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Journal ArticleDOI

An Efficient Multiple-Loop Sensor Configuration Applicable for Undisciplined Traffic

TLDR
The scheme proposed in this paper employs a new configuration, where all the loops are connected in series, which considerably reduces the system complexity and improves reliability, and can be used for real-time intelligent transportation system (ITS) applications under heterogeneous and less lane-disciplined conditions.
Abstract
This paper presents an effective multiple-inductive-loop pattern suitable for heterogeneous and less lane-disciplined traffic and its performance evaluation. Vehicle detection system based on conventional inductive loops works well only for lane-based and homogeneous traffic. A multiple-loop system for sensing vehicles in a heterogeneous and less lane-disciplined condition has been reported recently. The scheme proposed in this paper employs a new configuration, where all the loops are connected in series, which considerably reduces the system complexity and improves reliability. Each loop has a unique resonance frequency and the excitation source given to the loops is programmed to have frequency components covering all the loop resonance frequencies. When a vehicle goes over a loop, the corresponding inductance and resonance frequency will change. The shift in frequency or its effect in any/every loop can be simultaneously monitored, and the vehicles can be detected and identified as a bicycle, a motorcycle, a car, a bus, etc., based on the signature. Another advantage of this scheme is that the loops are in parallel resonance; hence, the power drawn from the source will be minimal. A prototype multiple-loop system has been built and tested based on the proposed scheme. The developed system detected, classified, and counted vehicles accurately. Moreover, the system also computes and provides the speed of the vehicle detected using a single set of multiple loops. The accuracy of the speed measurement has been compared with actual values and found to be accurate and can be used for real-time intelligent transportation system (ITS) applications under heterogeneous and less lane-disciplined (e.g., Indian) conditions.

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Citations
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Proceedings ArticleDOI

Collection and exploration of GPS based vehicle traces database

TL;DR: A 100 GB database of geographical coordinates gathered from Global Positioning Systems (GPS) receivers embedded in several vehicles is presented and some necessary criteria and parameters useful to improve quality of traffic management are explored.
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A study on time based association rule mining on spatial-temporal data for intelligent transportation applications

TL;DR: This paper presents an analysis on different data mining algorithms, soft and evolution computation techniques which are focused on extracting transactional and time based association rules.
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An Improved Inductive Loop Detector Design for Efficient Traffic Signal Operations and Leaner Space Requirements

TL;DR: The current study proposes a solution that uses electronic circuit modification to convert the existing serially connected loops to carry out lane-by-lane detection and proposes an improved loop design that can be used for vehicle classification and wrong way detection.
Proceedings ArticleDOI

A Feasibility Study on Upgrading the Static TLC Infrastructure to Adaptive TLC

TL;DR: The simulation results suggest that the proposed TLC algorithm can tolerate 20% error in the PCU count without degrading the performance, and demonstrates that the traffic information with the required accuracy can be processed in real time using the available platforms.
Journal ArticleDOI

Intelligent cognition of traffic loads on road bridges: From measurement to simulation – A review

TL;DR: In this paper , the state-of-the-art approaches most relevant to the traffic load cognition on road bridges, including in-site measurement and data-driven simulation, are introduced.
References
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Proceedings ArticleDOI

The PeMS algorithms for accurate, real-time estimates of g-factors and speeds from single-loop detectors

TL;DR: This study suggests that real-time speed and travel time estimates derived from single-loop detector data assuming a common g-factor for all detectors in the district can be in error by 50 percent, and so they are of little value to travelers.
Proceedings ArticleDOI

A vehicle classification based on inductive loop detectors

TL;DR: In this article, the influence of loop length (in direction of vehicle movement) on differences between characteristics describing the magnetic profiles of the vehicles belonging to the different classes is discussed, and the case of extremely short loop (10 cm) which allows detection of the number of axles is also analyzed.
Journal ArticleDOI

Use of vehicle signature analysis and lexicographic optimization for vehicle reidentification on freeways

TL;DR: This paper formulates and solves the vehicle reidentification problem as a lexicographic optimization problem with the potential to yield reliable section measures such as travel times and densities, and enables the measurement of partial dynamic origin/destination demands.
Journal ArticleDOI

Vehicle-Classification Algorithm for Single-Loop Detectors Using Neural Networks

TL;DR: A new algorithm for ILD using back-propagation neural networks is suggested to improve the vehicle-classification accuracy compared to the conventional method based on ILD.
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