Particular Methods of Simultaneous Collection of Personal Mobility Research Data from Several Points

Abstract

Until three or four decades ago, humanity was no longer constrained by the depletion of certain natural resources (especially energy) and the intensified degradation of the natural environment. The consequences of these major crises—such as the economic, financial, and social upheaval of the world, dramatic climate change, and the entry of politics into a Brownian sarabande—necessitate the transition of civilization to another viable formula. Adequate, timely, and sustainable solutions are being sought, from the subsistence ones to those of economic efficiency. Thus, the mobility revolution is credited as one of the most important levers of change. Starting from the reality that most cars in traffic are not occupied at their maximum capacity, some ideas have already been advanced and even put into practice—“inspired”—which would improve the efficiency in this regard. For example, the reduction in the number of seats in cars and, correspondingly, the considerable reduction in the dimensions of cars are already found in the design of mini-cars and the correlation of identical interests of the self-movement of several people and the integration of this coincidence in a single concurrent mobility formula is already functional through the ride sharing system. Along with intelligent mobility management and the transition to electric and autonomous mobility, streamlining the occupancy of moving cars is seen as a great potential of the mobility revolution. Within the limits of the methodology of data collection in economic statistics, this study proposes a complex technique associated with a convincing tactic of empirical measurement of specific indices of urban traffic issues. The special method is characterized by the simultaneity and plurality of observation points followed by the correlation of the values obtained from the measurement thus performed, with the results from the video recordings taken at the same time. This method will increase the methodological accuracy of data collection in economic statistics. By advancing this method, this study aims to capture the occupancy rates of people in urban vehicle traffic, in different cities, using a combined method.