Estimation of Origin – Destination Matrix from Traffic Counts Based On Fuzzy Logic

Abstract

Determining trip demand matrix is among the basic data in transportation planning. This matrix is derived by surveys, interviews with citizens or questionnaires that required time, money and manpower. Thus, in recent years, demand estimation methods based on network information is taken into consideration. In these methods with the information including: volume, travel time, capacity of the links and initial demand matrix it is possible to estimate the demand matrix. In this paper, we removed the additional parameters in previous studies and used a simple solution to estimate the matrix. This paper proposes a Fuzzy-PFE estimation method that allows to improve the estimation performances of PFE estimator. The objective function presented based on the reduction of travel time and travel time of routs in networks is uncertain. The method is developed by fuzzy sets theory and fuzzy programming that seems to be convenient theoretical framework to represent uncertainty in the available data. The new model is the removal of iterative process of origin -destination matrix estimation using travel time and increase convergence of the model for the large-scale and congested networks by applying little changes in the basic model. In this paper we used TRANSCAD Software to determine the shortest path in the network and optimization of objective function is performed by CPLEX.

Building and Calibration Transport Demand Model in Anamorava Region

“…In this regard, Xu and Chan (1993) were pioneers in assessing OD matrix by applying fuzzy weights [15]. A year later Rosinowski's (1994) developed an algorithm named TFlow Fuzzy (TFF) which traffic counting in the model considers imprecise value based on fuzzy sets theory functioning according to route choice proportion for each link calculating only one time and keeping constant throughout the process of calculation [16]. Reddy and Chakraborty (1998) developed a fuzzy bilevel inference applying maximum entropy in the upper level using assignment algorithm [17].…”