The evaluation of freeway service quality is crucial work, and thus, transportation professionals have developed numerous measures including traffic volume, speed, and density. However, recent research efforts have indicated that such traditional measures may not fully reflect the quality of roadway service from the perspective of individual drivers, necessitating the development of alternative approaches that complement or replace the current service quality measures. As an alternative approach, the speed variation of a vehicle has been suggested as a promising indicator of traffic flow quality perceived by individual drivers. In particular, acceleration noise, defined by the standard deviation of the acceleration of a vehicle, has been often studied as a measure of the degree of speed variation. However, previous studies have been limited to the experimental level due to the difficulty in collecting high-resolution vehicle speed profiles for computing acceleration noise.
In this dissertation, the characteristics of speed variation, measured by acceleration noise, are investigated using the rich set of GPS data collected from the instrumented vehicles driven by the participants of the Commute Atlanta research program. The employment of the real-world vehicle activity data, composed of every second of vehicle operation, renders this research effort unique and provides an opportunity to investigate the various aspects of acceleration noise in the real-world context. The investigation is performed by relating acceleration noise to its three influential factors: traffic conditions, roadway, and driver/vehicles. In addition, a fuzzy inference system-based methodology, combining vehicle speed and acceleration noise from instrumented vehicles, is proposed as an approach to evaluating traffic flow quality.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/11562 |
| Date | 07 July 2006 |
| Creators | Ko, Joonho |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 3145097 bytes, application/pdf |
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