Estimation of path flow based on link-based traffic assignment

Abstract

Traffic assignment is a methodology to estimate the flow in the network of interest and has a great influence on traffic planning. Traffic assignment can be generally divided into the link-based algorithm and the path-based algorithm, depending on the variables used. The link-based algorithm has the advantage of easy operation and implementation, but has the disadvantage of directly considering information on the route which drivers actually use. The path-based algorithm is an effective approach from the viewpoint of traffic planning and operation because it can provide information on user’s individual path, but has a drawback that it is not easy to implement in a large-scale network due to high memory requirements. This dissertation suggests the methodology that has both 1) easy implementation and low memory (typical characteristics of link-based traffic assignment) and 2) determination of user’s individual path (those of path-based traffic assignment) by estimating path flow from optimal link-based traffic assignment in the form of an underdetermined system. Using the standard node-link database provided by the Ministry of Land, Infrastructure and Transport (MOLIT), and origin-destination data (O-D data) between the highway tollgates provided by the Korea Expressway Corporation, traffic assignments in a real-world large-scale network are conducted for the three types of O-D data: weekdays, weekends, and holidays. This dissertation predicts the service level according to the preference of users by estimating the path flow values after determining the optimal link-based traffic assignment on the Korean highway where the origin-destination data is known. This dissertation can contribute to various areas such as route expansion strategies for public transportation services and user-friendly self-driving services. In addition, it can contribute to overcoming conventional difficulties in the transportation engineering such as hypothesis verification for transportation policy.