scholarly journals Performance Evaluation of Heuristics and Meta-Heuristics Traffic Control Strategies Using the UTNSim Traffic Simulator

2021 ◽  
Vol 29 (3) ◽  
Author(s):  
Ng Kok Mun ◽  
Mamun Ibne Reaz
Keyword(s):  
Traffic Control ◽  
Control Strategies ◽  
Research Question ◽  
Computation Time ◽  
Urban Traffic ◽  
Superior Performance ◽  
Computational Time ◽  
Performance Indices ◽  
Different Types ◽  
Real Time Application

In the past few decades, intelligent traffic controllers have been developed to responsively cope with the increasing traffic demands and congestions in urban traffic networks. Various studies to compare and evaluate the performance of traffic controllers have been conducted to investigate its effect on traffic performances such as its ability to reduce delay time, stops, throughputs and queues within a traffic network. In this paper, the authors aim to present another comparative study on heuristics versus meta-heuristics traffic control methods. To our knowledge, such comparison has not been conducted and could provide insights into a purely heuristic controller compared to meta-heuristics. The study aims to answer the research question “Can heuristics traffic control strategies outperformed meta-heuristics in terms of performance and computational costs?” For this purpose, a heuristics model-based control strategy (MCS) which was previously developed by the authors is compared to genetic algorithms (GA) and evolution strategy (ES) respectively on a nine intersections symmetric network. These control strategies were implemented via simulations on a traffic simulator called UTNSim for three different types of traffic scenarios. Performance indices such as average delays, vehicle throughputs and the computational time of these controllers were evaluated. The results revealed that the heuristic MCS outperformed GA and ES with superior performance in average delays whereas vehicle throughputs were in close agreement. The computation time of the MCS is also feasible for real-time application compared to GA and ES that has longer convergent time.

scholarly journals Rhythmic Control of Automated Traffic—Part II: Grid Network Rhythm and Online Routing

2021 ◽  
Author(s):  
Xi Lin ◽  
Meng Li ◽  
Zuo-Jun Max Shen ◽  
Yafeng Yin ◽  
Fang He
Keyword(s):  
Real Time ◽  
Operations Management ◽  
Traffic Control ◽  
Control Method ◽  
Superior Performance ◽  
Mixed Integer ◽  
Computational Time ◽  
Linear Programming Relaxation ◽  
Online Routing ◽  
Time Space

Connected and automated vehicle (CAV) technology is providing urban transportation managers tremendous opportunities for better operation of urban mobility systems. However, there are significant challenges in real-time implementation as the computational time of the corresponding operations optimization model increases exponentially with increasing vehicle numbers. Following the companion paper (Chen et al. 2021), which proposes a novel automated traffic control scheme for isolated intersections, this study proposes a network-level, real-time traffic control framework for CAVs on grid networks. The proposed framework integrates a rhythmic control method with an online routing algorithm to realize collision-free control of all CAVs on a network and achieve superior performance in average vehicle delay, network traffic throughput, and computational scalability. Specifically, we construct a preset network rhythm that all CAVs can follow to move on the network and avoid collisions at all intersections. Based on the network rhythm, we then formulate online routing for the CAVs as a mixed integer linear program, which optimizes the entry times of CAVs at all entrances of the network and their time–space routings in real time. We provide a sufficient condition that the linear programming relaxation of the online routing model yields an optimal integer solution. Extensive numerical tests are conducted to show the performance of the proposed operations management framework under various scenarios. It is illustrated that the framework is capable of achieving negligible delays and increased network throughput. Furthermore, the computational time results are also promising. The CPU time for solving a collision-free control optimization problem with 2,000 vehicles is only 0.3 second on an ordinary personal computer.

scholarly journals Clustering Mining of Urban Traffic Flow Based on CVAE

2020 ◽  
pp. 34-44
Author(s):  
Min-Tong Su ◽  
◽  
Jin Zheng ◽  
Zu-Ping Zhang
Keyword(s):  
Traffic Flow ◽  
Traffic Control ◽  
Clustering Algorithm ◽  
Control Strategies ◽  
Urban Traffic ◽  
Living Standards ◽  
Temporal Data ◽  
Low Dimensional ◽  
The City ◽  
Ap Clustering

Understanding the urban traffic flow at intersections is helpful to formulate traffic control strategies, so as to ease traffic pressure and improve people's living standards. There are many related researches on traffic flow, and similarity research is one of them. Different from the traditional way, this paper studies the traffic flow from the perspective of image similarity. The Convolutional Variational Auto-Encoder (CVAE) is introduced to extract the low-dimensional features of traffic flow during a day, and Affinity Propagation (AP) clustering algorithm is used to cluster the features without real labels. Combining the clustering results with geographic coordinates reveals the distribution pattern of traffic flow. The experimental data includes about 10 million vehicle records at 650 intersections in Changsha on a certain day. The clustering results show that the traffic flow at the intersection of Changsha City can be divided into three categories according to the time-variant trends, and the distribution of each category basically conforms to the daily traffic laws of the city. Furthermore, the effectiveness of the clustering process is further verified by clustering the open source temporal data of different lengths.

Radiation Curable Polysiloxane: Synthesis to Applications

Soft Matter ◽  
2021 ◽  
Author(s):  
Selvan T. Muthamil ◽  
Titash Mondal
Keyword(s):  
Structural Features ◽  
Superior Performance ◽  
Operational Conditions ◽  
Different Types

Among the different types of specialty polymers, polysiloxane finds its position in the pyramid's apex in performance attributes. Unique structural features result in superior performance benefits over wide operational conditions....

On‐line modelling and planning for urban traffic control

2021 ◽  
Author(s):  
Alberto Pozanco ◽  
Susana Fernández ◽  
Daniel Borrajo
Keyword(s):  
Traffic Control ◽  
Urban Traffic ◽  
On Line ◽  
Urban Traffic Control

scholarly journals Optimal Fractional PID Controller for Buck Converter Using Cohort Intelligent Algorithm

2021 ◽  
Vol 4 (3) ◽  
pp. 50
Author(s):  
Preeti Warrier ◽  
Pritesh Shah
Keyword(s):  
Fractional Order ◽  
Pid Controller ◽  
Power Converters ◽  
Buck Converter ◽  
Optimization Techniques ◽  
Superior Performance ◽  
Computational Time ◽  
Optimization Approach ◽  
Response Characteristics ◽  
Fractional Order Controller

The control of power converters is difficult due to their non-linear nature and, hence, the quest for smart and efficient controllers is continuous and ongoing. Fractional-order controllers have demonstrated superior performance in power electronic systems in recent years. However, it is a challenge to attain optimal parameters of the fractional-order controller for such types of systems. This article describes the optimal design of a fractional order PID (FOPID) controller for a buck converter using the cohort intelligence (CI) optimization approach. The CI is an artificial intelligence-based socio-inspired meta-heuristic algorithm, which has been inspired by the behavior of a group of candidates called a cohort. The FOPID controller parameters are designed for the minimization of various performance indices, with more emphasis on the integral squared error (ISE) performance index. The FOPID controller shows faster transient and dynamic response characteristics in comparison to the conventional PID controller. Comparison of the proposed method with different optimization techniques like the GA, PSO, ABC, and SA shows good results in lesser computational time. Hence the CI method can be effectively used for the optimal tuning of FOPID controllers, as it gives comparable results to other optimization algorithms at a much faster rate. Such controllers can be optimized for multiple objectives and used in the control of various power converters giving rise to more efficient systems catering to the Industry 4.0 standards.

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