Fotech Solutions

Figure 1: DAS response showing multiple vehicles travelling along a fibre optic cable

Traffic monitoring with distributed acoustic sensing

Industry Mentor: Matt McDonald, Chief Technology Officer, Fotech Solutions

Problem Description

Consider the application of distributed acoustic sensing (DAS) system monitoring to a fibre optic cable deployed along an active roadway. Traffic on the roadway causes mechanical deformation of the ground which strain the fibre optic cable, causing phase shift in the backscattered light measured by the DAS system. Figure 1 shows the system response as traffic moves along the fibre optic cable.

Analysing a subset of the data in window in space-time allows individual vehicles to be recognized. Transforming this data into the time-slowness ($\tau−p$) domain using a Radon transform then admits a representation suitable for counting the number of vehicles present in the frame and identifying individual velocities using standard peak-detection methods. Figure 2 shows a subset of data with four vehicles travelling in the positive direction at different velocities and the Radon transform of the data showing four peaks isolated in the ($\tau − p$) domain, each labelled with its associated velocity.

Figure 2: (Top) Four vehicles travelling along a fibre optic cable at different speeds. (Bottom) $\tau − p$ domain representation of traffic response showing time offset and apparent slowness.
Figure 2: (Top) Four vehicles travelling along a fibre optic cable at different speeds. (Bottom) $\tau − p$ domain representation of traffic response showing time offset and apparent slowness.

The proposed problem, is to develop a detection and tracking method capable of identifying individual vehicles and reporting their position and velocity as they move along the road/fibre. Furthermore, once the position and velocity is determined, various metrics for traffic flow could be determined, allowing for prediction and optimization of traffic congestion.

Matt McDonald
Matt McDonald
Director of Artificial Intelligence and Machine Learning

I’m an applied mathematician working somewhere in the gray area between research, development and application of novel sensor technologies, signal processing, data analysis and interpretation. I pride myself in understanding all levels of a system and being able to explain any of it to anyone. I’m equally comfortable buried in code, in the field or presenting tricky results at a high level.

Parimala Thulasiraman
Parimala Thulasiraman
Professor of Computer Science
Boya Peng
Boya Peng
Graduate student
Brian Chan
Brian Chan
Math PhD, graduated
Emily Rose Korfanty
Emily Rose Korfanty
MSc Student in Mathematics
Jaeun Park
Jaeun Park
Mathematics Ph.D student
Jianou Zhang
Jianou Zhang
graduate student/Fotech Solutions Project member
Sarah Nataj
Sarah Nataj
Research Assistance, Sessional instructor