Developing a Dynamic Traffic Assignment Model for Large-Scale Networks: A Case Study in the City of Yazd, Iran

2021 ◽  
Author(s):  
Hamid Dehghan Banadaki ◽  
Mahmoud Saffarzadeh ◽  
Hassan Zoghi
Keyword(s):  
Large Scale ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Dynamic Traffic ◽  
Assignment Model ◽  
Large Scale Networks ◽  
The City

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

2017 ◽  
Vol 2667 (1) ◽  
pp. 119-130 ◽  
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.

scholarly journals A dynamic traffic assignment model for highly congested urban networks

2012 ◽  
Vol 24 ◽  
pp. 62-82 ◽  
Author(s):  
Moshe E. Ben-Akiva ◽  
Song Gao ◽  
Zheng Wei ◽  
Yang Wen
Keyword(s):  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Dynamic Traffic ◽  
Urban Networks ◽  
Assignment Model

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.

A dynamic traffic assignment model for a continuum transportation system

2011 ◽  
Vol 45 (2) ◽  
pp. 343-363 ◽  
Author(s):  
Yanqun Jiang ◽  
S.C. Wong ◽  
H.W. Ho ◽  
Peng Zhang ◽  
Ruxun Liu ◽  
...  
Keyword(s):  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Transportation System ◽  
Dynamic Traffic ◽  
Assignment Model

Optimality Conditions for a Dynamic Traffic Assignment Model

1978 ◽  
Vol 12 (3) ◽  
pp. 200-207 ◽  
Author(s):  
Deepak K. Merchant ◽  
George L. Nemhauser
Keyword(s):  
Optimality Conditions ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Dynamic Traffic ◽  
Assignment Model
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