# Leveraging Bluetooth and GPS Sensors for Route-Level Passenger Origin–Destination Flow Estimation

## TL;DR

This paper introduces a cost-effective method using Bluetooth and GPS sensors on buses to estimate passenger origin-destination flows with high accuracy.

## Contribution

A novel method using low-cost Bluetooth sensors and GPS to estimate passenger OD flows without requiring user interaction.

## Key findings

- The method achieved 91.22–96.02% accuracy in estimating boarding proportions during peak hours.
- Alighting estimation accuracy reached 95.18–95.52% with reduced mean squared error compared to historical methods.
- Fuzzy C-Means clustering effectively differentiated passenger from non-passenger devices.

## Abstract

Accurate estimation of passenger origin–destination (OD) matrices is critical for optimizing public transportation systems, yet conventional methods face challenges, such as incomplete alighting data, high infrastructure costs, and privacy concerns. With existing GPS sensors and the additional deployment of a single low-cost Bluetooth sensor (10–20 US dollars) per bus, the proposed method can derive passenger OD flow without requiring passengers to tap in or tap out. The GPS sensor updates the bus locations, and the Bluetooth sensor receives signals from surrounding devices, including those onboard devices and nearby external devices. A Fuzzy C-Means clustering algorithm was employed to differentiate passenger and non-passenger devices based on detected indicators, such as detection frequency, signal strength, vehicular mobility, etc. Validation on Shanghai’s Fengpu BRT line demonstrated 91.22–96.02% accuracy in boarding proportion estimation and 95.18–95.52% for alighting during peak hours. Compared to the historical data-based method, the proposed method achieved higher similarity to ground truth and reduced the mean squared error by 12.89–69.95%.