Deep Deconvolution for Traffic Analysis With Distributed Acoustic Sensing Data

Ende, Martijn van den; André, Fabrice; Sladen, Anthony; Richard, Cédric

doi:10.1109/tits.2022.3223084

Cited by 22 publications

(4 citation statements)

References 50 publications

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“…These signals show strong variations in both amplitude and slope that can reflect the kinematic and kinetic information of cars. Specifically, the low-frequency dipping events in the DAS data are related to the subsurface deformation induced by the weight of the car [12,20]. The strength of this quasi-static deformation is controlled by both the distance and force exerted by the point load (i.e., cars).…”

Section: Waveform Characteristics Of Processing Resultsmentioning

confidence: 99%

“…Liu et al [19] used an improved wavelet threshold and a dual-threshold algorithm to detect traffic flow. Van den Ende et al [20] used a deep-learning-based deconvolution approach to improve the temporal resolution and detection accuracy of car signals. Wiesmeyr et al [21] employed the Hough transform borrowed from the image processing field to estimate vehicle flow and the average speed of large vehicles.…”

Section: Introductionmentioning

confidence: 99%

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Urban DAS Data Processing and Its Preliminary Application to City Traffic Monitoring

Wang

Chen

Min

et al. 2022

Sensors

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Distributed acoustic sensing (DAS) is an emerging technology for recording vibration signals via the optical fibers buried in subsurface conduits. It has some apparent advantages compared to the traditional seismic geophones, i.e., it can acquire much denser spatial and temporal sampling of the seismic wavefield and can be deployed more easily. Considering that the usage of optical fibers in the urban environment has drawn relatively less attention aside from its functionality as a telecommunication cable, we propose to use its ability to record seismic signals to preliminarily monitor the traffic. Firstly, to solve the problems that DAS signals are prone to a variety of environmental noise and are generally of weak amplitude compared to noise, we propose a fast workflow for real-time DAS data processing, which can enhance the detection of regular car signals and suppress the other components. We conduct a DAS experiment in Hangzhou, China, a busy-traffic city that can provide us with a rich data library to validate our DAS data-processing workflow. At last, the well-processed data enable us to extract their slope and coherency attributes that can provide an estimate of real traffic situations. The one-minute (with video validations) and 24 h statistics of these attributes show that the speed and volume of car flow have significant correlations with them, which provides us with high confidence that urban DAS with robust data processing has great potential for city traffic monitoring with high precision and convenience. Additionally, it is also worth noting the limitation of this study. All the attributes are statistically analyzed based on the behaviors of a large number of cars, which is meaningful but lacking in precision. We will try to develop more quantitative processing and analyzing methods to provide precise information on individual cars in future works.

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Section: Waveform Characteristics Of Processing Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Urban DAS Data Processing and Its Preliminary Application to City Traffic Monitoring

Wang

Chen

Min

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…For the majority of its trajectory the DAS cable follows a major road, and hence the seismic and quasi-static signals of vehicles are abundant in the data (see van den Ende et al, 2022). The location of the cable was determined based on documentation provided by the operator, and an a-posteriori calibration procedure based on these traffic signals.…”

Section: Data Acquisitionmentioning

confidence: 99%

Distributed Acoustic Sensing for aftershock monitoring: the case of the 2019 Mw 4.9 Le Teil earthquake

van den Ende,

Ampuero,

Courboulex

et al. 2024

Preprint

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Recent developments in Distributed Acoustic Sensing (DAS) have greatly expanded our capabilities for dense geophysical instrumentation by tapping into existing (but unused) fibre-optic telecommunication networks. Leveraging these so-called "dark fibres" permits an extremely rapid deployment of thousands of vibration sensors over distances of several tens of kilometres, which is ideal for rapid postseismic response efforts. Here we report on the use of dark-fibre DAS for monitoring of the aftershock sequence of the 2019-11-11 Mw 4.9 Le Teil, France earthquake. Through comparison with the local seismometer network, we assess the capabilities of the DAS array to detect and locate small-magnitude seismic events. Likely owing to cable deployment and DAS sensing characteristics, we find that the DAS noise floor is up to 3 orders of magnitude higher than that of nearby seismometers, which greatly inhibits the detection and analysis of the low-energy events. However, locating a selected aftershock with DAS yields an accuracy and precision that is comparable to that of the seismic network, even though the DAS array has a relatively unfavourable geometry. Based on these observations we provide a number of recommendations for routinely incorporating DAS into postseismic response protocols, and for optimal use of DAS alongside conventional seismic instrumentation.

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“…Once the denoising step has been completed, a frequency analysis is performed. When a moving event approaches a given sensing point, there are two simultaneous effects taking place [43,46]. First there is a low-frequency (<3 Hz.)…”

Section: Signal Processingmentioning

confidence: 99%

Fiber Optic Acoustic Sensing to Understand and Affect the Rhythm of the Cities: Proof-of-Concept to Create Data-Driven Urban Mobility Models

et al. 2023

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In the framework of massive sensing and smart sustainable cities, this work presents an urban distributed acoustic sensing testbed in the vicinity of the School of Technology and Telecommunication Engineering of the University of Granada, Spain. After positioning the sensing technology and the state of the art of similar existing approaches, the results of the monitoring experiment are described. Details of the sensing scenario, basic types of events automatically distinguishable, initial noise removal actions and frequency and signal complexity analysis are provided. The experiment, used as a proof-of-concept, shows the enormous potential of the sensing technology to generate data-driven urban mobility models. In order to support this fact, examples of preliminary density of traffic analysis and average speed calculation for buses, cars and pedestrians in the testbed’s neighborhood are exposed, together with the accidental presence of a local earthquake. Challenges, benefits and future research directions of this sensing technology are pointed out.

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Deep Deconvolution for Traffic Analysis With Distributed Acoustic Sensing Data

Cited by 22 publications

References 50 publications

Urban DAS Data Processing and Its Preliminary Application to City Traffic Monitoring

Urban DAS Data Processing and Its Preliminary Application to City Traffic Monitoring

Distributed Acoustic Sensing for aftershock monitoring: the case of the 2019 Mw 4.9 Le Teil earthquake

Fiber Optic Acoustic Sensing to Understand and Affect the Rhythm of the Cities: Proof-of-Concept to Create Data-Driven Urban Mobility Models

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