scholarly journals A Multiclass, Multimodal Dynamic Traffic Assignment Model with Departure Time

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Meng Meng ◽  
Chunfu Shao ◽  
Yiik Diew Wong ◽  
Jie Zhang
Keyword(s):  
Traffic Control ◽  
Dynamic Traffic Assignment ◽  
Route Guidance ◽  
Traffic Equilibrium ◽  
Dynamic Traffic ◽  
Schedule Delay ◽  
Assignment Model ◽  
Departure Time ◽  
Departure Time Choice ◽  
Dynamic Route

The paper develops a multiclass, multimodal dynamic traffic equilibrium model with consideration of the departure time choice problem. Travelers choose the departure time and the route simultaneously with a Logit-based structure. The route travel cost is a summation of travel time and schedule delay which is associated with arrival time at destination. In addition, the travelers are classified into three groups according to their value of time. A variational inequality (VI) formulation is proposed based on the equilibrium conditions. Two examples are given to testify the effectiveness of the model and the solution algorithm. The model can give the optimal travel route as well as the best departure time, which would contribute to traffic control and dynamic route guidance.

A demand model with departure time choice for within-day dynamic traffic assignment

2006 ◽  
Vol 175 (3) ◽  
pp. 1557-1576 ◽  
Author(s):  
Giuseppe Bellei ◽  
Guido Gentile ◽  
Lorenzo Meschini ◽  
Natale Papola
Keyword(s):  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Demand Model ◽  
Dynamic Traffic ◽  
Departure Time ◽  
Departure Time Choice

Dynamic Traffic Assignment: Genetic Algorithms Approach

1997 ◽  
Vol 1588 (1) ◽  
pp. 95-103 ◽  
Author(s):  
Adel W. Sadek ◽  
Brian L. Smith ◽  
Michael J. Demetsky
Keyword(s):  
Genetic Algorithms ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Route Guidance ◽  
Dynamic Traffic ◽  
Artificial Intelligence Technique ◽  
Hampton Roads ◽  
Assignment Model ◽  
Software Packages ◽  
Guidance Strategies

Real-time route guidance is a promising approach to alleviating congestion on the nation’s highways. A dynamic traffic assignment model is central to the development of guidance strategies. The artificial intelligence technique of genetic algorithms (GAs) is used to solve a dynamic traffic assignment model developed for a real-world routing scenario in Hampton Roads, Virginia. The results of the GA approach are presented and discussed, and the performance of the GA program is compared with an example of commercially available nonlinear programming (NLP) software. Among the main conclusions is that GAs offer tangible advantages when used to solve the dynamic traffic assignment problem. First, GAs allow the relaxation of many of the assumptions that were needed to solve the problem analytically by traditional techniques. GAs can also handle larger problems than some of the commercially available NLP software packages.

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