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Source Data for Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips

Cite this dataset

Matarazzo, Thomas et al. (2022). Source Data for Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips [Dataset]. Dryad.


This data accompanies the study "Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips" published in (Nature) Communications Engineering. This paper focuses on using large and inexpsensive datasets for obtaining information on the dynamics of bridges. In this study, data is collected by smartphones in moving vehicles as the cross over a bridge, in three distinct applications. Smartphone data was collected in controlled field experiments and uncontrolled Uber rides on a long-span suspension bridge in the USA (The Golden Gate Bridge) and an analytical method was developed to accurately recover modal properties. The method was also successfully applied to partially-controlled crowdsourced data collected on a short-span highway bridge in Italy. The results suggest that larve and inexpensive datasets collected by smartphones could play a role in monitoring the health of existing transportation infrastructure.

The data provided includes the source data for the figures in the publication as well as the "controlled data" referenced in the study.


All data were recorded by an iPhone 5 and iPhone 6 using the Sensor Play App. Two-hundred and four datasets (102 per phone) were collected during vehicle trips over the Golden Gate Bridge in morning and afternoon rush-hour periods over five days (June 18 - 22, 2017). The positions and orientations of the phones were fixed during data collection. The operator manually hit "record" on each phones at the approach of the bridge and hit "stop" at the end of the bridge -- the data is not syncrhonized.

Two sedan-style vehicles were used and five target speeds were defined: 32, 40, 48, 56, and 64 km/hr (note the speed limit on the bridge is 72 km/hr). Datasets 1-50 were collected by a Nissan sedan, and datasets 51-102 were collected by a Ford sedan.

Each dataset is provided as a CSV file with twenty-four channels of raw data including timestamps, the default output for the Sensor Play App:

  • Accelerometer: X, Y, Z
  • Gyroscope: X, Y, Z
  • Attitude: Roll, Pitch, Yaw
  • Location: Longitude, Latitude, Speed, TrueHeading, Altitude
  • Motion Activity: Type & Confidence
  • Barometer: Pressure, Relative Altitude
  • Magnetometer: µT X, Y, Z, calibrated x,y,z, 

The referenced study "Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips" only analyzed vertical acceleration (acceleration in the Z-direction) and GPS lat-long channels, which were sampled at 100 Hz and 1 Hz, respectively.

Further details of the data and analysis are available in the Methods section of "Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips".

Source data is provided for figures 2, 3, 4, 5, and 7 in the Main Text of "Crowdsourcing Bridge Dynamic Monitoring with Smartphone Vehicle Trips" and figures S1, S2, and S3 of the Supplementary Material

Usage notes

XLS, XLSX, and CSV files





Cisco Systems (Canada)

Dover (United States)

Ford Motor Company (United States)

Amsterdam Institute for Advanced Metropolitan Solutions

Fraunhofer Institute for Secure Information Technology

Kuwait-MIT Center for Natural Resources and the Environment

Lab Campus


Singapore–MIT Alliance for Research and Technology

SNCF Gares & Connexions


United States Department of Defense

MIT Senseable City Lab Consortium

National Science Foundation of Sri Lanka, Award: 1351537