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.

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 Truck Activity Monitoring System for Freight Transportation Analysis

2017 ◽  
Vol 2610 (1) ◽  
pp. 97-107 ◽  
Author(s):  
Andre Tok ◽  
Kyung (Kate) Hyun ◽  
Sarah Hernandez ◽  
Kyungsoo Jeong ◽  
Yue (Ethan) Sun ◽  
...  
Keyword(s):  
Monitoring System ◽  
Visual Analytics ◽  
Freight Transportation ◽  
Activity Monitoring ◽  
Classification Models ◽  
Activity Data ◽  
Traffic Sensors ◽  
Loop Detector ◽  
Freight Planning ◽  
Inductive Loop Detector

Understanding truck activity is an essential component of strategic freight planning and programming. However, recent studies have revealed a significant void in the availability of detailed truck activity data. Although some existing detectors are capable of providing truck counts by axle configuration, higher-resolution data that indicate truck body configuration, industry served, and commodity carried cannot be obtained from existing sensors. This paper presents the newly developed Truck Activity Monitoring System, which leverages existing in-pavement traffic sensors to provide truck activity data in California. Existing inductive loop detector sites were updated with inductive signature technology and advanced truck classification models were implemented to provide detailed truck count data with more than 40 truck body configurations. The system has been deployed to more than 90 detector locations in California to provide coverage at state borders, regional cordons, and significant metropolitan truck corridors. An interactive geographic information system website provides users with advanced visual analytics and access to archived data across all deployed locations. The case studies presented in this paper demonstrate the potential of the data obtained from this system in analyzing and understanding current and historical industry-specific truck activity.

Traffic Management Parameters from Single Inductive Loop Detectors

2000 ◽  
Vol 1719 (1) ◽  
pp. 112-120 ◽  
Author(s):  
Tom Cherrett ◽  
Hugh Bell ◽  
Mike McDonald
Keyword(s):  
Traffic Control ◽  
Traffic Management ◽  
Fixed Time ◽  
Urban Traffic ◽  
Flow Conditions ◽  
Inductive Loop ◽  
Loop Detectors ◽  
Time Period ◽  
S Period ◽  
Urban Traffic Control

Investigated are potential new uses for the digital output produced by single inductive loop detectors (2 m x 1.5 m and 2 m x 6.5 m) used in most European urban traffic control systems. Over a fixed time period, the average loop-occupancy time per vehicle (ALOTPV) for a detector being sampled every 250 ms is determined by taking the number of 250-ms occupancies and dividing by the number of vehicles. In a similar way, the average headway time between vehicles (AHTBV) is determined by taking the number of 250-ms vacancies and dividing by the number of vehicles. Over a 30-s period, the minimum and maximum values of ALOTPV and AHTBV ranged from 1 to 120 (an ALOTPV of 1 and an AHTBV of 120 representing free-flow conditions, an ALOTPV of 120 and an AHTBV of 1 representing a stationary queue). Identifying periods when a link was operating under capacity and at capacity and when it had become saturated could be more clearly identified by using plots of ALOTPV and AHTBV data over time compared to the more traditional percentage occupancy output. ALOTPV also was used to successfully identify long vehicles from cars down to speeds of 15 km/h.

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