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.

scholarly journals Investigating the Applicability of Upstream Detection Strategy at Pedestrian Signalised Crossings

2017 ◽  
Vol 29 (5) ◽  
pp. 503-510 ◽  
Author(s):  
Sitti A Hassan ◽  
Nick B Hounsell ◽  
Birendra P Shrestha
Keyword(s):  
Traffic Control ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Flows ◽  
Detection Strategy ◽  
Vehicle Delay ◽  
Simulation Results ◽  
The Uk ◽  
User Friendly

In the UK, the Puffin crossing has provision to extend pedestrian green time for those who take longer to cross. However, even at such a pedestrian friendly facility, the traffic signal control is usually designed to minimise vehicle delay while providing the crossing facility. This situation is rather contrary to the current policies to encourage walking. It is this inequity that has prompted the need to re-examine the traffic control of signalised crossings to provide more benefit to both pedestrians and vehicles. In this context, this paper explores the possibility of implementing an Upstream Detection strategy at a Puffin crossing to provide a user friendly crossing. The study has been carried out by simulating a mid-block Puffin crossing for various detector distances and a number of combinations of pedestrian and traffic flows. This paper presents the simulation results and recommends the situations at which Upstream Detection would be suitable.

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.

A New Design of Intelligent Traffic Signal Control

2013 ◽  
Vol 3 (3) ◽  
pp. 51-67
Author(s):  
Fatemeh Daneshfar ◽  
Javad RavanJamJah
Keyword(s):  
Knowledge Base ◽  
Traffic Control ◽  
Clustering Algorithm ◽  
Recursive Algorithm ◽  
Green Light ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Fixed Sequence ◽  
Traffic Pattern

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.

scholarly journals Double Deep Q-Network with a Dual-Agent for Traffic Signal Control

2020 ◽  
Vol 10 (5) ◽  
pp. 1622 ◽  
Author(s):  
Jianfeng Gu ◽  
Yong Fang ◽  
Zhichao Sheng ◽  
Peng Wen
Keyword(s):  
Traffic Control ◽  
Traffic Congestion ◽  
Control Process ◽  
Stable State ◽  
Control Policy ◽  
Traffic Signal ◽  
Signalized Intersection ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Two Agents

Adaptive traffic signal control (ATSC) based on deep reinforcement learning (DRL) has shown promising prospects to reduce traffic congestion. Most existing methods keeping traffic signal phases fixed adopt two agent actions to match a four-phase suffering unstable performance and undesirable operation in a four-phase signalized intersection. In this paper, a Double Deep Q-Network (DDQN) with a dual-agent algorithm is proposed to obtain a stable traffic signal control policy. Specifically, two agents are denoted by two different states and shift the control of green lights to make the phase sequence fixed and control process stable. State representations and reward functions are presented by improving the observability and reducing the leaning difficulty of two agents. To enhance the feasibility and reliability of two agents in the traffic control of the four-phase signalized intersection, a network structure incorporating DDQN is proposed to map states to rewards. Experiments under Simulation of Urban Mobility (SUMO) are carried out, and results show that the proposed traffic signal control algorithm is effective in improving traffic capacity.

scholarly journals Cyclic Weighted k-means Method with Application to Time-of-Day Interval Partition

2021 ◽  
Vol 13 (9) ◽  
pp. 4796
Author(s):  
Gaizhen Wang ◽  
Wei Qin ◽  
Yunhao Wang
Keyword(s):  
Traffic Control ◽  
Clustering Algorithms ◽  
Time Of Day ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Flows ◽  
Cluster Number ◽  
Current Cycle ◽  
Cycle 24

Time-of-day interval partition (TIP) at a signalized intersection is of great importance in traffic control. There are two shortcomings of the traditional clustering algorithms based on traditional distance definitions (such as Euclidean distance) of traffic flows. First, some continuous time intervals are usually divided into small segments. Second, 0 o’clock (24 o’clock) is usually selected as the breakpoint. It follows that the relationship between TIP and traffic signal control is neglected. To this end, a novel cyclic distance of traffic flows is defined, which can make the end of the last cycle (24 o’clock of the last day) and the beginning of the current cycle (0 o’clock of the current day) cluster into one group. Next, a cyclic weighted k-means method is proposed, with centroid initialization, cluster number selection, and breakpoint adjustment. Lastly, the proposed method is applied to a real intersection to evaluate the benefits of traffic signal control. The conclusion of the empirical study confirms the feasibility and effectiveness of the method.

scholarly journals DQN-BASED TRAFFIC SIGNAL CONTROL SYSTEMS

2021 ◽  
Vol 6 (7(57)) ◽  
pp. 16-18
Author(s):  
Ivan Vladimirovich Kondratov
Keyword(s):  
Reinforcement Learning ◽  
Control Systems ◽  
Real Time ◽  
Traffic Control ◽  
Optimization Methods ◽  
Traffic Signal ◽  
Modern World ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Signal Control Systems

Real-time adaptive traffic control is an important problem in modern world. Historically, various optimization methods have been used to build adaptive traffic signal control systems. Recently, reinforcement learning has been advanced, and various papers showed efficiency of Deep-Q-Learning (DQN) in solving traffic control problems and providing real-time adaptive control for traffic, decreasing traffic pressure and lowering average travel time for drivers. In this paper we consider the problem of traffic signal control, present the basics of reinforcement learning and review the latest results in this area.

A New Design of Intelligent Traffic Signal Control

2015 ◽  
pp. 1686-1702
Author(s):  
Fatemeh Daneshfar ◽  
Javad RavanJamJah
Keyword(s):  
Knowledge Base ◽  
Traffic Control ◽  
Clustering Algorithm ◽  
Intelligent Transportation System ◽  
Recursive Algorithm ◽  
Green Light ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Fixed Sequence

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.

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