ABSTRACT
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.
ABSTRACT
There is rising interest in re-evaluating transport policy, moving away from analysing concrete economic indicators and towards examining the number of persons participating in activities as 'social justice.' So, using the Capacity Approach, this study presents an aggregated model to calculate the level of capability in two neighbourhoods in Tehran, Iran, involving variables from the 'Individuals' and 'Environment' dimensions. The computed factors were then weighted by the Fuzzy AHP technique based on the opinion of Iranian and international experts and combined to produce the final Capability Index (CI). CI results show that residents of the central business district, with an average of 0.628, had a 20% higher CI than residents of the city's outskirts, with 0.483. Furthermore, CI values corroborate prior research results that individuals with higher income, education level, and work position, in addition to higher individual capacity, benefit from a better neighbourhood environment. Also, calculations show that the neighbourhoods are not environmentally accessible for people with disabilities, and this group is suffering from low external capability. Additionally, the model estimates different levels of CI to evaluate the current status and prioritize future urban projects, so the model can be helpful for policy-makers to investigate the capability dimensions separately and decide about future plans.
