Analysis of Interstate 405 Traffic Data and Performance Measures, and Recommendations to Improve Corridor Performance
2018-01-25
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View StatisticsCollection period
2014-01-01
2017-06-30
2017-06-30
Date completed
2017-12-22
Date updated
Time period coverage
2014-01-2017-06
Geographic coverage
I-405 (Segment from Bellevue, WA to Lynnwood, WA)
Source information
Journal Title
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Title
Analysis of Interstate 405 Traffic Data and Performance Measures, and Recommendations to Improve Corridor Performance
Published Date
2018-01-25
Authors
Author Contact
Zhang, Yufeng
zhan4879@umn.edu
zhan4879@umn.edu
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Dataset
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Other Dataset
Abstract
This study reflects findings from an independent and objective analysis of traffic data pertaining to performance of Washington’s I-405 tolled corridor. Researchers were asked to describe where the I-405 express toll lane (ETL) facility is working and where it is underperforming by analyzing the accuracy, utility, and limitations of available and applicable traffic data. Further, researchers were asked to compare findings against relevant performance measures contained in state statute. The researchers did a comprehensive study on the speed, volume and travel time on ETLs and General purpose lane (GPLs) based on the loop detector data and transaction data. The conclusions were drawn from the analysis.
This data repository hosts the final report of the study as well as all the direct data analysis results that were not able to be included in the report due to length limit, for public sharing and data reuse purpose.
Description
This repository contains one final report (.pdf file), one direct data analysis results (.rar file) where speed, volume, vehicle mile traveled (VMT), and travel times results were presented in visualized figures.
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Funding information
Washington Joint Transportation Committee
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Suggested citation
Schmit, Matt; Khani, Alireza; Zhang, Yufeng. (2018). Analysis of Interstate 405 Traffic Data and Performance Measures, and Recommendations to Improve Corridor Performance. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/D6HX1R.
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