The traffic network is subject to random disruptions, such as incidents, bad weather, or other drivers’ random behavior. A traveler’s route choice behavior in such a network is thus affected by the probabilities of such disruptions, his/her attitude towards risk, and real-time information on revealed traffic conditions that could potentially reduce the level of uncertainty due to the disruptions. As the road network’s performance is de-termined collectively by all travelers’ choices, it is also affected by these factors. This thesis features the development of a multi-class user equilibrium model based on hetero-geneous risk attitude distributions and a user equilibrium model based on various disrup-tion probabilities and information penetration rates that can be used to perform sensitivity analyses for a traffic network. The method of successive average (MSA) is used to solve for the equilibrium conditions. Laboratory experimental data are used to calibrate the risk attitude model. A sample sensitivity analysis is performed to show the disruption and in-formation penetration effects on network performance. Initial calibrations show promis-ing results for route flow predictions in a congested network with respect to heterogene-ous attitude. With respect to disruption probability and information access, having too v high information penetration will not improve the network’s performance, while having a small disruption probability can improve traffic conditions in the network
| Identifer | oai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:theses-1942 |
| Date | 01 January 2012 |
| Creators | Pothering, Ryan J |
| Publisher | ScholarWorks@UMass Amherst |
| Source Sets | University of Massachusetts, Amherst |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Masters Theses 1911 - February 2014 |
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