Tablet-Based Traffic Counting Application Designed to Minimize Human Error

Toth, Christopher; Suh, Wonho; Elango, Vetri; Sadana, Ramik; Guin, Angshuman; Hunter, Michael; Guensler, Randall

doi:10.3141/2339-05

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…To ensure the repeatability and quality of the data, prerecorded videos were used to perform the counts. A tablet application developed at the Georgia Institute of Technology was used by data collectors to process the resulting video (11). The data was obtained at a vehicle record level and aggregated up to 15-min bins to match the data from the pneumatic tubes.…”

Section: Video Processingmentioning

confidence: 99%

Integrating Intersection Traffic Signal Data into a Traffic Monitoring Program

Guin

Hunter

Rodgers

et al. 2016

Transportation Research Record: Journal of the Transportation R

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There are ongoing efforts to leverage the large volume of data collected to support real-time traffic operations for various non-real-time uses. However, adapting data streams to a different purpose can be challenging, especially if different applications use the data in different ways. Although traffic operations and traffic monitoring programs often use similar technology for vehicle detection, they may have different sensitivities to potential errors. Although small errors in traffic counts may not be critical in traffic operations, for which data are refreshed every few seconds, the same error levels could become much more significant in traffic monitoring, for which data, and the corresponding errors, are typically aggregated over longer periods of time. This study investigates if and how traffic volume data from detectors at signalized intersections could be appropriately used in a traffic monitoring program that supports the Highway Performance Monitoring System (HPMS). For this purpose, this study evaluates both the accuracy and representativeness of these traffic signal detector data in comparison to standard HPMS-type traffic count data obtained by portable pneumatic counters under different traffic flow conditions and intersection geometries. The results, although varying by site, indicate that these traffic signal detectors can produce 15-min aggregate traffic counts of comparable quality to results from portable pneumatic tube counters and provide 90% accuracy at a 95% level of confidence for 15-min aggregate counts. Through an examination of the representativeness of the data under different conditions, the study develops a set of eligibility criteria that can be used to identify intersections that are suitable for performance monitoring data collection.

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Section: Video Processingmentioning

confidence: 99%

Integrating Intersection Traffic Signal Data into a Traffic Monitoring Program

Guin

Hunter

Rodgers

et al. 2016

Transportation Research Record: Journal of the Transportation R

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“…Therefore, Georgia DOT's pan-tilt-zoom cameras were used to record the system boundaries (13 O-Ds) from 15:00 to 17:00 on March 7, 2012. Time series of traffic volumes were manually extracted from the videos by using the tablet-based traffic-counting application developed at the Georgia Institute of Technology (29).…”

Section: Case Studymentioning

confidence: 99%

Microsimulation-Based Framework for Freeway Travel Time Forecasting

Chilukuri

Laval

Guin

2014

Transportation Research Record: Journal of the Transportation R

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This paper presents a microsimulation-based framework for generating short-term forecasts of travel time on freeway corridors. The microsimulation model that was developed replicated freeway capacity drop and relaxation phenomena critical for modeling non–steady state conditions. This framework was evaluated offline on a real-world freeway corridor by using data from manual counts and the Georgia Department of Transportation's video detection system. The travel time forecasts were compared with ground truth travel time data; the comparison demonstrated the efficacy of this framework to produce realistic forecasts.

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“…Although manual counting from pre-recorded videos is an accurate method of traffic counting and can provide total volume and classification counts, it may be prone to error and cost extra money due to inefficient traffic count survey design, human error, etc. [6] [7] [8]. Depending on the quality of the video footage, the volume of traffic, the speed of vehicles, traffic composition, and the number of lanes, each hour of video can take up to 3 hours to count [9] [10] [11].…”

Section: Introductionmentioning

confidence: 99%

Accuracy Assessment and Guidelines for Manual Traffic Counts from Pre-Recorded Video Data

Majumder,

Wilmot

2023

JTTs

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Traffic count is the fundamental data source for transportation planning, management, design, and effectiveness evaluation. Recording traffic flow and counting from the recorded videos are increasingly used due to convenience, high accuracy, and cost-effectiveness. Manual counting from pre-recorded video footage can be prone to inconsistencies and errors, leading to inaccurate counts. Besides, there are no standard guidelines for collecting video data and conducting manual counts from the recorded videos. This paper aims to comprehensively assess the accuracy of manual counts from pre-recorded videos and introduces guidelines for efficiently collecting video data and conducting manual counts by trained individuals. The accuracy assessment of the manual counts was conducted based on repeated counts, and the guidelines were provided from the experience of conducting a traffic survey on forty strip mall access points in Baton Rouge, Louisiana, USA. The percentage of total error, classification error, and interval error were found to be 1.05 percent, 1.08 percent, and 1.29 percent, respectively. Besides, the percent root mean square errors (RMSE) were found to be 1.13 percent, 1.21 percent, and 1.48 percent, respectively. Guidelines were provided for selecting survey sites, instruments and timeframe, fieldwork, and manual counts for an efficient traffic data collection survey.

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Tablet-Based Traffic Counting Application Designed to Minimize Human Error

Cited by 8 publications

References 6 publications

Integrating Intersection Traffic Signal Data into a Traffic Monitoring Program

Integrating Intersection Traffic Signal Data into a Traffic Monitoring Program

Microsimulation-Based Framework for Freeway Travel Time Forecasting

Accuracy Assessment and Guidelines for Manual Traffic Counts from Pre-Recorded Video Data

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