Return to the VTRC Home Page
Click here to print the printer friendly version of this page.
Page Title: VTRC Report Detail

The contents of this report reflect the views of the author(s), who is responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the Virginia Department of Transportation, the Commonwealth Transportation Board, or the Federal Highway Administration. This report does not constitute a standard, specification, or regulation. Any inclusion of manufacturer names, trade names, or trademarks is for identification purposes only and is not to be considered an endorsement.


Considerations for Calculating Arterial System Performance Measures in Virginia
Ramkumar Venkatanarayana
Ramkumar Venkatanarayana
Year: 2017
VTRC No.: 17-R2
Abstract: The Moving Ahead for Progress in the 21st Century Act (MAP-21) mandates that state departments of transportation monitor and report performance measures in several areas. System performance measures on the National Highway System (NHS) are part of the final MAP-21 rule making. The NHS includes both freeways and arterials. However, in comparison to freeways, arterial system performance measures have not been studied extensively until recently. In addition, the Virginia Department of Transportation (VDOT) business plan (FY 16) aims to improve arterial travel times and safety through increased performance monitoring and management. To support all these goals, this study investigated several measures, parameters, options, and factors that impact arterial system performance measure calculations. The study network in VDOT’s Hampton Roads District included 288 directional miles of arterials with diverse attributes. The benchmark network consisted of 15 miles of roads and Bluetooth data.
Measures studied included traffic delay, planning time index, travel time index, the American Association of State Highway and Transportation Officials reliability indexes (RI80, for all days and weekdays), congested hours, and congested miles. Eleven calculation parameters were studied, namely, data quantity and quality, data filtering, spatial segmentation, weighting factors, correlation among the measures, time-of-day traffic volume profiles, truck definition, time aggregation interval, congestion reference speed, congestion threshold, and peak period definition. For each parameter, a number of value options were studied. Four geometric and traffic factors were studied, namely, annual average daily traffic volumes, speed limit, signal density, and segment lengths. Given the large number of parameters, options, and factors, and the small benchmark network, this study focused on exploratory analyses rather than statistical significance tests.
Key findings of the study include:
• Data from the National Performance Monitoring Research Data Set (NPMRDS) have less observable daily patterns and high day-to-day variability compared to Bluetooth and INRIX data. Data availability is low across all data sources during nighttime periods.
• Even after data filtering, annual network delay errors were as high as -40% (INRIX) and +155% (NPMRDS) compared to the benchmark; errors in regional planning time index and 80th percentile reliability indexes (RI80) were below 15%, and travel time index error was less than 10%. All indices were highly correlated and were robust to changes in most parameter options, often changing less than 3%.
• Volume profile methodologies and peak period definitions impact peak period vehicle miles traveled by more than 10%. Volume profiles and large spatial segments also impact delays by more than 10%. Changes in the definition of “truck,” temporal aggregation options, and small changes in spatial segmentation hardly impacted delay.
Recommendations include:
• VDOT should calculate and monitor measures to gain more experience with the data, the network measures, and their trends; nighttime data are not prime for measures.
• VDOT should study big data approaches and mobilize data storage and computational resources to analyze these large datasets.