Performance Comparison of Automatic Vehicle Identification and Inductive Loop Traffic Detectors for Incident Detection

Khoury, Joseph; Haas, Carl T.; Mahmassani, Hani S.; Logman, Haitham; Rioux, Tom

doi:10.1061/(asce)0733-947x(2003)129:6(600)

Cited by 11 publications

(4 citation statements)

References 6 publications

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“…Thus, there are several authors who deal with various data sources or their comparisons. In addition to traditional data analyses such as (Khoury et al, 2003), today there is a lot of research dealing with floating vehicles.…”

Section: Literature Reviewmentioning

confidence: 99%

Basic characteristics of floating car data from the perspective of traffic loss during the COVID-19 pandemic

Purkrábková¹

2023

European Transport/Trasporti Europei

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Data from floating vehicles is a modern technology and can be another source of data. There is a free data source available in the Czech Republic, which is relatively new. The addressed source of data from floating vehicles covers the whole Czech Republic, which is a promising source for future use e.g. in transport planning in logistics, estimation of travel times and other related issues. For this reason, it is appropriate to examine the qualitative parameters of the data to see if they characterize the traffic stream. The present paper deals with the size of the processed data. Furthermore, the paper compares the data quality and coverage. January data for four subsequent years was used. The period of the COVID19 pandemic, when traffic declined, was included. Finally, data from selected highways are compared and the period covered is evaluated.

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Section: Literature Reviewmentioning

confidence: 99%

Basic characteristics of floating car data from the perspective of traffic loss during the COVID-19 pandemic

Purkrábková¹

2023

European Transport/Trasporti Europei

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“…Comparative algorithms are simpler than other algorithms. Many implementations rely on static thresholds, making them relatively inefficient for handling fluctuating traffic demands [ 2]. Some implementations enable managers to vary thresholds using pre-specified criteria, e.g., by time of day, but populating threshold lookup tables frequently remains an incomplete task.…”

Section: A Comparative Approachmentioning

confidence: 99%

Evaluation of Texas Incident Detection Algorithm after Years of Implementation

Hamad¹,

Omar²

2013

JTLE

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This paper summarizes a performance evaluation of the automatic incident detection algorithm deployed by many traffic management centers across the State of Texas, USA after a few years of implementation in the field. This analysis is the first step towards improving performance of incident detection which will eventually help optimizing freeway incident management practices at these TMCs. To conduct this analysis, the researchers used archived ITS data, particularly freeway incident alarms and incident scenarios data, as well as a geodatabase of ITS features. Matching alarm and scenario data enabled the determination of two performance measures: incident detection rates and false alarm rates. While many researchers evaluated the performance of their proposed incident detection algorithms, only a few assessed the actual performance of these algorithms after long period of implementation. Although the analysis described in this paper uses data from one jurisdiction (San Antonio, Texas), the methodology is sufficiently generic to enable implementation at other traffic management centers. 

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“…This capability can be achieved by using automatic incident detection (AID) algorithms, which were first introduced in the 1970s to help traffic operators learn of traffic incidents on interstates [13]. AID algorithms are evaluated primarily on two criteria including the false alarm rate (FAR) and mean time to detect (MTTD) [14]. There generally exists a tradeoff between these measures in that an AID algorithm with a low FAR will have a high MTTD (and vice versa).…”

Section: The Traffic Flow Model Used By the Cbr Traffic Controlmentioning

confidence: 99%

Development and Evaluation of a Control System for Regional Traffic Management

McLin

Scherer²

2011

Advances in Civil Engineering

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Traffic congestion is a worsening problem in metropolitan areas which will require integrated regional traffic control systems to improve traffic conditions. This paper presents a regional traffic control system which can detect incident conditions and provide integrated traffic management during nonrecurrent congestion events. The system combines advanced artificial intelligence techniques with a traffic performance model based on HCM equations. Preliminary evaluation of the control system using traffic microsimulation demonstrates that it has the potential to improve system conditions during traffic incidents. In addition, several enhancements were identified which will make the system more robust in a real traffic control setting. An assessment of the control system elements indicates that there are no substantial technical barriers in implementing this system in a large traffic network.

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Performance Comparison of Automatic Vehicle Identification and Inductive Loop Traffic Detectors for Incident Detection

Cited by 11 publications

References 6 publications

Basic characteristics of floating car data from the perspective of traffic loss during the COVID-19 pandemic

Basic characteristics of floating car data from the perspective of traffic loss during the COVID-19 pandemic

Evaluation of Texas Incident Detection Algorithm after Years of Implementation

Development and Evaluation of a Control System for Regional Traffic Management

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