Abstract
Time-dependent origin–destination (TDOD) demand is a key input of dynamic traffic assignment (DTA) in advanced traffic management systems. Model reliability is highly dependent on the accuracy of this information. One method to achieve TDOD demand matrices is to use a primary demand matrix and traffic volume counts in some links of a network. This paper proposes a bi-level model to correct the TDOD demand matrix. The extended gradient method (EGM) – an iterative method that minimizes the discrepancy between the counted and estimated traffic volumes – is a suggested means to solve this problem. The methodology is first tested on a small synthetic network to verify its performance. Then, it is applied to a real network to demonstrate its scalability. The results illustrate the effectiveness of this algorithm for the correction of TDOD demand matrices.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
