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The findings suggest that there are various COVID-19 implications for airlines and airports serving this market segment, ranging from the use of self-service technology, the generation of commercial/ancillary revenues and the design of surface access policies.This paper analyzes airline reactions to the COVID-19 crisis in the spring months of 2020 along the typical crisis response strategies of retrenchment, persevering, innovating, and exit. Based on the content analysis of 148 news items filtered from a daily aviation industry newsletter published during the unfolding of the crisis in Europe (from January 6 to June 2), the paper specifies and differentiates European airlines' strategic responses, outlines key implications for the post-COVID-19 competitive landscape, and raises attention points for managers and policy makers.Stakeholders are increasingly aware of the environmental and human rights issues related to highly conspicuous fashion merchandising. To mitigate the negative responses from environmentally conscious consumer groups, fashion merchandisers have sought to partner with non-governmental organizations (NGOs). While there is a growing body of literature on sustainability and social responsibility (SSR), the increasingly popular practice of fast-fashion industry partnering with NGOs has been neglected, and so far, remained under the radar. Such partnerships may be of success, but at the same time while promising on the surface, they can actually go awry, resulting in adverse outcomes for both parties. We build upon the loose-coupling theory to explain the relationships between fast-fashion multinational enterprises (MNEs) and NGOs. We discuss three causes (casual indeterminacy; fragmented external environment; discrete internal environment) and four key benefits (adaptability to environmental changes, flexibility, innovation, and firewalls for separate identity) for loosely-coupled partnerships. We then explore the dark side of such partnerships, identifying three challenges (power imbalance, mistrust and opportunism, and misaligning goals). Finally, we offer a set of propositions as a way of advancing our knowledge of partnerships in fashion merchandising industry.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.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 10 % of their actual number.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. LY2606368 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.