Autonomous vehicles (AVs) may soon be publicly available and are expected to increase both network capacity and travel demand. Reduced safety margins from computer precision may increase network capacity and allow for more efficient intersection controls. AVs also offer the option of repositioning trips to avoid parking fees or share the vehicle between household members, which may increase the total number of vehicle trips and decrease the relative utility of transit. Since AVs may be available within one or two decades, which is within the span of long-term planning models, practitioners may soon wish to predict the effects of AVs on traffic networks. This thesis modifies the four-step planning model commonly used by practitioners to include AV behaviors and capacity improvements. Because dynamic traffic assignment (DTA) offers more realistic flow propagation and intersection control options, the four-step model is modified to incorporate DTA with endogenous departure time choices. To facilitate modeling of AV intersections, the tile-based reservation (TBR) control policy is simplified into a conflict region (CR) model compatible with general simulation-based DTA and with greatly improved computational tractability. Results suggest that although the total number of personal-vehicle trips may almost double (due to repositioning trips to the origin to avoid parking costs), increases in network and intersection capacity can mostly offset or even improve network conditions. Use of dynamic flow propagation instead of static travel time functions in the four-step model results in predictions of increased average travel speed although both static and dynamic planning models predict a high reliance on repositioning trips (i.e., empty-vehicle travel).
To study AV behaviors in DTA, this thesis first integrates DTA into the four-step model with the addition of departure time choice. This model alone may be useful for practitioners as departure time modeling is a major concern with DTA planning models. Also, the TBR intersection policy has only been studied in micro-simulation with heuristic routing strategies. The CR model opens this new technique to study under UE behavior, which is the first step for the bridge between technology demonstration simulations to models practitioners can use to evaluate implementation. . Therefore, the models developed here for the purposes of predicting AV trip and mode choices may themselves become useful tools for other applications. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/31334 |
| Date | 16 September 2015 |
| Creators | Levin, Michael William |
| Contributors | Boyles, Stephen David, 1982- |
| Source Sets | University of Texas |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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