A high-definition traffic performance monitoring system with the Inductive Loop Detector signature technology

Jeng, Shin-Ting; Chu, Lianyu

doi:10.1109/itsc.2014.6957957

Cited by 25 publications

(19 citation statements)

References 15 publications

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“…The characteristics of the signal such as the amplitude, phase, and frequency spectrum are varied depending on the classes of vehicles. These unique characteristics of the signal are known as the magnetic profile [50], which is used to perform vehicle classification.…”

Section: In-roadway-based Vehicle Classificationmentioning

confidence: 99%

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

Won

2020

IEEE Access

148

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A traffic monitoring system is an integral part of Intelligent Transportation Systems (ITS). It is one of the critical transportation infrastructures that transportation agencies invest a huge amount of money to collect and analyze the traffic data to better utilize the roadway systems, improve the safety of transportation, and establish future transportation plans. With recent advances in MEMS, machine learning, and wireless communication technologies, numerous innovative traffic monitoring systems have been developed. In this article, we present a review of state-of-the-art traffic monitoring systems focusing on the major functionality-vehicle classification. We organize various vehicle classification systems, examine research issues and technical challenges, and discuss hardware/software design, deployment experience, and system performance of the vehicle classification systems. Finally, we discuss a number of critical open problems and future research directions in an aim to provide valuable resources to academia, industry, and government agencies for selecting appropriate technologies for their traffic monitoring applications.

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Section: In-roadway-based Vehicle Classificationmentioning

confidence: 99%

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

Won

2020

IEEE Access

148

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“…Jeng [9] motorcycles, passenger cars, other two-axle fourtire single unit vehicles, buses, two-axle, six-tire single-unit trucks, tree-axle single-unit trucks, four or more axle single-unit trucks, four or fewer axle single-trailer trucks, five-axle single-trailer trucks, six or more axle single-trailer trucks, five or fewer axle multi-trailer trucks, six-axle multi-trailer trucks, seven or more axle multi-trailer trucks high 92.4%…”

Section: Magnetometermentioning

confidence: 99%

DeepWiTraffic: Low Cost WiFi-Based Traffic Monitoring System Using Deep Learning

Won

Sahu

Park

2019

2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)

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A traffic monitoring system (TMS) is an integral part of Intelligent Transportation Systems (ITS) for traffic analysis and planning. This paper addresses the endemic cost issue of deploying a large number of TMSs to cover huge miles of two-lane rural highways (119,247 miles in U.S.). A low-cost and portable TMS called DeepWiTraffic based on COTs WiFi devices and deep learning is proposed. DeepWiTraffic enables accurate vehicle detection and classification by exploiting the unique WiFi Channel State Information (CSI) of passing vehicles. Spatial and temporal correlations of preprocessed CSI amplitude and phase data are identified and analyzed using deep learning to classify vehicles into five different types: motorcycle, passenger vehicle, SUV, pickup truck, and large truck. A large amount of CSI data of passing vehicles and the corresponding ground truth video data are collected for about 120 hours to validate the effectiveness of DeepWiTraffic. The results show that the average detection accuracy of 99.4%, and the average classification accuracy of 91.1% (Motorcycle: 97.2%, Passenger Car: 91.1%, SUV:83.8%, Pickup Truck: 83.3%, and Large Truck: 99.7%) are achieved at a very small cost of about $1,000.

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“…Velocity of a road for a given time interval is a perspective of the traffic condition information, while the acquisition of raw data (location, real‐time speed, etc.) for the velocity information is often costly from the financial aspect, for example, using cameras , dedicated sensors , and loop detectors . Differently, vehicular crowdsensing is a popular data collection scheme, in which the data providers periodically store and incidentally offer their data rather than being specialized to provide data .…”

Section: Introductionmentioning

confidence: 99%

RTS: road topology‐based scheme for traffic condition estimation via vehicular crowdsensing

Shao

Wang

Liu

et al. 2016

Concurrency and Computation

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Urban traffic condition usually serves as basic information for some intelligent urban applications, for example, intelligent transportation system. The traditional acquisition of such information is often costly because of the dependencies on infrastructures, such as cameras and loop detectors. Crowdsensing, as a new economic paradigm, can be utilized together with vehicular networks to efficiently gather vehicle-sensed data for estimating the traffic condition. However, it has the problem of being lack of data uploading efficiency and data usage effectiveness. In this paper, we take into account the topology of the road net to deal with these problems. Specifically, we divide the road net into road sections and junction areas. Based on this division, we introduce a two-phased data collection and processing scheme named road topology-based scheme. It leverages the correlations among adjacent roads. In a junction area, data collected by vehicles are first processed and integrated by a sponsor vehicle to locally calculate traffic condition. Both the selection of the sponsor and the calculation of road condition utilize the road correlation. The sponsor then uploads the local data to a server. By employing the inherent relations among roads, the server processes data and estimates traffic condition for the road sections without vehicular data in a global vision. We conduct experiments based on real vehicle trace data. The results indicate that our design can commendably handle the problems of efficiency and effectiveness in traffic condition evaluation using the vehicular crowdsensing data

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A high-definition traffic performance monitoring system with the Inductive Loop Detector signature technology

Cited by 25 publications

References 15 publications

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

DeepWiTraffic: Low Cost WiFi-Based Traffic Monitoring System Using Deep Learning

RTS: road topology‐based scheme for traffic condition estimation via vehicular crowdsensing

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