Mobile Automation and Sensing Systems

IMU-based traffic flow monitoring

Bluetooth and WiFi readers are commonly used for estimating travel time along arterial corridors. Unlike competing traffic sensor technologies, Bluetooth and WiFi readers are inexpensive to purchase and install, and require no calibration. Despite these advantages, processing the data generated by Bluetooth and WiFi readers is difficult, since the travel time measurement is affected by the detection range of the readers. This range of detection causes random errors in vehicle position, and make it difficult to monitor travel time on short scales. To minimize the relative error in travel time estimation, one needs to estimate travel time between points that are far apart, which in turn reduces the likelihood of vehicle reidentification, and make it more difficult to understand how the congestion is distributed between the two sensing locations.

To address this problem, we propose to augment some vehicles with inertial sensors that communicate over Bluetooth or WiFi technologies. Such sensors monitor the acceleration and rotation rates of the vehicles along their path. The acceleration patterns allow one to better understand the congestion patterns between the two Bluetooth or WiFi readers. The acceleration information can then be transmitted to the readers by dynamically modifying the device name or the device MAC address to encode the acceleration patterns. This allows information to be transmitted to the reader infrastructure without having to physically pair the device with all readers.


MASS is broadly focusing on Transportation Engineering and Environmental Monitoring.


Mobile Automation and
Sensing Systems

The University at Texas
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Austin, Tx-78712

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