Road crashes in Adelaide metropolitan region, the consequences of COVID-19.

Background: Many countries instituted lockdown rules as the COVID-19 pandemic progressed, however, the effects of COVID-19 on transportation safety vary widely across countries and regions. In several situations, it has been shown that although the COVID-19 closure has decreased average traffic flow, it has also led to an increase in speeding, which will indeed increase the severity of crashes and the number of fatalities and serious injuries. Methods: At the local level, Generalized linear Mixed (GLM) modelling is used to look at how often road crashes changed in the Adelaide metropolitan area before and after the COVID-19 pandemic. The Geographically Weighted Generalized Linear Model (GWGLM) is also used to explore how the association between the number of crashes and the factors that explain them varies across census blocks. Using both no-spatial and spatial models, the effects of urban structure elements like land use mix, road network design, distance to CBD, and proximity to public transit on the frequency of crashes at the local level were studied. Results: This research showed that lockdown orders led to a mild reduction (approximately 7%) in crash frequency. However, this decrease, which has occurred mostly during the first three months of the lockdown, has not systematically alleviated traffic safety risks in the Greater Adelaide Metropolitan Area. Crash hotspots shifted from areas adjacent to workplaces and education centres to green spaces and city fringes, while crash incidence periods switched from weekdays to weekends and winter to summer. Implications: The outcomes of this research provided insights into the impact of shifting driving behaviour on safety during disorderly catastrophes such as COVID-19.

Using gravity model to make store closing decisions: A data driven approach.

Many studies propose methods for finding the best location for new stores and facilities, but few studies address the store closing problem. As a result of the recent COVID-19 pandemic, many companies have been facing financial issues. In this situation, one of the most common solutions to prevent loss is to downsize by closing one or more chain stores. Such decisions are usually made based on single-store performance; therefore, the under-performing stores are subject to closures. This study first proposes a multiplicative variation of the well-known Huff gravity model and introduces a new attractiveness factor to the model. Then a forward-backward approach is used to train the model and predict customer response and revenue loss after the hypothetical closure of a particular store from a chain. In this research the department stores in New York City are studied using large-scale spatial, mobility, and spending datasets. The case study results suggest that the stores recommended being closed under the proposed model may not always match the single store performance, and emphasizes the fact that the performance of a chain is a result of interaction among the stores rather than a simple sum of their performance considered as isolated and independent units. The proposed approach provides managers and decision-makers with new insights into store closing decisions and will likely reduce revenue loss due to store closures.