Estimating Urban Travel Intensity from Ambient Seismic Signals via a Hybrid CatBoost–LSTM Framework
Year: 2026
Type: article
Abstract: Urban travel intensity is a practical proxy for human mobility, but direct mobility data are often costly, geographically restricted, and privacy sensitive. UTScan uses continuous ambient seismic data to estimate urban travel intensity in a passive, non-intrusive manner. Model development used 10 cities in Hubei Province during January–April 2020, and external validation used 84 non-Hubei cities that satisfied the study’s data-quality criteria. From each hourly power spectral density (PSD) curve, we extracted 13 features in the 2–20 Hz anthropogenic band, applied a station-wise low-activity baseline subtraction, and then modeled daily travel intensity with a CatBoost–LSTM framework. Under the calendar-based forward-validation protocol, the final UTScan implementation (FusionB) achieved a mean RMSE of 0.537 ± 0.214 and a mean Pearson correlation of 0.768 ± 0.076 across the internal Hubei folds and a mean RMSE of 0.789 ± 0.229 and a mean Pearson correlation of 0.605 ± 0.370 across the 84-city external validation set. Additional sensitivity analyses using alternative validation windows and light-touch outlier handling indicated that the main conclusions were stable, while single-station representativeness remained the principal limitation. Ambient seismic noise is therefore a useful passive proxy for estimating city-scale mobility dynamics, especially for abrupt mobility disruptions, but its interpretation remains conditional on station siting, source mixture, and the proxy nature of the Baidu travel-intensity target.
Source: Applied Sciences
DOI: 10.3390/app16073407
Authors: Kai Guo, Jianmin Hou
Language: English
Topic: Seismic Waves and Analysis
Subfield: Geophysics
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: G5322767877
OpenAlex ID: W7147666347