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Trajectory Data Analyses for Pedestrian Space-time Activity Study

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Vehicle-as-a-Sensor Approach for Urban Track Anomaly Detection.

Vlado Sruk¹, Siniša Fajt¹, Miljenko Krhen²

¹University of Zagreb Faculty of Electrical Engineering and Computing, Unska 3, 10000 Zagreb, Croatia.

Sensors (Basel, Switzerland)

|November 13, 2025

Summary

This study introduces a Vibration-based Track Anomaly Detection (VTAD) system using public trams as mobile sensors. It reliably detects track anomalies with meter-level accuracy, offering a cost-effective predictive maintenance solution.

Keywords:

anomaly detection predictive maintenance urban tram infrastructure vehicle-as-a-sensor vibration-based monitoring

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Area of Science:

Engineering
Transportation Science
Smart City Infrastructure

Background:

Urban tram infrastructure requires continuous monitoring for safety and efficiency.
Traditional methods using specialized diagnostic trains are often costly and inefficient.
Integrating condition monitoring into existing public transport offers a novel approach.

Purpose of the Study:

To present a Vibration-based Track Anomaly Detection (VTAD) system for real-time monitoring of urban tram infrastructure.
To utilize public transport vehicles as distributed mobile sensor platforms.
To establish a cost-effective and scalable predictive maintenance solution.

Main Methods:

Integration of low-cost micro-electro-mechanical system (MEMS) accelerometers, Global Positioning System (GPS) modules, and ESP32 microcontrollers.
Wireless data transmission using Message Queuing Telemetry Transport (MQTT) for continuous condition monitoring.
Application of a ±6σ statistical threshold to vertical vibration signals for anomaly detection.

Main Results:

Field tests demonstrated reliable detection and location of track anomalies with meter-level accuracy.
The system successfully converted public transport vehicles into effective mobile sensor platforms.
Validation through repeated measurements confirmed the system's reliability.

Conclusions:

The VTAD system provides a validated, cost-effective, and scalable predictive maintenance solution for tram infrastructure.
The system enables integration into intelligent transportation systems and smart city infrastructure.
This approach eliminates the need for dedicated diagnostic trains, optimizing resource allocation.