Methodology for Evaluating Impact of Actuated Traffic Signal Control on Connected Vehicle Green Light Prediction

Author(s):  
Jijo Mathew ◽  
Howell Li ◽  
Rik Law ◽  
Jingtao Ma ◽  
Joerg Christian Wolf ◽  
...  
2013 ◽  
Vol 3 (3) ◽  
pp. 51-67
Author(s):  
Fatemeh Daneshfar ◽  
Javad RavanJamJah

Dynamic traffic signal control in Intelligent Transportation System (ITS) recently has received increasing attention. This paper proposed an adaptive and cooperative multi-agentfuzzy system for a decentralized traffic signal control. The proposed model has three levels of control, the current intersection traffic situation, its neighboring intersections recommendations and a knowledge base, which provides the current intersection traffic pattern. The proposed architecture comprises a knowledge base, prediction module and a traffic observer that provide data to real traffic data preparation module, then a decision-making layer takes decision to how long should the intersection green light be extended. Also every intersection flow is predicted in two different ways: 1- through a recursive algorithm. 2- based on a two stage fuzzy clustering algorithm. The proposed solution is tested with traffic control of a large connected junction and the result obtained is promising in comparison to the conventional fixed sequence traffic signal and to the vehicle actuated traffic signal control strategies which are the most applicable strategies in this area. Also to simulate the proposed traffic control solutions, a Netlogo-based traffic simulator has been developed as the agents’ world which simulates the roads, traffic flow and intersections.


Author(s):  
Joerg Christian Wolf ◽  
Jingtao Ma ◽  
Bill Cisco ◽  
Justin Neill ◽  
Brian Moen ◽  
...  

This paper documents the development of signal performance metrics (SPMs) from connected vehicle data, including application to existing deployment locations in the United States. The metrics are aggregated from anonymized vehicle traces traversing signalized intersections that are part of a system deployment that is completely based on existing communication and signal control infrastructure. No retrofit to controllers is necessary. The system structure consists of (1) traffic signal data collection via real-time data polling, (2) signal state prediction and Signal Phase and Timing (SPaT) message generation, (3) data dissemination of SPaT and Map data (MAP) messages, and (4) in-vehicle applications including countdown timers, speed advice to avoid stops, and emerging applications such as powertrain management or automatic engine start/stop functions. Four vehicle metrics were constructed including a velocity profile, arrivals by phase state (green, red), delay, and split failures. A large-scale case study in the City of Frisco, TX showed potential in helping daily management of traffic signal control, and potentially improving traffic flows. The connected vehicle SPMs were imported and visualized in a business intelligence tool (Microsoft Power BI) to deliver a signal intelligence report comprised of a series of interactive data dashboards. This interactive report provides a web-based or stand-alone interface to individual signals, or corridor or citywide measures of average vehicle delay, split failures, and arrival states.


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