Traffic oscillations, or simply stop-and-go waves, are a common phenomenon arising in congested traffic but still not well understood. This phenomenon causes broad adverse impacts to safety risk, fuel efficiency and greenhouse emission. To eliminate or reduce those impacts, understanding the cause and propagation mechanism is essential. This dissertation studied driving behavior in traffic oscillations with the objective to uncover the formation and propagation mechanism of traffic oscillations. This study establishes a behavioral car-following model, the Asymmetric Behavioral model, based on empirical trajectory data that is able to reproduce the spontaneous formation and ensuing propagation of traffic oscillations in congested traffic. By analyzing individual drivers' car-following behavior throughout oscillation cycles it is found that this behavior is consistent across drivers and can be captured by a simple model. The statistical analysis of the model's parameters reveals that driver' behavior during oscillation (i.e., reaction to oscillation) is strongly correlated with driver behavior before oscillations and it varies with the development stage of the oscillation. Simulation of the model shows that it is able to produce characteristics of traffic oscillations consistently with empirical observations. This study also unveils the generation mechanism of the traffic hysteresis phenomenon arising in traffic oscillations using the Asymmetric Behavioral model. It is found that the occurrence of traffic hysteresis is closely correlated with driver behavior when experiencing traffic oscillations. In the growth and fully-developed stage of traffic oscillations, drivers behave differently, which results in different distribution of hysteresis patterns. This research makes it possible to unveil new management and control strategies of traffic oscillations to improve traffic operation and to quantify the environmental and safety impacts of traffic oscillations. For example, it can be used to estimate the increase of greenhouse emission and decrease of fuel efficiency imposed by traffic oscillations. It can also be used to study the increase of accident rate.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/48975 |
| Date | 30 May 2012 |
| Creators | Chen, Danjue |
| Contributors | Laval, Jorge |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
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