A mathematical programming module for merge control in system optimal dynamic traffic assignment

2005 ◽  
Author(s):  
Wei-Hua Lin ◽  
Hongchao Liu
Keyword(s):  
Mathematical Programming ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Dynamic Traffic ◽  
System Optimal ◽  
Optimal Dynamic

scholarly journals Discrete-Time System Optimal Dynamic Traffic Assignment (SO-DTA) with Partial Control for Physical Queuing Networks

2018 ◽  
Vol 52 (4) ◽  
pp. 982-1001 ◽  
Author(s):  
Samitha Samaranayake ◽  
Walid Krichene ◽  
Jack Reilly ◽  
Maria Laura Delle Monache ◽  
Paola Goatin ◽  
...  
Keyword(s):  
Discrete Time ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Queuing Networks ◽  
Discrete Time System ◽  
Dynamic Traffic ◽  
Time System ◽  
Partial Control ◽  
System Optimal ◽  
Optimal Dynamic

Energy and Mobility Impacts of System Optimal Dynamic Traffic Assignment for a Mixed Traffic of Legacy and Automated Vehicles

2019 ◽  
Vol 2673 (9) ◽  
pp. 389-406
Author(s):  
H. M. Abdul Aziz
Keyword(s):  
Energy Consumption ◽  
Travel Time ◽  
Traffic Assignment ◽  
Dynamic Traffic Assignment ◽  
Transportation Networks ◽  
Mixed Traffic ◽  
Automated Vehicles ◽  
Dynamic Traffic ◽  
System Optimal ◽  
Optimal Dynamic

This research develops a system optimal dynamic traffic assignment (DTA) model for mixed traffic of human drivers and automated vehicles (AVs) and investigates network level mobility and energy impacts for different market shares of AVs. A methodology based on vehicle-specific-energy is proposed to estimate the energy consumption from the embedded spatial-queuing traffic flow model within the DTA formulation. Results with a test network indicate that potential travel time and energy consumption reductions are possible with increased AV market share in transportation networks. Results also report a decrease in travel time as high as 49% and energy consumption as high as 28% at the system level. The developed DTA model will be able to assist in transportation planning and the investment decision process by estimating the mobility and energy impacts in future transportation networks with mixed traffic of human drivers and AVs.

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