Large-Scale Dynamic Traffic Assignment: Practical Lessons from an Application in Sydney, Australia

The calibration of dynamic traffic assignment (DTA) models involves the estimation of model parameters to best replicate real-world measurements. Good calibration is essential to estimate and predict accurately traffic states, which are crucial for traffic management applications to alleviate congestion. A widely used approach to calibrate simulation-based DTA models is the extended Kalman filter (EKF). The EKF assumes that the DTA model parameters are unconstrained, although they are in fact constrained; for instance, origin–destination (O-D) flows are nonnegative. This assumption is typically not problematic for small- and medium-scale networks in which the EKF has been successfully applied. However, in large-scale networks (which typically contain numbers of O-D pairs with small magnitudes of flow), the estimates may severely violate constraints. In consequence, simply truncating the infeasible estimates may result in the divergence of EKF, leading to extremely poor state estimations and predictions. To address this issue, a constrained EKF (CEKF) approach is presented; it imposes constraints on the posterior distribution of the state estimators to obtain the maximum a posteriori (MAP) estimates that are feasible. The MAP estimates are obtained with a heuristic followed by the coordinate descent method. The procedure determines the optimum and are computationally faster by 31.5% over coordinate descent and by 94.9% over the interior point method. Experiments on the Singapore expressway network indicated that the CEKF significantly improved model accuracy and outperformed the traditional EKF (up to 78.17%) and generalized least squares (up to 17.13%) approaches in state estimation and prediction.

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Calibration and validation of a simulation-based dynamic traffic assignment model for a large-scale congested network

Simulation Modelling Practice and Theory ◽

10.1016/j.simpat.2018.04.006 ◽

2018 ◽

Vol 86 ◽

pp. 169-186 ◽

Cited By ~ 18

Author(s):

Sajjad Shafiei ◽

Ziyuan Gu ◽

Meead Saberi

Keyword(s):

Large Scale ◽

Traffic Assignment ◽

Dynamic Traffic Assignment ◽

Dynamic Traffic ◽

Calibration And Validation ◽

Assignment Model ◽

Simulation Based

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Estimating Path Travel Costs for Heterogeneous Users on Large-Scale Networks: Heuristic Approach to Integrated Activity-Based Model–Dynamic Traffic Assignment Models

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2667-12 ◽

2017 ◽

Vol 2667 (1) ◽

pp. 119-130 ◽

Cited By ~ 1

Author(s):

Xiang (Alex) Xu ◽

Fatemeh Fakhrmoosavi ◽

Ali Zockaie ◽

Hani S. Mahmassani

Keyword(s):

Large Scale ◽

Traffic Assignment ◽

Dynamic Traffic Assignment ◽

Heuristic Approach ◽

Dynamic Traffic ◽

Travel Costs ◽

Vehicle Trajectories ◽

Optimal Paths ◽

Large Scale Networks ◽

Generalized Cost

Integrating activity-based models (ABMs) with simulation-based dynamic traffic assignment (DTA) have gained attention from transportation planning agencies seeking tools to address the arising planning challenges as well as transportation policies such as road pricing. Optimal paths with least generalized cost are needed to route travelers at the DTA level, while at the ABM level, only the least generalized cost information is needed (without fully specified paths). Thus, rerunning (executing) the least generalized cost path-finding algorithm at each iteration of ABM and DTA does not seem to be efficient, especially for large-scale networks. Furthermore, storing the dynamic travel cost skims for multiclass users as an alternative approach is not efficient either in regard to memory requirements. In this study, the aim was to estimate the least generalized cost so as to be used in destination and mode choice models at the ABM level. A heuristic approach was developed to use the simulated vehicle trajectories that were assigned to the optimal paths in the DTA level to estimate different cost measures, including distance, time, and monetary cost associated with the least generalized cost path for any given combination of the origin, destination, and departure time (ODT) and value of time. The proposed approximation method presented in this study used vehicle trajectories, aligned with the origin–destination direction and located in a specific boundary shaping an ellipse around the origin and destination zones at a certain time window, to estimate travel costs for the given ODT and user class. Numerical results for two real-world networks suggest the applicability of the method in large-scale networks in addition to its lower computational burden, including solution time and memory requirements, relative to other alternative approaches.

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