Adaptive ramp metering (ARM) is a critical component of smart freeway corridors under an active traffic management portfolio. While improving capacity through smart corridors and application of proactive traffic management solutions is less costly and easier to deploy than freeway widening, conversion to smart corridors still represents a sizable investment for a state department of transportation. Early evidence of improvements following these projects can be valuable to agencies. However, in the U.S. there have been limited evaluations, of smart corridors in general and ARM in particular, based on real operational data. This thesis explores travel time reliability measures for the eastbound (EB) Interstate 80 (I-80) corridor in the San Francisco Bay Area before and after implementation of ARM using INRIX data. These measures include buffer index, planning time, and measures from the literature that account for both skew and width of the travel time distribution. The measures are estimated for the entire corridor as well as corridor segments upstream of a bottleneck that historically have the worst measures of reliability. A new metric for measuring unreliability that may be derived from readily available INRIX data is also proposed in the thesis using data from the study corridor. While the ARM system is relatively new, the results indicate positive trends in measures of reliability even as the number of incidents on the corridor has increased in line with the national crash trends. The spatio-temporal trend evaluation framework used here may be used in the future to obtain more robust conclusions. However, since multiple smart corridor components were installed simultaneously, it may not be possible to fully isolate the effects of the ARM, or any of the other systems, individually.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-3039 |
| Date | 01 September 2017 |
| Creators | Low, Travis Charles |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
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