID-19) limitations. This article presents a taxonomy and review of sensing technologies based on the Internet of Things (IoT) for real-time crowd analysis, which can be adopted in the different segments of the PT system (buses/trams/trains, railway/metro stations, and bus/tram stops). To discuss such technologies in a clear systematic perspective, we introduce a reference architecture for crowd management, which employs modern information and communication technologies (ICTs) in order to: 1) monitor and predict crowding events;2) implement crowd-aware policies for real-time and adaptive operation control in intelligent transportation systems (ITSs);and 3) inform in real time the users of the crowding status of the PT system, by means of electronic displays installed inside vehicles or at bus/tram stops/stations and/or by mobile transport applications. It is envisioned that the innovative crowd management functionalities enabled by ICT/IoT sensing technologies can be incrementally implemented as an add-on to state-of-the-art ITS platforms, which are already in use by major PT companies operating in urban areas. Moreover, it is argued that, in this new framework, additional services can be delivered to the passengers, such as online ticketing, vehicle access control and reservation in severely crowded situations, and evolved crowd-aware route planning. © 2001-2012 IEEE.

Identifying Risks in the Cruise Supply Chain: An Empirical Study in Shanghai, China

The increased frequency and severe consequences of risks in the cruise industry have attracted increasing attention from both academics and practitioners, especially after the 2012 ‘Costa Concordia' disaster and the 2020 coronavirus outbreak on the ‘Diamond Princess'. Although the literature on risk studies associated with the cruise industry and supply-chain risk management is growing, the extant literature lacks a study to view risks in the cruise industry associated with the supply chain. This paper addresses this gap by reviewing the literature on risks related to the cruise industry and general supply-chain risks to create a framework of cruise supply-chain risks. Then, semi-structured interviews were conducted to validate the identified risks and explore potential undiscovered risks. A novel risk typology of the cruise supply chain was then built based on the literature review and the empirical study. This includes macro risks, safety, security, and health risks, information risks, and supply risks. This framework can be applied for the purpose of systematically identifying the risks and their impacts on the cruise supply chain. This paper contributes to the development of a comprehensive cruise supply-chain risk classification with a detailed explanation of each risk in the cruise supply chain, which can be used by stakeholders in the cruise industry to identify and measure the impact of each risk. Additionally, this paper provides avenues for future research by scholars interested in assessing and managing cruise supply-chain risks. © National Academy of Sciences: Transportation Research Board 2022.

A Deep Spatiotemporal Fuzzy Neural Network for Subway Passenger Flow Prediction With COVID-19 Search Engine Data

Passenger flow prediction is of great significance in the operation and management of subways, especially in reducing energy consumption and improving service quality. Due to the impact of COVID-19, subway passenger flow fluctuates a lot, which makes passenger flow estimation or forecasting a very challenging task. This article mainly carries out two aspects of work to solve the task of subway passenger flow prediction under pandemic. First, this article introduces search engine data as a new data source and provides a systematic method to extract valid quires and search volumes that are closely associated with subway passenger flow under pandemic. Second, this article combines the fuzzy theory and neural network to propose a deep learning architecture called "deep spatiotemporal fuzzy neural network” to deal with the complex spatiotemporal features and uncertain external data of subway passenger flow prediction. Experiments on the actual dataset of the Beijing subway prove the superiority of the model and the effectiveness of search engine data in subway passenger flow forecasting.

AI-based traffic counting: A Case Study in Vietnam

Ho Chi Minh City, particularly Vietnamese cities in general, is so busy and crowded since tremendous numbers of motorbikes move on roads. Ho Chi Minh City leaders have encountered several challenges in fully understanding and effectively dealing with problems of urban traffic for the past few decades. Software-based solutions are proper and dramatically necessary, currently. This paper presents the deployment of an AI-based application at the Ho Chi Minh City Department of Transportation. The paper mainly concentrates on traffic counting problems during the outbreak of the Covid-19 pandemic from June 2021. The performance of the AI-based application was compared with medical declaration data and achieved an accuracy of 93.80%. © 2022 IEEE.

Mobility Pattern Analysis during 2020 Chinese National Day under the COVID-19 pandemic

With the fast development of new technologies, such as Internet of Things, big data and Internet plus, Intelligent Transportation Systems (ITS) have made remarkable achievements and the intelligence in ITS has also been continuously increased, which a new field, i.e., Social Transportation, is emerging. In social transportation systems, physical and cyber elements are tightly conjoined, coordinated, and integrated with human and social characteristics. In this paper, we collect and analyze traffic data from physical world and social media data from cyberspace to sense the human mobility patterns during holidays under the COVID-19 pandemic. © 2022 IEEE.

Supply chain delays contribute to broader challenges

Systems engineering often contributes to a business' ability to operate effectively through a crisis. However, as the global economy continued to emerge from the Covid-19 pandemic, recurring systemic failures still ravaged the global supply chain. The supply chain's fragility lingered this year, and the aerospace industry's inability to receive parts when needed may extend beyond 2023. This weakness can be attributed to many issues, including a worker shortage and failure to account for a stressed global transportation system. Systems engineering can often mitigate the longterm effects of a major disruption. However, previous failures to fully model the global supply chain illustrates this problem's complexity. The pandemic demonstrated that the aerospace industry is not immune to global disruptions, even when those disruptions do not appear to involve these products. Aided by better systems modeling, market forces worked to correct failures amplified by the pandemic. Broad and strategic investments are needed to address the long-term workforce shortage.

Guest Editorial Introduction to the Special Issue on Intelligent Transportation Systems in Epidemic Areas

The COVID-19 pandemic has posed significant challenges to transportation systems in various aspects, such as transferring patients and medical resources, enforcing physical distancing in public transportation, and controlling virus transmission through transportation networks. To address these challenges, a variety of artificial intelligence technologies, such as autonomous driving, big data analytics, intelligent vehicle routing and scheduling, and intelligent traffic control, have been employed in the design of intelligent transportation systems. This Special Issue provides a forum for researchers and practitioners to present the most recent advances in presenting and applying intelligent technologies to promote transportation systems in large-scale epidemics.

Using Data Mining to Estimate Patterns of Contagion-Risk Interactions in an Intercity Public Road Transport System

The COVID-19 pandemic has had very negative effects on public transport systems. These effects have compromised the role they should play as enablers of social equity and environmentally sustainable mobility and have caused serious economic losses for public transport operators. For this reason, in the context of pandemics, meaningful epidemiological information gathered in the specific framework of these systems is of great interest. This article presents the findings of an investigation into the risk of transmission of a respiratory infectious disease in an intercity road transport system that carries millions of passengers annually. To achieve this objective, a data mining methodology was used to generate the data required to ascertain the level of risk. Using this methodology, the occupancy of vehicle seats by passengers was simulated using two different strategies. The first is an empirical approach to the behaviour of passengers when occupying a free seat and the second attempts to minimise the risk of contagion. For each of these strategies, the interactions with risk of infection between passengers were estimated, the patterns of these interactions on the different routes of the transport system were obtained using k-means clustering technique, and the impact of the strategies was analysed.

The impact of digital transformation on supply chains through e-commerce: Literature review and a framework

One of the profound impacts of digitalization on supply chains is manifested through e -commerce. The latter has significantly grown during the last two decades, with further amplifications during the COVID-19 pandemic. This has created operational and policy making challenges for firms when deciding about how best to manage the resulting growth in e -commerce. While the impact of e-commerce on supply chains has been widely recognized in the literature, there was no effort to systematically review the literature, conceptualize some of the challenges and propose future research directions. This paper fills this gap by reviewing 153 publications from 1999 to 2019. We classify the reviewed literature based on which supply chain drivers were investigated, as well as, the employed research methodology. In addition, we conduct network and content analysis to uncover the main research themes and potential research directions namely, developing analytical centred;modelling based ecosystem for environment;leveraging data mining to enhance sustainability;balance between growth and sustainability;consumer demand and uncertainty;coordination in e-commerce logistics;last mile alternatives and cost management of innovative technique implementations. Furthermore, based on our literature review, we propose a conceptual framework where we interlink supply chain stages with a firm's business strategy, digital transformation strategy and performance.