Abstract

Collecting rich individual trip data at an individual level has long been viewed as a hard task and has become a bottleneck in modeling and calibrating travel behavior models since traditional survey methods are both costly and time-consuming. New technologies make such data a possibility and thus there is a need for frameworks that model individual behavior in real-time using such data. Such modeling will find use in a variety of real-time network optimization and prediction schemes. This dissertation describes the details of plausible behavioral modeling of this kind, and develops new data structures that are needed both for handling the network combinatorics in the analysis and in the data storage. The work is presented in the context of a new technology we propose called the Persistent Traffic Cookie (PTC) system which uses the short range wireless connection between vehicles and road side controllers to store authenticated, time-stamped node sequences on an onboard database. The dissertation makes the premise that traditional travel behavior models, including those based on disaggregate decision paradigms were developed primarily for application in aggregate level prediction and are thus not very applicable for an individual’s route choice prediction in real-time. A scheme that does not require variation of explanatory variables across the choice sets or variation in the individual’s decisions for calibration may be essential. Thus the dissertation developed models based on observed frequencies of decisions. The research also stresses the importance of path and sub-path notions in route choice decisions and provides appropriate data structures that enable modeling with such notions. Two methods that directly query the collected sequence data using efficient data structures based on the suffix tree and the suffix array schemes and node/edge transition probability model, are proposed to predict individual travels from trip diary database. A day-to-day PTC simulation framework with behavior components is proposed to generate consistent PTC data and implemented in Paramics microscopic traffic simulator. Day-to-day PTC simulations are carried out for two Paramics networks, including the Irvine Triangle network, which is a well-calibrated real world network. Various scenarios are created to test the sensitivities of the proposed prediction methods. The simulation results shows that it seems the prediction methods are robust with regard to the underlying behavior models, traffic conditions and tracking periods.