A Framework for Generating Synthetic Urban Mobility Datasets With Customizable Anomalous Scenarios

The development of advanced data-driven Intelligent Transportation Systems (ITS) strongly relies on the availability of representative mobility datasets. While several datasets are publicly available, practically none explicitly represent anomalous mobility scenarios such as strikes, road closures, or sudden spikes in mobility demand due to special events, also due to the lack of standardized annotations for anomalies. Moreover, existing datasets often do not include fine-grained mobility traces due to privacy concerns, and generally do not fully capture the actual variability of real-world conditions. This poses a significant challenge for ITS researchers and practitioners, requiring accurate, annotated data to model, simulate, and analyze the effects of disruptive events on urban mobility. To address these gaps, in this paper, we present a solution for automatically generating synthetic urban mobility datasets including various anomalous scenarios. Built on top of the well-known SUMO framework, our solution is designed to apply to any urban road network, as it leverages open data sources to create detailed, scenario-specific datasets. The tool features a Graphical User Interface, empowering users, including non-technical staff such as urban planners and decision-makers, to easily generate realistic datasets, including fully customizable anomalous scenarios. We show the effectiveness of our proposal by conducting a case study based on the city of Genoa, Italy, leveraging publicly available data provided by the city’s Municipality. In the case study, we show how the solution can be employed to easily generate detailed mobility datasets involving different anomalous scenarios, and how the resulting datasets can be used to perform different fine-grained mobility analyses. Additionally, we assess the realism and consistency of the generated data by validating the internal plausibility and coherence of the synthetic mobility flows, including verification that spatio-temporal patterns align with widely accepted urban mobility principles. By democratizing access to high-quality, annotated mobility data for anomalous conditions, we envision that our tool could significantly contribute to the field of urban mobility research and practice.