scholarly journals On the Potential Impacts of Smart Traffic Control for Delay, Fuel Energy Consumption, and Emissions: An NSGA-II-Based Optimization Case Study from Dhahran, Saudi Arabia

2020 ◽  
Vol 12 (18) ◽  
pp. 7394 ◽  
Author(s):  
Mohammed Al-Turki ◽  
Arshad Jamal ◽  
Hassan M. Al-Ahmadi ◽  
Mohammed A. Al-Sughaiyer ◽  
Muhammad Zahid
Keyword(s):  
Saudi Arabia ◽  
Fuel Consumption ◽  
Performance Measures ◽  
Traffic Control ◽  
Traffic Light ◽  
Nsga Ii ◽  
Simulation And Optimization ◽  
Traffic Conditions ◽  
Study Results ◽  
Conflicting Objectives

Intelligent traffic control at urban intersections is vital to ensure efficient and sustainable traffic operations. Urban road intersections are hotspots of congestion and traffic accidents. Poor traffic management at these locations could cause numerous issues, such as longer travel time, low travel speed, long vehicle queues, delays, increased fuel consumption, and environmental emissions, and so forth. Previous studies have shown that the mentioned traffic performance measures or measures of effectiveness (MOEs) could be significantly improved by adopting intelligent traffic control protocols. The majority of studies in this regard have focused on mono or bi-objective optimization with homogenous and lane-based traffic conditions. However, decision-makers often have to deal with multiple conflicting objectives to find an optimal solution under heterogeneous stochastic traffic conditions. Therefore, it is essential to determine the optimum decision plan that offers the least conflict among several objectives. Hence, the current study aimed to develop a multi-objective intelligent traffic control protocol based on the non-dominated sorting genetic algorithm II (NSGA-II) at isolated signalized intersections in the city of Dhahran, Kingdom of Saudi Arabia. The MOEs (optimization objectives) that were considered included average vehicle delay, the total number of vehicle stops, average fuel consumption, and vehicular emissions. NSGA-II simulations were run with different initial populations. The study results showed that the proposed method was effective in optimizing considered performance measures along the optimal Pareto front. MOEs were improved in the range of 16% to 23% compared to existing conditions. To assess the efficacy of the proposed approach, an optimization analysis was performed using a Synchro traffic light simulation and optimization tool. Although the Synchro optimization resulted in a relatively lower signal timing plan than NSGA-II, the proposed algorithm outperformed the Synchro optimization results in terms of percentage reduction in MOE values.

Analysis of Freeway Traffic Incident Conditions by Using Second-Order Spatiotemporal Traffic Performance Measures

2005 ◽  
Vol 1925 (1) ◽  
pp. 20-28 ◽  
Author(s):  
Sherif Ishak ◽  
Ciprian Alecsandru
Keyword(s):  
Performance Measures ◽  
Second Order ◽  
Operating Conditions ◽  
Data Sets ◽  
Freeway Traffic ◽  
Contour Maps ◽  
Traffic Conditions ◽  
Statistical Measures ◽  
Study Results ◽  
Texture Characterization

The characteristics of preincident, postincident, and nonincident traffic conditions on freeways are investigated. The characteristics are defined by second-order statistical measures derived from spatiotemporal speed contour maps. Four performance measures are used to quantify properties such as smoothness, homogeneity, and randomness in traffic conditions in a manner similar to texture characterization of digital images. With real-world incident and traffic data sets, statistical analysis was conducted to seek distinctive characteristics of three groups of traffic operating conditions: preincident, postincident, and nonincident. The study results showed that the spatiotemporal characteristics of each of the three groups were not discernible. Although the distributions of performance measures within each group are statistically different, no consistent pattern was detected to imply that certain characteristics could increase the likelihood of incidents or identify precursory conditions to incidents.

Design and Implementation of Intelligent Traffic Control System Based on Mathematica

2011 ◽  
Vol 58-60 ◽  
pp. 2477-2482 ◽  
Author(s):  
Nai Jun Xie ◽  
Qi Hua Cheng
Keyword(s):  
Control System ◽  
Traffic Control ◽  
Traffic Light ◽  
Traffic Efficiency ◽  
Traffic Conditions ◽  
Signal Light ◽  
Simulation Based ◽  
Traffic Light Control ◽  
Traffic Control System ◽  
Mathematica Software

Intelligent traffic light control system based on fuzzy control was designed and the implementation of it was also discussed. The system can alter the signal light time according to the number of automobile waiting for passage. The simulation based on Mathematica software show that this method has better effect than traditional way in increase the automobile traffic efficiency and energy saving, what’s more it can adapt to complex traffic conditions.

scholarly journals Intelligent Intersection Control for Delay Optimization: Using Meta-Heuristic Search Algorithms

2020 ◽  
Vol 12 (5) ◽  
pp. 1896 ◽  
Author(s):  
Arshad Jamal ◽  
Muhammad Tauhidur Rahman ◽  
Hassan M. Al-Ahmadi ◽  
Irfan Ullah ◽  
Muhammad Zahid
Keyword(s):  
Objective Function ◽  
Traffic Control ◽  
Intelligent Transportation System ◽  
Signalized Intersections ◽  
Traffic Signal ◽  
Traffic Signal Control ◽  
Solution Quality ◽  
Simulation And Optimization ◽  
Study Results ◽  
Vehicle Delay

Traffic signal control is an integral component of an intelligent transportation system (ITS) that play a vital role in alleviating traffic congestion. Poor traffic management and inefficient operations at signalized intersections cause numerous problems as excessive vehicle delays, increased fuel consumption, and vehicular emissions. Operational performance at signalized intersections could be significantly enhanced by optimizing phasing and signal timing plans using intelligent traffic control methods. Previous studies in this regard have mostly focused on lane-based homogenous traffic conditions. However, traffic patterns are usually non-linear and highly stochastic, particularly during rush hours, which limits the adoption of such methods. Hence, this study aims to develop metaheuristic-based methods for intelligent traffic control at isolated signalized intersections, in the city of Dhahran, Saudi Arabia. Genetic algorithm (GA) and differential evolution (DE) were employed to enhance the intersection’s level of service (LOS) by optimizing the signal timings plan. Average vehicle delay through the intersection was selected as the primary performance index and algorithms objective function. The study results indicated that both GA and DE produced a systematic signal timings plan and significantly reduced travel time delay ranging from 15 to 35% compared to existing conditions. Although DE converged much faster to the objective function, GA outperforms DE in terms of solution quality i.e., minimum vehicle delay. To validate the performance of proposed methods, cycle length-delay curves from GA and DE were compared with optimization outputs from TRANSYT 7F, a state-of-the-art traffic signal simulation, and optimization tool. Validation results demonstrated the adequacy and robustness of proposed methods.

Workload Perception of Air Traffic Control Officers and Pilots During Continuous Descent Operations Approach Procedures

2019 ◽  
Vol 9 (1) ◽  
pp. 2-11
Author(s):  
Marina Efthymiou ◽  
Frank Fichert ◽  
Olaf Lantzsch
Keyword(s):  
Fuel Consumption ◽  
Traffic Control ◽  
Air Traffic Control ◽  
Negative Impact ◽  
Air Traffic ◽  
Reduce Fuel Consumption ◽  
Total Load ◽  
Open Path

Abstract. The paper examines the workload perceived by air traffic control officers (ATCOs) and pilots during continuous descent operations (CDOs), applying closed- and open-path procedures. CDOs reduce fuel consumption and noise emissions. Therefore, they are supported by airports as well as airlines. However, their use often depends on pilots asking for CDOs and controllers giving approval and directions. An adapted NASA Total Load Index (TLX) was used to measure the workload perception of ATCOs and pilots when applying CDOs at selected European airports. The main finding is that ATCOs’ workload increased when giving both closed- and open-path CDOs, which may have a negative impact on their willingness to apply CDOs. The main problem reported by pilots was insufficient distance-to-go information provided by ATCOs. The workload change is important when considering the use of CDOs.

scholarly journals Using Design Thinking Principles to Improve Outpatients’ Experiences in Hospital Pharmacies: A Case Study of Two Hospitals in Asir Region, Saudi Arabia

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 854
Author(s):  
Dalia Almaghaslah ◽  
Abdulrhman Alsayari ◽  
Saleh Ali Alyahya ◽  
Rana Alshehri ◽  
Khawlah Alqadi ◽  
...  
Keyword(s):  
Saudi Arabia ◽  
Problem Solving ◽  
Management System ◽  
Design Thinking ◽  
Queue Management ◽  
End User ◽  
Study Results ◽  
Hospital Pharmacies ◽  
Asir Region

Introduction: Design thinking, an innovative problem-solving approach, has gained wide popularity in healthcare disciplines. The aim of this work is to improve outpatients’ experiences in hospital pharmacies in two hospitals in Asir region, Saudi Arabia. Methods: The design thinking approach, adopted from Stanford University’s D-School, was used in this study. Results: Several problems were identified: lack of comfortable environment in the pharmacies’ waiting area, lack of a queue management system, and workflow inefficiencies related to ordering and supplies of medicines. A prototype was proposed to overcome these challenges. Discussion and Conclusion: The design thinking approach helped in identifying end-user (patients visiting outpatient pharmacies) values and desires and provided an understanding of their struggles. It also proposed tailored solutions that could improve patients’ experiences while using the services of the outpatient pharmacies.

Optimizing Traffic Control to Reduce Fuel Consumption and Vehicular Emissions

2009 ◽  
Vol 2128 (1) ◽  
pp. 105-113 ◽  
Author(s):  
Aleksandar Stevanovic ◽  
Jelka Stevanovic ◽  
Kai Zhang ◽  
Stuart Batterman
Keyword(s):  
Fuel Consumption ◽  
Traffic Control ◽  
Vehicular Emissions ◽  
Reduce Fuel Consumption

scholarly journals Seeking optimal groundwater pumping strategies at Pinggu District in Beijing, China

2012 ◽  
Vol 15 (2) ◽  
pp. 607-619 ◽  
Author(s):  
A. L. Yang ◽  
G. H. Huang ◽  
X. S. Qin ◽  
L. Li ◽  
W. Li
Keyword(s):  
Groundwater Management ◽  
Fuzzy Optimization ◽  
Cost Effective ◽  
Optimization Method ◽  
Chance Constrained Programming ◽  
Groundwater Pumping ◽  
Simulation And Optimization ◽  
Study Results ◽  
Pumping Rates ◽  
Beijing China

A simulation-based fuzzy optimization method (SFOM) was proposed for regional groundwater pumping management in considering uncertainties. SFOM enhanced the traditional groundwater management models by incorporating a response matrix model (RMM) into a fuzzy chance-constrained programming (FCCP) framework. RMM was used to approximate the input–output relationship between pumping actions and subsurface hydrologic responses. Due to its explicit expression, RMM could be easily embedded into an optimization model to help seek cost-effective pumping solutions. A groundwater management case in Pinggu District of Beijing, China, was used to demonstrate the method's applicability. The study results showed that the obtained system cost and pumping rates would vary significantly under different confidence levels of constraints satisfaction. The decision-makers could identify the best groundwater pumping strategy through analyzing the tradeoff between the risk of violating the related water resources conservation target and the economic benefit. Compared with traditional approaches, SFOM was particularly advantageous in linking simulation and optimization models together, and tackling uncertainties using fuzzy chance constraints.

Multi Objective Design Optimization of Two Bar Truss Using NSGA II and TOPSIS

2014 ◽  
Vol 984-985 ◽  
pp. 419-424
Author(s):  
P. Sabarinath ◽  
M.R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimal Solutions ◽  
Objective Functions ◽  
Nsga Ii ◽  
Multi Objective ◽  
Total Displacement ◽  
Design Variables ◽  
Conflicting Objectives

Arriving optimal solutions is one of the important tasks in engineering design. Many real-world design optimization problems involve multiple conflicting objectives. The design variables are of continuous or discrete in nature. In general, for solving Multi Objective Optimization methods weight method is preferred. In this method, all the objective functions are converted into a single objective function by assigning suitable weights to each objective functions. The main drawback lies in the selection of proper weights. Recently, evolutionary algorithms are used to find the nondominated optimal solutions called as Pareto optimal front in a single run. In recent years, Non-dominated Sorting Genetic Algorithm II (NSGA-II) finds increasing applications in solving multi objective problems comprising of conflicting objectives because of low computational requirements, elitism and parameter-less sharing approach. In this work, we propose a methodology which integrates NSGA-II and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for solving a two bar truss problem. NSGA-II searches for the Pareto set where two bar truss is evaluated in terms of minimizing the weight of the truss and minimizing the total displacement of the joint under the given load. Subsequently, TOPSIS selects the best compromise solution.

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