arXiv subjects

Sandro Martinelli Reia

Publications and source records attributed to Sandro Martinelli Reia.

3 recordsLinked to original sources

arXiv2026

SF-LIFE: A Large-Scale Simulated Movement Dataset for the San Francisco Bay Area

We introduce SF-LIFE, a large-scale simulated movement dataset designed to accelerate research in transportation, mobility, and machine learning. The dataset contains 3,024,000,000,000 location records capturing complete, noise-free, multi-modality trajectories of 500,000 simulated agents observed at a 1Hz frequency navigating the San Francisco Bay Area network over a 70-day period. The data captures (1) needs-driven daily agendas of individual agents generated by an agent-based simulation of human patterns of life and (2) detailed kinematic trajectories moving agents across the OpenStreetMap representation of San Francisco using data from 40+ transit agencies across 9 counties. SF-LIFE provides unprecedented scale and detail as trajectories are based on real transit infrastructure using San Francisco General Transit Feed Specification (GTFS) data, having agent movements across multiple modalities, including bus, rail, bike, automobile, and walking. For this high-fidelity simulated representation of San Francisco, we provide (1) the full trajectory data annotated with transportation mode labels, (2) reduced-size versions of the trajectory data with reduced temporal frequency, (3) agent activity information describing the causal activity why an agent visits a place, (4) agent demographic data, and (5) the underlying OSM road network and building data. As the first dataset of its scale and level of detail, SF-LIFE overcomes the privacy, noise, and completeness limitations inherent in real-world tracking data, providing a robust and ethically sourced resource for research in transit optimization, human mobility analysis, and urban computing.

physics.soc-ph↗

arXiv2023

Supporting Post-disaster Recovery with Agent-based Modeling in Multilayer Socio-physical Networks

The examination of post-disaster recovery (PDR) in a socio-physical system enables us to elucidate the complex relationships between humans and infrastructures. Although existing studies have identified many patterns in the PDR process, they fall short of describing how individual recoveries contribute to the overall recovery of the system. To enhance the understanding of individual return behavior and the recovery of point-of-interests (POIs), we propose an agent-based model (ABM), called PostDisasterSim. We apply the model to analyze the recovery of five counties in Texas following Hurricane Harvey in 2017. Specifically, we construct a three-layer network comprising the human layer, the social infrastructure layer, and the physical infrastructure layer, using mobile phone location data and POI data. Based on prior studies and a household survey, we develop the ABM to simulate how evacuated individuals return to their homes, and social and physical infrastructures recover. By implementing the ABM, we unveil the heterogeneity in recovery dynamics in terms of agent types, housing types, household income levels, and geographical locations. Moreover, simulation results across nine scenarios quantitatively demonstrate the positive effects of social and physical infrastructure improvement plans. This study can assist disaster scientists in uncovering nuanced recovery patterns and policymakers in translating policies like resource allocation into practice.

cs.CY↗

arXiv2014

Conway's game of life is a near-critical metastable state in the multiverse of cellular automata

Conway's cellular automaton Game of LIFE has been conjectured to be a critical (or quasicritical) dynamical system. This criticality is generally seen as a continuous order-disorder transition in cellular automata (CA) rule space. LIFE's mean-field return map predicts an absorbing vacuum phase ($ρ=0$) and an active phase density, with $ρ=0.37$, which contrasts with LIFE's absorbing states in a square lattice, which have a stationary density $ρ_{2D} \approx 0.03$. Here, we study and classify mean-field maps for $6144$ outer-totalistic CA and compare them with the corresponding behavior found in the square lattice. We show that the single-site mean-field approach gives qualitative (and even quantitative) predictions for most of them. The transition region in rule space seems to correspond to a nonequilibrium discontinuous absorbing phase transition instead of a continuous order-disorder one. We claim that LIFE is a quasicritical nucleation process where vacuum phase domains invade the alive phase. Therefore, LIFE is not at the "border of chaos," but thrives on the "border of extinction."

cond-mat.stat-mech↗