Decision making and planning of large scale infrastructures within cities is often a long process encompassing years, between multiple institutions represented by experts that require negotiations and consensus of demands and goals. The role big data plays in such design could be crucial, by providing access to otherwise elusive information on movements of people and goods in a city which can then transparently inform the design process, especially about possible demands and related complexities on the infrastructure being planned. To harness this data, it is necessary to formulate the problem technically such that data can inform experts, by articulating their expertise through the data. In this paper we present an application to analyze millions of instances of spatial data to identify potential locations for electrical road installation(s) in a city, to aid urban planners and other relevant stakeholders in planning and designing an Electrical Road System for a city. The dataset being used is gathered from a major vehicle manufacturer in Sweden, containing millions of instances of GPS data emitted by thousands of vehicles. A plan for electrified transport system is formulated by retrieving locations suitable for both static and dynamic charging installations. We investigate the technical formulation of methods and metrics for such a complex design problem, based on criteria set by experts, thus contributing to the science of big data for design of infrastructure and to methodology of data science in an institutional context.
['Vinutha Magal Shreenath', 'Sebastiaan Meijer']
Spatial big data for designing large scale infrastructure: a case-study of electrical road systems