Fair spatial distribution of services has always been a challenge for urban planners to supply sufficient accessibility for individuals and to consider the needs of vulnerable social groups. Nevertheless, equity assessment literature has mostly focused on measuring the accessibility through distance-based variables, and social stratification based on just income, as well. Accordingly, this study intends to employ an accessibility measure that considers non-distance-based variables along with distance-based ones. It uses multi-criteria decision-making to propose an accessibility index to assess vertical and horizontal equity of residents’ accessibility to a range of public facilities, in the district 6 of Tehran, Iran. Therefore, using the integration of geographical information system-based spatial analysis, wider people specification, and the Gini coefficient, this measure proposes a novel framework to examine the equity level of individuals belonging to the lower, middle, and upper social classes. Results imply a 0.228 Gini index for horizontal equity, which indicates a low level of inequity among residents. Besides, for vertical equity evaluation, Gini index of 0.097, 0.249, and 0.167, respectively, for lower, middle, and upper groups show people in the lower class experience very low levels of inequity, therefore higher accessibility is well distributed among groups with higher social needs. This approach can help planners to choose the right analysis method and prioritize urban decisions toward the equitable distribution of facilities.
Although numerous studies have been conducted to discover the spatial patterns of road crashes, relatively few have focused on the patterns of road crashes suffered by socially disadvantaged groups, while simultaneously accounting for urban environmental features. This study used advanced econometric (negative binomial regression) and spatial (geographically weighted Poisson regression) approaches to capture latent geographical diversity in crash patterns. The police-reported crash data for the over-65 population in metropolitan Adelaide, Australia, were investigated for two periods: before and after COVID-19. Using both spatial and nonspatial models, the effects of land use mix, population density, road network design, distance to the central business district, and accessibility of public transit on crash frequency, and location at the neighborhood level were investigated. The findings revealed that, in addition to sociodemographic factors, the aforementioned components had nonlinear effects in varied geographical contexts. Although the number of crashes fell by 20% during the periods studied, the fundamental reasons for such incidents did not change. The results of the study could assist academics and policy makers in Australia to better understand the multidimensional implications of the built environment on the road safety of the elderly—a vulnerable group in society who were disproportionately affected by the global pandemic. The hybrid technique presented in this research has the potential to be useful in other scenarios experiencing varying crash patterns.
The safety of student pedestrians on suburban roads is an issue that is mainly overlooked. Numerous studies have been carried out on pedestrian crossing safety in developing countries. However, a study to identify factors that affect the gap acceptance behavior of student pedestrians on suburban roads is still lacking. Consequently, it is crucial to understand how factors influence pedestrians’ decisions. This motivated an investigation into student pedestrian crossing behavior at a crosswalk near a university campus located on suburban roads in Babol city, Iran. A video recording of the case study was conducted to provide a database for the analysis. Following data extraction, a logistic regression method was used to examine gap acceptance behavior based on influential variables. Results indicated that waiting for time, the mean speed of the flow, headway between vehicles, available gap size, and the type and speed of the approaching vehicle were significant. In addition, the gender and age of pedestrians did not significantly affect gap acceptance behavior. Generally, traffic-related variables were observed as significantly influencing gap selection behavior.
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