Synthetic urban agglomeration modelling to enable big data applications in transportation systems

Abstract

The paper proposes a new usage of Monte Carlo simulation in the field of transportation. The method allows to overcome problems connected to data availability in big data research, and to render the research independent of biases connected to usage of existing cities and agglomerations. Urban development trends and emerging disruptive technologies, such as autonomous vehicles, can change the urban system. Simulations will be needed to ensure that urban agglomerations develop low-carbon emission transportation systems, by simulating non-existent characteristics of smart cities. In the paper, the Monte Carlo simulation was used to simulate the numbers of residents in each group in the city agglomeration. On the basis of that and the assumptions described in the paper, the OD (origin-destination) matrix of the simulated agglomeration was made. The simulation result is presented with the PTV Visum model and the simulated origin-destination matrix heatmap. However, the model and OD matrix presented are just an example. The method allows to simulate city agglomeration of any size, depending on current research needs.