# Cost-Effective Train Presence Detection and Alerting Using Resource-Constrained Devices

## TL;DR

This paper introduces a low-cost, portable system for detecting approaching trains using simple sensors and microcontrollers, ensuring safety in remote railway areas.

## Contribution

A lightweight framework for train detection using resource-constrained devices and a linear regression model for real-time predictions.

## Key findings

- The system detects approaching trains 15 seconds in advance with no false negatives.
- The decision-making process completes in under 10 ms with alerts transmitted via LoRa.
- The system can operate for over 6 days on a 10 Ah battery.

## Abstract

Early train detection is vital for ensuring the safety of railway personnel, particularly in remote locations where fixed signaling infrastructure is unavailable. Unlike many existing solutions that rely on high-power, high-cost sensors and compute platforms, this work presents a lightweight, low-cost, and portable framework designed to run entirely on resource-constrained microcontrollers with just kilobytes of Random Access Memory (RAM). The proposed system uses vibration data from low-cost accelerometers and employs a simple yet effective Linear Regression (LR) model for almost real-time prediction of train arrival times. To ensure feasibility on low-end hardware, a parallel-processing framework is introduced, enabling continuous data collection, Machine Learning (ML) inference, and wireless communication with strict timing and energy constraints. The decision-making process, including data preprocessing and ML prediction, completes in under 10 ms, and alerts are transmitted via LoRa, enabling kilometer-range communication. Field tests on active railway lines confirm that the system detects approaching trains 15 s in advance with no false negatives and a small number of explainable false positives. Power characterization demonstrates that the system can operate for more than 6 days on a 10 Ah battery, with potential for months of operation using wake-on-vibration modes.