Vehicle classification using road side sensors and feature-free data smashing approach

Kleyko, Denis; Hostettler, Roland; Lyamin, Nikita; Birk, Wolfgang; Wiklund, Urban; Osipov, Evgeny

doi:10.1109/itsc.2016.7795877

Cited by 10 publications

(4 citation statements)

References 10 publications

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“…Many researchers have developed methods for various road traffic problems. The efficacy of machine learning for vehicle classification using roadside sensors has been rigorously examined in [42,43]. The mathematical landscape of methods used for traffic flow forecasting include Hidden Markov models [44], gradient boosting regression tree [45], artificial neural networks [46], decision trees [47], support vector machines [48], Long short-term memory (LSTM) [49,50], and Bayes networks [51].…”

Section: Related Workmentioning

confidence: 99%

Public Transport Congestion Detection Using Incremental Learning

Makara¹,

Maric²,

Pekár

2022

SSRN Journal

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Section: Related Workmentioning

confidence: 99%

Public Transport Congestion Detection Using Incremental Learning

Makara¹,

Maric²,

Pekár

2022

SSRN Journal

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“…Wi-Fi channel state information [14] has also been used to provide similar non-contact traffic monitoring. Strain measurements [15] and vibration sensing [16] have been shown to provide vehicle detection and classification information.…”

Section: Introductionmentioning

confidence: 99%

Detecting Vehicle Loading Events in Bridge Rotation Data Measured with Multi-Axial Accelerometers

Ferguson

Woods

Hester

2022

Sensors

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Structural Health Monitoring (SHM) is critical in the observation and analysis of our national infrastructure of bridges. Due to the ease of measuring bridge rotation, bridge SHM using rotation measurements is becoming more popular, as even a single DC accelerometer placed at each end of span can accurately capture bridge deformations. Event detection methods for SHM typically entail additional instrumentation, such as strain gauges or continuously recording video cameras, and thus the additional cost limits their utility in resource-constrained environments and for wider deployment. Herein, we present a more cost-effective event detection method which exploits the existing bridge rotation instrumentation (tri-axial MEMS accelerometers) to also act as a trigger for subsequent stages of the SHM system and thus obviates the need for additional vehicle detection equipment. We show how the generalised variance over a short sliding window can be used to robustly discriminate individual vehicle loading events, both in time and magnitude, from raw acceleration data. Numerical simulation results examine the operation of the event detector under varying operating conditions, including vehicle types and sensor locations. The method’s application is demonstrated for two case studies involving in-service bridges experiencing live free-flow traffic. An initial implementation on a Raspberry Pi Zero 2 shows that the proposed functionality can be realised in less than 400 ARM A32 instructions with a latency of 47 microseconds.

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“…Stocker et al [ 25 ] applied the CEF C3M01 acceleration sensor produced by Webrosensor Oy in measuring vehicle-caused pavement vibration, and used the machine learning algorithm in vehicle classification and counting. Kleyko et al [ 26 ] deployed the magnetic induction sensor and acceleration sensor on the roadside to process the monitoring data using the feature-free data smashing approach, aiming to accomplish vehicle classification and traffic flow counting. Huang et al [ 27 ] adopted the wireless MEMS acceleration sensor to measure pavement vibration under moving vehicle load.…”

Section: Introductionmentioning

confidence: 99%

Real-Time and Efficient Traffic Information Acquisition via Pavement Vibration IoT Monitoring System

Yan

Wei

et al. 2021

Sensors

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Traditional road-embedded monitoring systems for traffic monitoring have the disadvantages of a short life, high energy consumption and data redundancy, resulting in insufficient durability and high cost. In order to improve the durability and efficiency of the road-embedded monitoring system, a pavement vibration monitoring system is developed based on the Internet of things (IoT). The system includes multi-acceleration sensing nodes, a gateway, and a cloud platform. The key design principles and technologies of each part of the system are proposed, which provides valuable experience for the application of IoT monitoring technology in road infrastructures. Characterized by low power consumption, distributed computing, and high extensibility properties, the pavement vibration IoT monitoring system can realize the monitoring, transmission, and analysis of pavement vibration signal, and acquires the real-time traffic information. This road-embedded system improves the intellectual capacity of road infrastructure and is conducive to the construction of a new generation of smart roads.

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Vehicle classification using road side sensors and feature-free data smashing approach

Cited by 10 publications

References 10 publications

Public Transport Congestion Detection Using Incremental Learning

Public Transport Congestion Detection Using Incremental Learning

Detecting Vehicle Loading Events in Bridge Rotation Data Measured with Multi-Axial Accelerometers

Real-Time and Efficient Traffic Information Acquisition via Pavement Vibration IoT Monitoring System

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