The objective of the research is to study optimal routing policy (ORP) problems and to develop an optimal adaptive routing algorithm practical for large-scale Stochastic Time-Dependent (STD) real-life networks, where a traveler could revise the route choice based upon en route information. The routing problems studied can be viewed as counterparts of shortest path problems in deterministic networks. A routing policy is defined as a decision rule that specifies what node to take next at each decision node based on realized link travel times and the current time. The existing routing policy algorithm is for explorative purpose and can only be applied to hypothetical simplified network. In this research, important changes have been made to make it practical in a large-scale real-life network. Important changes in the new algorithm include piece-wise linear travel time representation, turn-based, label-correcting, criterion of stochastic links, and dynamic blocked links. Complete dependency perfect online information (CDPI) variant is then studied in a real-life network (Pioneer Valley, Massachusetts). Link travel times are modeled as random variables with time-dependent distributions which are obtained by running Dynamic Traffic Assignment (DTA) using data provided by Pioneer Valley Planning Commission (PVPC). A comprehensive explanation of the changes by comparing the two algorithms and an in-depth discussion of the parameters that affects the runtime of the new algorithm is given. Computational tests on the runtime changing with different parameters are then carried out and the summary of its effectiveness are presented. To further and fully understand the applicability and efficiency, this algorithm is then tested in another large-scale network, Stockholm in Sweden, and in small random networks. This research is also a good starting point to investigate strategic route choice models and strategic route choice behavior in a real-life network. The major tasks are to acquire data, generate time-adaptive routing policies, and estimate the runtime of the algorithm by changing the parameters in two large-scale real-life networks, and to test the algorithm in small random networks. The research contributes to the knowledge base of ORP problems in stochastic time-dependent (STD) networks by developing an algorithm practical for large-scale networks that considers complete time-wise and link-wise stochastic dependency.
| Identifer | oai:union.ndltd.org:UMASS/oai:scholarworks.umass.edu:theses-2047 |
| Date | 01 January 2012 |
| Creators | Ding, Jing |
| 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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