Probabilistic and Coordinated Traffic Flow Management Optimization

2013 ◽  
Author(s):  
Aditya Saraf ◽  
George Hunter ◽  
Krishnakumar Ramamoorthy ◽  
Gaurav M. Nagle ◽  
Kevin Cheng
Keyword(s):  
Traffic Flow ◽  
Flow Management ◽  
Traffic Flow Management ◽  
Management Optimization

Robust And Practical Traffic Flow Management Optimization Algorithm For Near-Term Implementation

2014 ◽  
Author(s):  
Aditya P. Saraf ◽  
George Hunter ◽  
Krishnakumar Ramamoorthy ◽  
Kevin Cheng ◽  
Katy Griffin ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Optimization Algorithm ◽  
Flow Management ◽  
Traffic Flow Management ◽  
Near Term ◽  
Management Optimization

Traffic Flow Management Impact on Delay and Fuel Consumption: an Atlanta Case Study

2012 ◽  
Vol 20 (3) ◽  
pp. 203-224 ◽  
Author(s):  
Shon R. Grabbe ◽  
Banavar Sridhar ◽  
Avijit Mukherjee ◽  
Alexander Morando
Keyword(s):  
Fuel Consumption ◽  
Traffic Flow ◽  
Flow Management ◽  
Traffic Flow Management

Evaluation of an Integrated Traffic Flow Management Decision Making Approach

2010 ◽  
Vol 18 (4) ◽  
pp. 331-358 ◽  
Author(s):  
Shon Grabbe ◽  
Banavar Sridhar ◽  
Avijit Mukherjee
Keyword(s):  
Decision Making ◽  
Traffic Flow ◽  
Management Decision ◽  
Flow Management ◽  
Traffic Flow Management ◽  
Management Decision Making

scholarly journals Estimating economic severity of Air Traffic Flow Management regulations

2021 ◽  
Vol 125 ◽  
pp. 103054
Author(s):  
Luis Delgado ◽  
Gérald Gurtner ◽  
Tatjana Bolić ◽  
Lorenzo Castelli
Keyword(s):  
Traffic Flow ◽  
Air Traffic ◽  
Flow Management ◽  
Air Traffic Flow Management ◽  
Air Traffic Flow ◽  
Traffic Flow Management

scholarly journals Traffic Flow Management of Autonomous Vehicles Using Platooning and Collision Avoidance Strategies

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1221
Author(s):  
Anum Mushtaq ◽  
Irfan ul Haq ◽  
Wajih un Nabi ◽  
Asifullah Khan ◽  
Omair Shafiq
Keyword(s):  
Traffic Flow ◽  
Collision Avoidance ◽  
Autonomous Vehicles ◽  
Flow Management ◽  
V2v Communication ◽  
Cooperative Strategies ◽  
Vehicle To Vehicle ◽  
Traffic Flow Management ◽  
Vehicle To Infrastructure ◽  
Formation And Evolution

Connected Autonomous Vehicles (AVs) promise innovative solutions for traffic flow management, especially for congestion mitigation. Vehicle-to-Vehicle (V2V) communication depends on wireless technology where vehicles can communicate with each other about obstacles and make cooperative strategies to avoid these obstacles. Vehicle-to-Infrastructure (V2I) also helps vehicles to make use of infrastructural components to navigate through different paths. This paper proposes an approach based on swarm intelligence for the formation and evolution of platoons to maintain traffic flow during congestion and collision avoidance practices using V2V and V2I communications. In this paper, we present a two level approach to improve traffic flow of AVs. At the first level, we reduce the congestion by forming platoons and study how platooning helps vehicles deal with congestion or obstacles in uncertain situations. We performed experiments based on different challenging scenarios during the platoon’s formation and evolution. At the second level, we incorporate a collision avoidance mechanism using V2V and V2I infrastructures. We used SUMO, Omnet++ with veins for simulations. The results show significant improvement in performance in maintaining traffic flow.

Investigation on Evaluation of Terminal Departure Capacity Based on Route Intersection

2013 ◽  
Vol 655-657 ◽  
pp. 2262-2265
Author(s):  
Jian Guo Kong
Keyword(s):  
Mathematical Model ◽  
Traffic Flow ◽  
Air Traffic ◽  
Flow Management ◽  
Air Traffic Flow Management ◽  
Flight Data ◽  
Proposed Model ◽  
Combining Data ◽  
Traffic Flow Management ◽  
Airport Terminal

Air traffic flow management is the key to evaluate airspace capacity reasonably and accurately. Based on the flight features of terminal route intersection, this paper builds a mathematical model for scattered flight of departure aircraft, and then evaluates the terminal capacity based on this model. By combining data from Flight Data Recorder (FDR) and flight schedule with the model, an example-runway 02R of Guangzhou Baiyun airport terminal was given to show the effectiveness of the proposed model.

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