A Variable Time-Discretization Strategies-Based, Time-Dependent Shortest Path Algorithm for Dynamic Traffic Assignment

Y. Tian, Yi-Chang Chiu

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

Within the simulation-based dynamic traffic assignment (SBDTA) model, the time-dependent shortest path (TDSP) algorithm plays a crucial role in the path-set update procedure by solving for the current optimal auxiliary solution (shortest path). Common types of TDSP algorithms require temporal discretization of link/node time/cost data, and the discretization could affect the solution quality of TDSP and of the overall SBDTA as well. This article introduces two variable time-discretization strategies applicable to TDSP algorithms. The strategies are aimed at determining the optimal time discretization for time-dependent links/nodes travel time data. The first proposed strategy produces a specific discretization interval for each link. The second proposed strategy generates time-varying intervals for the same link over the analysis period. The proposed strategies are implemented in a link-based time-dependent A* algorithm in a SBDTA model DynusT and tested with two numerical experiments on two traffic networks. The results show that the proposed discretization methods achieve the research goal-to flexibly and scalably balance the memory usage and run time for SBDTA without degrading the convergence. This property is rather important when dealing with a large real-world network with a long analysis period. © 2014

Fingerprint

Traffic Assignment
Shortest Path Algorithm
Time Discretization
Discretization
Shortest path
Simulation
Travel time
Traffic Network
Interval
Discretization Method
A* Algorithm
Travel Time
Vertex of a graph
Data storage equipment
Strategy
Time-varying
Update
Numerical Experiment
Costs
Path

Keywords

  • Link-Varying Discretization
  • Simulation-Based Dynamic Traffic Assignment
  • Time-Dependent Shortest Path
  • Time-Varying Discretization
  • Variable Time Discretization

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Automotive Engineering
  • Aerospace Engineering
  • Control and Systems Engineering
  • Applied Mathematics
  • Computer Science Applications

Cite this

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title = "A Variable Time-Discretization Strategies-Based, Time-Dependent Shortest Path Algorithm for Dynamic Traffic Assignment",
abstract = "Within the simulation-based dynamic traffic assignment (SBDTA) model, the time-dependent shortest path (TDSP) algorithm plays a crucial role in the path-set update procedure by solving for the current optimal auxiliary solution (shortest path). Common types of TDSP algorithms require temporal discretization of link/node time/cost data, and the discretization could affect the solution quality of TDSP and of the overall SBDTA as well. This article introduces two variable time-discretization strategies applicable to TDSP algorithms. The strategies are aimed at determining the optimal time discretization for time-dependent links/nodes travel time data. The first proposed strategy produces a specific discretization interval for each link. The second proposed strategy generates time-varying intervals for the same link over the analysis period. The proposed strategies are implemented in a link-based time-dependent A* algorithm in a SBDTA model DynusT and tested with two numerical experiments on two traffic networks. The results show that the proposed discretization methods achieve the research goal-to flexibly and scalably balance the memory usage and run time for SBDTA without degrading the convergence. This property is rather important when dealing with a large real-world network with a long analysis period. {\circledC} 2014",
keywords = "Link-Varying Discretization, Simulation-Based Dynamic Traffic Assignment, Time-Dependent Shortest Path, Time-Varying Discretization, Variable Time Discretization",
author = "Y. Tian and Yi-Chang Chiu",
year = "2014",
doi = "10.1080/15472450.2013.806753",
language = "English (US)",
journal = "Journal of Intelligent Transportation Systems",
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AU - Tian, Y.

AU - Chiu, Yi-Chang

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N2 - Within the simulation-based dynamic traffic assignment (SBDTA) model, the time-dependent shortest path (TDSP) algorithm plays a crucial role in the path-set update procedure by solving for the current optimal auxiliary solution (shortest path). Common types of TDSP algorithms require temporal discretization of link/node time/cost data, and the discretization could affect the solution quality of TDSP and of the overall SBDTA as well. This article introduces two variable time-discretization strategies applicable to TDSP algorithms. The strategies are aimed at determining the optimal time discretization for time-dependent links/nodes travel time data. The first proposed strategy produces a specific discretization interval for each link. The second proposed strategy generates time-varying intervals for the same link over the analysis period. The proposed strategies are implemented in a link-based time-dependent A* algorithm in a SBDTA model DynusT and tested with two numerical experiments on two traffic networks. The results show that the proposed discretization methods achieve the research goal-to flexibly and scalably balance the memory usage and run time for SBDTA without degrading the convergence. This property is rather important when dealing with a large real-world network with a long analysis period. © 2014

AB - Within the simulation-based dynamic traffic assignment (SBDTA) model, the time-dependent shortest path (TDSP) algorithm plays a crucial role in the path-set update procedure by solving for the current optimal auxiliary solution (shortest path). Common types of TDSP algorithms require temporal discretization of link/node time/cost data, and the discretization could affect the solution quality of TDSP and of the overall SBDTA as well. This article introduces two variable time-discretization strategies applicable to TDSP algorithms. The strategies are aimed at determining the optimal time discretization for time-dependent links/nodes travel time data. The first proposed strategy produces a specific discretization interval for each link. The second proposed strategy generates time-varying intervals for the same link over the analysis period. The proposed strategies are implemented in a link-based time-dependent A* algorithm in a SBDTA model DynusT and tested with two numerical experiments on two traffic networks. The results show that the proposed discretization methods achieve the research goal-to flexibly and scalably balance the memory usage and run time for SBDTA without degrading the convergence. This property is rather important when dealing with a large real-world network with a long analysis period. © 2014

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