Abstract

Transport connectivity is among the most important factors in increasing firm productivity and accelerating economic development. The literature generally supports the idea of agglomeration economies, although there is little evidence of their effectiveness in Africa. There are often empirical challenges, such as spatial externalities and endogeneity of infrastructure development. Using firm registry data in Liberia, this study used the instrumental variable spatial autoregressive model to examine the effects of transport connectivity on firms' decisions on where to locate. The study found significant spatial autocorrelation and possible endogeneity related to transport infrastructure, and that firms are more likely to be located where market accessibility is better. The data indicated strong agglomeration economies, indicating that the primary city, Monrovia, is likely to continue to grow and attract more people and firms, and that secondary cities can also grow with greater transport connectivity between populated areas, such as district centers. Document type: Book

Abstract

Air pollution from vehicular traffic is a major source of health damage in urban areas. The problems of urban traffic and pollution are essentially geographic, because their incidence and impacts depend on the spatial distribution of economic activities, households, and transport links. This paper uses satellite images to investigate the spatial dynamics of vehicle traffic, air pollution, and exposure of vulnerable residents in the Dar es Salaam metro region of Tanzania. The results highlight significant impacts of seasonal weather (temperature, humidity, and wind-speed factors) on the spatial distribution and intensity of air pollution from vehicle emissions. These effects on the metro region's air quality vary highly by area. During seasons when weather factors maximize pollution, the worst exposure occurs in areas along the wind path of high-traffic roadways. The research identifies core areas where congestion reduction would yield the greatest exposure reduction for children and the elderly in poor households. Document type: Book

Abstract

Non-motorized transportation modes, especially cycling and walking, offer numerous benefits, including improvements in the livability of cities, healthy physical activity, efficient urban transportation systems, less traffic congestion, less noise pollution, clean air, less impact on climate change and decreases in the incidence of diseases related to vehicular emissions. Considering the substantial number of short-distance trips, the time consumed in traffic jams, the higher costs for parking vehicles and restrictions in central business districts, many commuters have found that non-motorized modes of transportation serve as viable and economical transport alternatives. Thus, local governments should encourage and stimulate non-motorized modes of transportation. In return, governments must provide safe conditions for these forms of transportation, and motorized vehicle users must respect and coexist with pedestrians and cyclists, which are the most vulnerable users of the transportation system. Although current trends in sustainable transport aim to encourage and stimulate non-motorized modes of transportation that are socially more efficient than motorized transportation, few to no safety policies have been implemented regarding vulnerable road users (VRU), mainly in large urban centers. Due to the spatial nature of the data used in transport-related studies, geospatial technologies provide a powerful analytical method for studying VRU safety frameworks through the use of spatial analysis. In this article, spatial analysis is used to determine the locations of regions that are characterized by a concentration of traffic accidents (black zones) involving VRU (injuries and casualties) in Sao Paulo, Brazil (developing country), and Rome, Italy (developed country). The black zones are investigated to obtain spatial patterns that can cause multiple accidents. A method based on kernel density estimation (KDE) is used to compare the two cities and show economic, social, cultural, demographic and geographic differences and/or similarities and how these factors are linked to the locations of VRU traffic accidents. Multivariate regression analyses (ordinary least squares (OLS) models and spatial regression models) are performed to investigate spatial correlations, to understand the dynamics of VRU road accidents in Sao Paulo and Rome and to detect factors (variables) that contribute to the occurrences of these events, such as the presence of trip generator hubs (TGH), the number of generated urban trips and demographic data. The adopted methodology presents satisfactory results for identifying and delimiting black spots and establishing a link between VRU traffic accident rates and TGH (hospitals, universities and retail shopping centers) and demographic and transport-related data. Document type: Article