THE ESTIMATION OF FREEWAY ORIGIN-DESTINATION DEMAND USING REAL-TIME TRAFFIC DATA OF FREEWAY TRAFFIC MANAGEMENT SYSTEM (FTMS)

Abstract

The goal of this paper is to develop the freeway Origin-Destination (OD) demand estimation model using real-time traffic data collected from Freeway Traffic Management System (FTMS). Although it is necessary for the dynamic OD demand between on and off- ramps to perform more effective traffic management strategies, Automated Vehicle Identification (AVI) systems are unable to assist to collect OD demand due to the limitation of construction and maintenance costs. The existing models use the simulation model to get a link distribution ratio of dynamic traffic flow by time process. It is difficult to load at FTMS and estimate a dynamic OD between on and off-ramps. The formulation of methodology proposed in this paper includes traffic flow techniques and dynamic OD demand estimation techniques using a real-time detector data. The proposed methodology is evaluated by using the real-time data of NAEBU Ring Road, a circulatory freeway system in Seoul, South Korea. Recently, Freeway Traffic Management System (FTMS) has been extended to most of urban freeway sections and puts it to several desirable means dealing with congestion management in Seoul. It is necessary for the dynamic OD demand between on and off-ramps to build the more effective traffic management strategies. To collect the OD demand, Automated Vehicle Identification (AVI) should be equipped at the entire on and off-ramps. Nonetheless AVI systems have been partially installed because of the limitation of construction and maintenance costs. The detectors are constructed at the whole section of mainline as an identical gap (usually 500m) and all the on and off-ramps at NAEBU Ring Road in Seoul. Traffic Management Center (TMC) has collected a useful real-time data, such as traffic volumes, travel speed and occupancy. Meanwhile, the current consideration involved with the estimation of freeway dynamic OD matrices have been taken increasing attention through the applicability to on-line traffic management systems. The existing simple dynamic OD estimation model is mostly formulated in order to minimize the gap between observed and predicted link traffic volumes. It comprises two parts of algorithms - traffic flow theory and optimal solutions algorithm. Regardless of sophisticated techniques for the solutions, most of the models have been dealt with a few types of link distribution proportion of trips on time process by using micro simulation models. The link distribution proportion means the proportion of trips located at every link on time process and is used for calculating the estimated link traffic volumes by a specific period of time.