Freight Traffic Assignment Methodology for Large-Scale Road–Rail Intermodal Networks

2015 ◽  
Vol 2477 (1) ◽  
pp. 50-57 ◽  
Author(s):  
M. Majbah Uddin ◽  
Nathan Huynh
Keyword(s):  
Large Scale ◽  
Traffic Assignment ◽  
Freight Traffic ◽  
Intermodal Networks

scholarly journals A Two-Phase Gradient Projection Algorithm for Solving the Combined Modal Split and Traffic Assignment Problem with Nested Logit Function

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Seungkyu Ryu ◽  
Anthony Chen ◽  
Songyot Kitthamkesorn
Keyword(s):  
Large Scale ◽  
Traffic Assignment ◽  
Transportation Network ◽  
User Equilibrium ◽  
Gradient Projection ◽  
Projection Algorithm ◽  
Nested Logit ◽  
Two Phase ◽  
Phase Gradient ◽  
Modal Split

This study provides a gradient projection (GP) algorithm to solve the combined modal split and traffic assignment (CMSTA) problem. The nested logit (NL) model is used to consider the mode correlation under the user equilibrium (UE) route choice condition. Specifically, a two-phase GP algorithm is developed to handle the hierarchical structure of the NL model in the CMSTA problem. The Seoul transportation network in Korea is adopted to demonstrate an applicability in a large-scale multimodal transportation network. The results show that the proposed GP solution algorithm outperforms the method of the successive averages (MSA) algorithm and the classical Evan’s algorithm.

Integrated simulation-based dynamic traffic and transit assignment model for large-scale network

2020 ◽  
Vol 47 (8) ◽  
pp. 898-907 ◽  
Author(s):  
Islam Kamel ◽  
Amer Shalaby ◽  
Baher Abdulhai
Keyword(s):  
Large Scale ◽  
Traffic Assignment ◽  
Dynamic Traffic ◽  
Transit Assignment ◽  
Large Scale Network ◽  
Assignment Model ◽  
Morning Peak ◽  
Simulation Based ◽  
Scale Network ◽  
Assignment Models

Although the traffic and transit assignment processes are intertwined, the interactions between them are usually ignored in practice, especially for large-scale networks. In this paper, we build a simulation-based traffic and transit assignment model that preserves the interactions between the two assignment processes for the large-scale network of the Greater Toronto Area during the morning peak. This traffic assignment model is dynamic, user-equilibrium seeking, and includes surface transit routes. It utilizes the congested travel times, determined by the dynamic traffic assignment, rather than using predefined timetables. Unlike the static transit assignment models, the proposed transit model distinguishes between different intervals within the morning peak by using the accurate demand, transit schedule, and time-based road level-of-service. The traffic and transit assignment models are calibrated against actual field observations. The resulting dynamic model is suitable for testing different demand management strategies that impose dynamic changes on multiple modes simultaneously.

scholarly journals Improved Calibration Method for Dynamic Traffic Assignment Models: Constrained Extended Kalman Filter

2017 ◽  
Vol 2667 (1) ◽  
pp. 142-153 ◽  
Author(s):  
Haizheng Zhang ◽  
Ravi Seshadri ◽  
A. Arun Prakash ◽  
Francisco C. Pereira ◽  
Constantinos Antoniou ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Large Scale ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Generalized Least Squares ◽  
Descent Method ◽  
Coordinate Descent ◽  
Model Parameters ◽  
Dynamic Traffic

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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