scholarly journals Extracting Densely Covered Areas Within Floating Car Datasets Using Inductive Loop Detector Data

2020 ◽  
Author(s):  
Christian Röger ◽  
Maja Kalinic ◽  
Jukka M. Krisp
Keyword(s):  
Count Data ◽  
Correct Choice ◽  
High Ratio ◽  
Inductive Loop ◽  
Floating Car Data ◽  
Loop Detectors ◽  
Loop Detector ◽  
Traffic Count ◽  
Inductive Loop Detector

AbstractWe present an approach to use static traffic count data to find relatively representative areas within Floating Car Data (FCD) datasets. We perform a case study within the state of Nordrhein-Westfalen, Germany using enviroCar FCD and traffic count data obtained from Inductive Loop Detectors (ILD). Findings indicate that our approach combining FCD and traffic count data is capable of assessing suitable subsets within FCD datasets that contain a relatively high ratio of FCD records and ILD readings. We face challenges concerning the correct choice of traffic count data, counting individual FCD trajectories and defining a threshold by which an area can be considered as representative.

scholarly journals The Impact of Detector Placement on Signalized Intersection Performance

2012 ◽  
Vol 21 (6) ◽  
pp. 413-423
Author(s):  
Daniela Koltovska Nečoska ◽  
Kristi M. Bombol
Keyword(s):  
Traffic Management ◽  
Signalized Intersection ◽  
Signal Control ◽  
Inductive Loop ◽  
Loop Detector ◽  
Inductive Loop Detector ◽  
Actuated Signal ◽  
Actuated Signal Control ◽  
Detector Placement ◽  
Stop Line

Despite the flow fluctuations and increased traffic demand in the Macedonian cities over the last fifteen years, the Republic of Macedonia is one of those countries which still employ only the traditional systems of traffic management and control. Those are fixed control systems that certainly cause problems such as increased travel times and travel expenses as well as environmental degradation. A general call for “…something has to be done…” becomes obvious. The best practices have shown that this can be realized through unconventional solutions i.e. by means of responsive traffic management. A very reasonable example of such a system is the vehicle actuated control system that we have found to be quite challenging to do our research. Thus, we set up two folded research issues in front of us. The first one was to scientifically prove that vehicle actuated signal control can really be a reasonable substitute for a fixed time signal control, which will enhance the overall signalized intersection performance provided the timing parameters and the detector placement ŕrĺ properly designed. The second one was to indicate that such an advanced control system is feasible and sustainable for Macedonian cities. This paper focuses on the first research issue only. For this purpose, a semi-actuated signal control strategy on an appropriately chosen signalized intersection was designed. The primary objective was to determine the way in which the inductive loop detector placement from the STOP line affects the overall intersection performance. To meet the goal, two scenarios were designed: 1. Detector placement at the STOP line, and 2. Detector placement at 8 metres behind the STOP line. Emphasis was placed on the semi-actuated signal control algorithm design. The designed algorithm was then applied in the net of VISSIM in order to simulate the semi-actuated signal control process. Performance comparison analysis with the formerly pre-timed signal control strategy followed. It was concluded that the overall intersection performance could be improved both by adequate inductive loop detector placement and by interaction with signal parameters. Hence, the placement distances would have to be considered under the limitation conditions only. KEYWORDS: signalized intersection, vehicle actuated control, semi-actuated control, inductive loop detectors, simulation, delays, level of service

An Improved Inductive Loop Detector Design for Efficient Traffic Signal Operations and Leaner Space Requirements

2018 ◽  
Vol 2672 (18) ◽  
pp. 143-153 ◽  
Author(s):  
Goli Koti Veera Yogesh ◽  
Anuj Sharma ◽  
Lelitha Vanajakshi
Keyword(s):  
Machine Learning Algorithms ◽  
Lane Detection ◽  
Vehicle Classification ◽  
Inductive Loop ◽  
Traffic Sensors ◽  
Loop Detectors ◽  
Loop Design ◽  
In Series ◽  
Inductive Loop Detector ◽  
Space Requirements

Inductive loop detectors (ILDs) are one of the most widely deployed traffic sensors. At present, for lane-by-lane detection, ILDs require separate connecting cables for each loop (each lane) and separate data acquisition systems or detector channels to process them. This becomes problematic with limited conduit and cabinet space. In most cases, transportation agencies use ILDs connected in series to avoid these constraints, in which case the lane-by-lane information is lost. However, research has shown that lane-by-lane detection can lead to safer and more efficient operations at signalized intersections. In order to ease the application of lane-by-lane detection, 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. This system achieved 100% accuracy of lane-by-lane detection in test runs. The paper also proposes an improved loop design, for future installations, that can be used for vehicle classification and wrong way detection. The study implemented machine-learning algorithms for vehicle classification and direction determination with an accuracy of 99.6% and 78.57%, respectively, using single loop configuration.

A New Approach to Estimate Vehicle Emissions Using Inductive Loop Detector Data

2012 ◽  
Vol 17 (3) ◽  
pp. 179-190 ◽  
Author(s):  
Shin-Ting (Cindy) Jeng ◽  
K. S. Nesamani ◽  
Stephen G. Ritchie
Keyword(s):  
Vehicle Emissions ◽  
New Approach ◽  
Inductive Loop ◽  
Loop Detector ◽  
Inductive Loop Detector

Individual Truck Speed Estimation from Advanced Single Inductive Loops

2019 ◽  
Vol 2673 (5) ◽  
pp. 272-284
Author(s):  
Yiqiao Li ◽  
Andre Y. C. Tok ◽  
Stephen G. Ritchie
Keyword(s):  
Essential Element ◽  
Feature Weighting ◽  
Speed Estimation ◽  
Vehicle Speed ◽  
Estimation Model ◽  
Inductive Loop ◽  
Traffic Conditions ◽  
Loop Detector ◽  
Inductive Loop Detector ◽  
The Individual

Trucks are an essential element in freight movements, transporting 73% of freight tonnage among all modes. However, they are also associated with severe adverse impacts on roadway congestion, safety, and air pollution. Truck speed by truck body types has been considered as an indicator of traffic conditions and roadway emissions. Even though vehicle speed estimation has been researched for decades, there exists a gap in estimating truck speeds particularly at the individual vehicle level. The wide diversity of vehicle lengths associated with trucks makes it especially challenging to estimate truck speeds from conventional inductive loop detector data. This paper presents a new speed estimation model which uses detailed vehicle signature data from single inductive loop sensors equipped with advanced detectors to provide accurate truck speed estimates. This model uses new inductive signature features that show a strong correlation with truck speed. A modified feature weighting K-means algorithm was used to cluster vehicle length related features into 16 specific groups. Individual vehicle speed regression models were then developed within each cluster. Finally, a multi-layer perceptron neural network model was used to assign single loop signatures to the pre-determined speed related clusters. The new model delivered promising estimation results on both a truck-focused dataset and a general traffic dataset.

On the estimation of space-mean-speed from inductive loop detector data

2010 ◽  
Vol 33 (1) ◽  
pp. 91-104 ◽  
Author(s):  
Jiang Han ◽  
John W. Polak ◽  
Javier Barria ◽  
Rajesh Krishnan
Keyword(s):  
Inductive Loop ◽  
Loop Detector ◽  
Inductive Loop Detector

Robust Blind Deconvolution Process for Vehicle Reidentification by an Inductive Loop Detector

2014 ◽  
Vol 14 (12) ◽  
pp. 4315-4322 ◽  
Author(s):  
David Guilbert ◽  
Sio-Song Ieng ◽  
Cedric Le Bastard ◽  
Yide Wang
Keyword(s):  
Blind Deconvolution ◽  
Inductive Loop ◽  
Loop Detector ◽  
Vehicle Reidentification ◽  
Inductive Loop Detector
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