The treatment of uncertainty in the dynamic origin-destination estimation problem using a fuzzy approach

• Published in: Transportation planning and technology 2015-10, Vol.38 (7), p.795-815
• Main Authors: Talebian, Ahmadreza, Shafahi, Yousef
• Format: Article
• Language: English
• Subjects: advanced traffic management systems; Demand; dynamic traffic assignment; Dynamics; Fuzzy; Fuzzy logic; Fuzzy set theory; fuzzy theory; Mathematical models; Optimization algorithms; origin-destination matrix estimation; Simulation; Traffic assignment; traffic demand; Traffic engineering; Traffic flow; Transportation planning; urban network
• ISSN: 0308-1060; 1029-0354
• DOI: 10.1080/03081060.2015.1059124

Regardless of existing types of transportation and traffic model and their applications, the essential input to these models is travel demand, which is usually described using origin-destination (OD) matrices. Due to the high cost and time required for the direct development of such matrices, they are sometimes estimated indirectly from traffic measurements recorded from the transportation network. Based on an assumed demand profile, OD estimation problems can be categorized into static or dynamic groups. Dynamic OD demand provides valuable information on the within-day fluctuation of traffic, which can be employed to analyse congestion dissipation. In addition, OD estimates are essential inputs to dynamic traffic assignment (DTA) models. This study presents a fuzzy approach to dynamic OD estimation problems. The problems are approached using a two-level model in which demand is estimated in the upper level and the lower level performs DTA via traffic simulation. Using fuzzy rules and the fuzzy C-Mean clustering approach, the proposed method treats uncertainty in historical OD demand and observed link counts. The approach employs expert knowledge to model fitted link counts and to set boundaries for the optimization problem by defining functions in the fuzzification process. The same operation is performed on the simulation outputs, and the entire process enables different types of optimization algorithm to be employed. The Box-complex method is utilized as an optimization algorithm in the implementation of the approach. Empirical case studies are performed on two networks to evaluate the validity and accuracy of the approach. The study results for a synthetic network and a real network demonstrate the robust performance of the proposed method even when using low-quality historical demand data.