Efficient Calculation of Microscopic Travel Demand Data with Low Calibration Effort

Determining travel demand within a region of interest takes a considerable calibration effort, requiring transportation surveys, traffic counts, and empirical trip volumes. However, there is a need for demand calculation without substantial calibration, for example to generate large-scale benchmark data for evaluating transportation algorithms. In this work, we present several approaches for demand calculation that take as input only publicly available data, such as population and POI densities. Our algorithms build upon the recently proposed radiation model, which is inspired by job search models in economics. We show that a straightforward implementation of the radiation model does not scale to continental road networks, taking months even on a modern 16-core server. Therefore, we introduce more scalable implementations, substantially decreasing the running time by five orders of magnitude from months to seconds. An extensive experimental evaluation shows that the output of our algorithms is in accordance with demand data used in production systems. Compared to simple approaches previously used in algorithmic publications to generate benchmark data, our algorithms output demand data of better quality, take less time, and have similar implementation complexity.