Evaluation of Smart Phone Weight-Mile Tax Truck Data for Supporting Freight Modeling, Performance Measures and Planning
2013
- 108Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage108
- Downloads81
- Abstract Views27
Lecture / Presentation Description
Oregon is one of the few states that currently charge a commercial truck weight-mile tax (WMT). This research serves to evaluate ancillary applications for a system developed by the Oregon Department of Transportation (ODOT) to simplify WMT collection. The data collection system developed by ODOT – Truck Road Use Electronics (TRUE) - includes a smart phone application with a Global Positioning System (GPS) device and microprocessor. The TRUE data has enormous advantages over commercial truck GPS data used in previous research due to its level of disaggregation and its potential to differentiate between vehicle and commodity types. This research evaluates the accuracy of the TRUE data and demonstrates the results of its application to develop trip generation rates for a variety of truck types and land use categories. This research also confirms the value of the TRUE data to enhance existing ODOT transportation planning models and performance measures. Further, the potential use of the data for emission estimates is evaluated. A sensitivity analysis using the U.S. Environmental Protection Agency's (EPA) Motor Vehicle Emission Simulator 2010b (MOVES2010b) is performed in order to the understand the level of error that might be encountered in emission estimates when such detailed data is not available.
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