Integration of Departure Time Choice Modeling and Dynamic Origin–Destination Demand Estimation in a Large-Scale Network

Abstract

Time-dependent origin–destination (OD) demand estimation using link traffic data in a large-scale network is a highly underdetermined problem. As a result, providing an accurate initial solution is crucial for obtaining a more reliable estimated demand. In this paper, we discuss the necessity of having a comprehensive demand profiling model that considers the spatial differences of OD pairs and we demonstrate its application in the calibration of large-scale traffic assignment models. First, we apply a departure choice model that adds a time dimension to the OD demand flows concerning their spatial differences. The time-profiled demand is then fed into the time-dependent OD demand estimation problem for further adjustment. Results show that in addition to reducing the error between simulation outputs and the observed link counts, the estimated demand profile more accurately reflects the spatial correlation of the OD pairs in the large-scale network being studied. Results provide practical insights into deployment and calibration of simulation-based dynamic traffic assignment models.

Integrated Optimization of Transit Networks with Schedule- and Frequency-Based Services Subject to the Bounded Stochastic User Equilibrium

“…Although the acquisition of OD data is beyond the scope of this study, it is acknowledged that given the penetration of smart card data and advanced methodologies for OD estimation (Hussain et al 2021), more detailed OD matrices may be available in the future. In addition, an increasing number of recent studies have assumed that the time-dependent passengers' demand data are known (Shafiei et al 2020;Yin et al 2021;Lee et al 2022). A2) A passenger boards the first arriving vehicle of the transit services on the chosen path.…”

Comparing Dynamic User Equilibrium and Noniterative Stochastic Route Choice in a Simulation-Based Dynamic Traffic Assignment Model: Practical Considerations for Large-Scale Networks

“…In this paper, we perform a mesoscopic simulation-based DTA on a subnetwork of Melbourne metropolitan area (Figure 1) covering the city center using an existing calibrated and validated model in AIMSUN [10] For more details on calibration and validation of the DTA model, please see [10,[37][38][39]. e supply and demand models were calibrated and validated.…”