Urban Traffic Adaptive Signal Control through Intelligent Agent

2013 ◽  
Author(s):  
Zahoor Khan ◽  
Mohammad Naeem
Keyword(s):  
Intelligent Agent ◽  
Urban Traffic ◽  
Signal Control ◽  
Adaptive Signal Control ◽  
Adaptive Signal

scholarly journals Queue Intensity Adaptive Signal Control for Isolated Intersection Based on Vehicle Trajectory Data

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Juyuan Yin ◽  
Peng Chen ◽  
Keshuang Tang ◽  
Jian Sun
Keyword(s):  
Mobile Internet ◽  
Urban Traffic ◽  
Internet Technology ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Trajectory Data ◽  
Adaptive Signal Control ◽  
Vehicle Trajectories ◽  
Vehicle Trajectory ◽  
Adaptive Signal

With recent development of mobile Internet technology and connected vehicle technology, vehicle trajectory data are readily available and exhibit great potential to be used as an alternative data source for urban traffic signal control. In this study, a Queue Intensity Adaptive (QIA) algorithm is proposed, using vehicle trajectory data as the only input to perform adaptive signal control. First, a Kalman filter-based method is employed to estimate real-time queue state with vehicle trajectories. Then, based on queue intensity that quantifies queuing pressure, five control situations are defined, and different min-max optimization models are designed correspondingly. Last, a situation-aware signal control optimization procedure is developed to adapt intersection’s queue intensity. QIA algorithm optimizes phase sequence and green time simultaneously. One case study was conducted at a field intersection in Shenzhen, China. The results show that provided with 7.4% penetrated vehicle trajectories, QIA algorithm effectively prevented queue spillback by constraining temporal percentage of queue spillback under 2.4%. The performance of QIA was also compared with the algorithm in Synchro and Max Pressure (MP) method. It was found that compared with Synchro, the extreme queue intensity, temporal percentage of queue spillback, delay, and stops were decreased by 54.7%, 97%, 22.3%, and 45.1%, respectively, and compared with MP the above four indices were decreased by 16%, 61.5%, −1.8%, and 49.4%, respectively.

scholarly journals A Platoon-Based Adaptive Signal Control Method with Connected Vehicle Technology

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ning Li ◽  
Shukai Chen ◽  
Jianjun Zhu ◽  
Daniel Jian Sun
Keyword(s):  
Control Method ◽  
Model Performance ◽  
Urban Traffic ◽  
Mixed Integer ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Connected Vehicle ◽  
Adaptive Signal Control ◽  
The Individual ◽  
Adaptive Signal

One important objective of urban traffic signal control is to reduce individual delay and improve safety for travelers in both private car and public bus transit. To achieve signal control optimization from the perspective of all users, this paper proposes a platoon-based adaptive signal control (PASC) strategy to provide multimodal signal control based on the online connected vehicle (CV) information. By introducing unified phase precedence constraints, PASC strategy is not restricted by fixed cycle length and offsets. A mixed-integer linear programming (MILP) model is proposed to optimize signal timings in a real-time manner, with platoon arrival and discharge dynamics at stop line modeled as constraints. Based on the individual passenger occupancy, the objective function aims at minimizing total personal delay for both buses and automobiles. With the communication between signals, PASC achieves to provide implicit coordination for the signalized arterials. Simulation results by VISSIM microsimulation indicate that PASC model successfully reduces around 40% bus passenger delay and 10% automobile delay, respectively, compared with signal timings optimized by SYNCHRO. Results from sensitivity analysis demonstrate that the model performance is not sensitive to the number fluctuation of bus passengers, and the requested CV penetration rate range is around 20% for the implementation.

Effects of Connectivity and Traffic Observability on an Adaptive Traffic Signal Control System

2021 ◽  
pp. 036119812110130
Author(s):  
S M A Bin Al Islam ◽  
Mehrdad Tajalli ◽  
Rasool Mohebifard ◽  
Ali Hajbabaie
Keyword(s):  
Travel Time ◽  
Market Share ◽  
Signal Control ◽  
Market Penetration ◽  
Travel Time Reliability ◽  
Traffic Demand ◽  
Adaptive Signal Control ◽  
Loop Detector ◽  
Traffic Performance ◽  
Adaptive Signal

The effectiveness of adaptive signal control strategies depends on the level of traffic observability, which is defined as the ability of a signal controller to estimate traffic state from connected vehicle (CV), loop detector data, or both. This paper aims to quantify the effects of traffic observability on network-level performance, traffic progression, and travel time reliability, and to quantify those effects for vehicle classes and major and minor directions in an arterial corridor. Specifically, we incorporated loop detector and CV data into an adaptive signal controller and measured several mobility- and event-based performance metrics under different degrees of traffic observability (i.e., detector-only, CV-only, and CV and loop detector data) with various CV market penetration rates. A real-world arterial street of 10 intersections in Seattle, Washington was simulated in Vissim under peak hour traffic demand level with transit vehicles. The results showed that a 40% CV market share was required for the adaptive signal controller using only CV data to outperform signal control with only loop detector data. At the same market penetration rate, signal control with CV-only data resulted in the same traffic performance, progression quality, and travel time reliability as the signal control with CV and loop detector data. Therefore, the inclusion of loop detector data did not further improve traffic operations when the CV market share reached 40%. Integrating 10% of CV data with loop detector data in the adaptive signal control improved traffic performance and travel time reliability.

scholarly journals Adaptive Signal Control to Enhance Effective Green Times for Pedestrians: A Case Study

2020 ◽  
Vol 47 ◽  
pp. 704-711
Author(s):  
Gorkem Akyol ◽  
Ismet Goksad Erdagi ◽  
Mehmet Ali Silgu ◽  
Hilmi Berk Celikoglu
Keyword(s):  
Signal Control ◽  
Adaptive Signal Control ◽  
Adaptive Signal

Real-Time Detector-Free Adaptive Signal Control with Low Penetration of Connected Vehicles

2018 ◽  
Vol 2672 (18) ◽  
pp. 35-44 ◽  
Author(s):  
Yiheng Feng ◽  
Jianfeng Zheng ◽  
Henry X. Liu
Keyword(s):  
Real Time ◽  
Traffic Control ◽  
Penetration Rate ◽  
Traffic Volume ◽  
Signal Control ◽  
Connected Vehicle ◽  
Estimation Model ◽  
Loop Detectors ◽  
Adaptive Signal Control ◽  
Adaptive Signal

Most of the existing connected vehicle (CV)-based traffic control models require a critical penetration rate. If the critical penetration rate cannot be reached, then data from traditional sources (e.g., loop detectors) need to be added to improve the performance. However, it can be expected that over the next 10 years or longer, the CV penetration will remain at a low level. This paper presents a real-time detector-free adaptive signal control with low penetration of CVs ([Formula: see text]10%). A probabilistic delay estimation model is proposed, which only requires a few critical CV trajectories. An adaptive signal control algorithm based on dynamic programming is implemented utilizing estimated delay to calculate the performance function. If no CV is observed during one signal cycle, historical traffic volume is used to generate signal timing plans. The proposed model is evaluated at a real-world intersection in VISSIM with different demand levels and CV penetration rates. Results show that the new model outperforms well-tuned actuated control regarding delay reduction, in all scenarios under only 10% penetrate rate. The results also suggest that the accuracy of historical traffic volume plays an important role in the performance of the algorithm.

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