Traffic Signals in Connected Vehicle Environments: Chances, Challenges and Examples for Future Traffic Signal Control

2015 ◽  
Author(s):  
Jakob Kaths ◽  
Eftychios Papapanagiotou ◽  
Fritz Busch
Keyword(s):  
Traffic Signals ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Connected Vehicle

scholarly journals Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Duowei Li ◽  
Jianping Wu ◽  
Ming Xu ◽  
Ziheng Wang ◽  
Kezhen Hu
Keyword(s):  
Reinforcement Learning ◽  
Waiting Time ◽  
Traffic Signals ◽  
Control Model ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Average Waiting Time ◽  
Adaptive Traffic Signal Control ◽  
Proposed Model

Controlling traffic signals to alleviate increasing traffic pressure is a concept that has received public attention for a long time. However, existing systems and methodologies for controlling traffic signals are insufficient for addressing the problem. To this end, we build a truly adaptive traffic signal control model in a traffic microsimulator, i.e., “Simulation of Urban Mobility” (SUMO), using the technology of modern deep reinforcement learning. The model is proposed based on a deep Q-network algorithm that precisely represents the elements associated with the problem: agents, environments, and actions. The real-time state of traffic, including the number of vehicles and the average speed, at one or more intersections is used as an input to the model. To reduce the average waiting time, the agents provide an optimal traffic signal phase and duration that should be implemented in both single-intersection cases and multi-intersection cases. The co-operation between agents enables the model to achieve an improvement in overall performance in a large road network. By testing with data sets pertaining to three different traffic conditions, we prove that the proposed model is better than other methods (e.g., Q-learning method, longest queue first method, and Webster fixed timing control method) for all cases. The proposed model reduces both the average waiting time and travel time, and it becomes more advantageous as the traffic environment becomes more complex.

Developing Signal Warrants for Restricted Crossing U-Turn Intersections

2021 ◽  
pp. 036119812110469
Author(s):  
Justice Appiah
Keyword(s):  
Traffic Control ◽  
Simulation Analysis ◽  
Traffic Signals ◽  
Traffic Volume ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Left Turn ◽  
Minor Road ◽  
Intersection Configuration

The restricted crossing U-turn (RCUT) intersection is a form of innovative intersection design that reroutes left-turn and through traffic from the minor road to U-turn crossovers on the major road. When implemented correctly, an RCUT intersection can provide significant safety and operational benefits over the conventional intersection configuration. The RCUT may be controlled by traffic signals, STOP control, merges and diverges, or a combination of these. There is currently no concrete guidance in relation to when the use of traffic signal control is warranted at an RCUT intersection. This study investigated traffic volume conditions that may warrant consideration of traffic signal control at an RCUT intersection. Simulation experiments including two geometric configurations and three traffic control schemes were designed and run in VISSIM to evaluate the effects of traffic conditions on intersection delay and queue lengths. Traffic was varied by changing the composition, approach volumes, and origin–destination flow patterns to reflect different conditions that may occur at the intersection on any given day. For the range of conditions studied, the results of the simulation analysis suggested that the RCUT intersection may operate better with traffic signals (at all junctions) when the minor roadway traffic volume is more than 450 vehicles per hour (vph) and the major roadway has two through lanes. The corresponding minor roadway volume threshold increases to 575 vph when the major roadway has four through lanes.

Deriving Signal Performance Metrics from Large-Scale Connected Vehicle System Deployment

2019 ◽  
Vol 2673 (4) ◽  
pp. 36-46 ◽  
Author(s):  
Joerg Christian Wolf ◽  
Jingtao Ma ◽  
Bill Cisco ◽  
Justin Neill ◽  
Brian Moen ◽  
...  
Keyword(s):  
Large Scale ◽  
Performance Metrics ◽  
Data Dissemination ◽  
The United States ◽  
Traffic Signal ◽  
System Structure ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Connected Vehicle ◽  
Time Data

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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