經高能力熟悉、高能力陌生、低能力熟悉與低能力陌生之實驗者所構建轉移矩陣，以馬可夫鏈檢定結果發現，利用動態路徑基準描述此類駕駛者轉移矩陣為佳，因此可說明在動態路徑基準構建前提下，逐點動態決策行為符合馬可夫鏈之型式。 Markovian Decision Process can be referred to a series stochastic decision with a number of states. The transition probabilities between the states are described by a Markov chain. The applications of Markov Decision Process or the related concept of Markov Chain can therefore be found in wide range of problems including these in Transportation such as Dynamic Traffic Assignment (DTA), dynamic analyses in Pavement Management System (PMS) and other problems with state-dependent nature. Of particular importance is the application of dynamic programming to obtain the optimal solution of stochastic Markovian decisions.
The node-to-node dynamic route choice behavior is of the most interest to study the individual driver’s route choices under the influence of the route guidance information where individual driver makes consecutive route switch decisions along with the traveling route. This particular issue has been successfully modeled with various forms and extensions under the notion of the “Indifference Bands” applied with Probit model specifications by Tong and his students at Tamkang University in recent years. The probability of “swithching” or “route choice” at each decision node along the route can therefore be estimated under these model specifications.
The analogy seems quite attractive to examine the so-called “node-to-node” dynamic decision to the state-to-state Markovian Decision Process. In this thesis, the “state” was defined at each decision node and the transition probabilities and the associated transition matrices were derived from the probabilities estimated from the node-to-node behavior model under three various definitions of dynamic switches at each node. A statistical test was performed to evaluate the hypothesis of first order Markov Chain.
The data bases for this thesis were compiled from two previous experiments under simulated environment using a special purpose in-vehicle guidance simulator applied to Taipei metropolitan area. The statistical tests results have confirmed that the node-to-node decision can be successfully referred to fit into a first-order Markovian Process at individual level. In addition, the study has also demonstrated the application of dynamic programming to obtain an optimal cause of routing decision for the individual driver. These results have suggested the further study to develop the dynamic route guidance strategies based on the current modeling treatments and findings. The analysis of aggregate behavior based on similar concept can be encouraged as well.
