Abstract:
The mobility of the rural population has become an important issue to address, which ensures proper connectivity for the rural population from their villages to market centers, urban towns, schools, and health facilities. The better connectivities can bring substantial economic and social changes among the masses that can be achieved through sustainable mobility planning. However, the sustainability of the mobility systems can only be expressed if the key indicators are identified appropriately to reflect its multiple aspects. This study proposes a novel mixed-method approach to exploring and ascertaining the socioeconomic indicators that the construction of rural roads has impacted. Rural road infrastructure often instigates direct or indirect socioeconomic impacts (SEIs) on the target population. Assessing and ascertaining SEIs pose a wide range of challenges considering their high number and qualitative and quantitative nature. Thus, a set of indicators have been identified in five different dimensions to deal with the sustainability in the mobility system for the rural population using a unique and novel way of geographical information system (GIS) and principal component analysis (PCA)-based multidimensional development framework. It proposes a mixed-method approach to identify the most significant criteria and subcriteria of the SEIs. The five dimensions of the rural development process considered in this study are transport, income, health, education, and neighborhood, which are analyzed and validated using geo-regression modeling. The applicability of this method has been demonstrated by employing a case study of rural roads. The findings of the study ascertain the status of impacted indicators to achieve sustainability. The study found relative impact/influence indicators, i.e., multidimensional development index (MDI) for various habitats of rural blocks of Jhunjhunu, Rajasthan. The eastern region of the Buhana block and region of the northern Khetri block are found to have very low and very high MDI value, respectively. The study found a strong and positive spatial correlation between rural block's proximity and the rural road network with its MDI value.