Off-campus UMass Amherst users: To download campus access theses, please use the following link to log into our proxy server with your UMass Amherst user name and password.
Non-UMass Amherst users: Please talk to your librarian about requesting this thesis through interlibrary loan.
Theses that have an embargo placed on them will not be available to anyone until the embargo expires.
Access Type
Open Access
Document Type
thesis
Degree Program
Civil Engineering
Degree Type
Master of Science in Civil Engineering (M.S.C.E.)
Year Degree Awarded
2009
Month Degree Awarded
May
Keywords
Departure Time Choice, Adaptive, Real-time Information, Arrival Reliability
Abstract
When faced with an uncertain network, travelers adjust departure time as well as route choices in response to real-time traveler information. Previous studies on algorithm design focus on adaptive route choices and cannot model adaptive departure time choices (DTC). In this thesis, the optimal adaptive departure time and route choice problem in a stochastic time-dependent network is studied. Travelers are assumed to minimize expected generalized cost which is the sum of expected travel cost and arrival delay costs. The uncertain network is modeled by jointly distributed random travel time variables for all links at all time periods. Real-time traveler information reveals realized link travel times and thus reduces uncertainties in the network.
The adaptive departure time and route choice process is conceptualized as a routing policy, 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. Waiting at origin nodes is allowed to model DTCs that are dependent on traveler information. Departure time is a random variable rather than fixed as in previous studies. A new concept of action time is introduced, which is the time-of-day when a traveler starts the DTC decision process. Because of the efforts involved in processing information and making decisions, a cost could be associated with a departure made after the action time.
An algorithm is designed to compute the minimum expected generalized cost routing policy and the corresponding optimal action time, from all origins to a destination for a given desired arrival time window. Computational tests are carried out on a hypothetical network and randomly generated networks. It is shown that adaptive DTCs lead to less expected generalized cost than fixed DTCs do. The benefit of adaptive DTC is larger when the variance of the travel time increases. The departure time distribution is more concentrated with a larger unit cost of departure delay. A wider arrival time window leads to a more dispersed departure time distribution, when there is no departure penalty.
DOI
https://doi.org/10.7275/828813
First Advisor
Song Gao