This paper aimed to develop a delivery truck scheduling management system using a decision tree to support decision-making in selecting a delivery truck. First-in-first-out (FIFO) and decision tree techniques were applied to prioritize loading doors for delivery trucks with the use of iterated local search (ILS) in recommending the route for the transport of goods. Besides, an arrangement of loading doors can be assigned to the door that meets the specified conditions. The experimental results showed that the system was able to assign the job to a delivery truck under the specified conditions that were close to the actual operation at a similarity of 0.80. In addition, the application of ILS suggested the route of the food delivery truck in planning the most effective transportation route with the best total distance.
This white paper presents a spatial decision support system (SDSS) aimed at generating optimized vehicles routes for multiple vehicles routing problems that involves serving the demand located at nodes of a transportation network. The SDSS incorporates MapPointTM (cartography and network data), a database and a metaheuristic developed generate routes.
Towards a new intelligent traffic system based on deep learning and data int...IJECEIAES
Time series forecasting is an important technique to study the behavior of temporal data in order to forecast the future values, which is widely applied in intelligent traffic systems (ITS). In this paper, several deep learning models were designed to deal with the multivariate time series forecasting problem for the purpose of long-term predicting traffic volume. Simulation results showed that the best forecasts are obtained with the use of two hidden long short-term memory (LSTM) layers: the first with 64 neurons and the second with 32 neurons. Over 93% of the forecasts were made with less than ±2.0% error. The analysis of variances is mainly due to peaks in some extreme conditions. For this purpose, the data was then merged between two different sources: electromagnetic loops and cameras. Data fusion is based on a calibration of the reliability of the sources according to the visibility conditions and time of the day. The integration results were then compared with the real data to prove the improvement of the prediction results in peak periods after the data fusion step.
With the development of the urbanization, industrialization and populace, there has been a huge development in the rush hour gridlock. With development in the rush hour gridlock, there got a heap of issues with it as well, these issues incorporate congested roads, mishaps and movement govern infringement at the overwhelming activity signals. This thusly adversy affects the economy of the nation and in addition the loss of lives. Thus, Speed control is in the need of great importance because of the expanded rate of mishaps announced in our everyday life. The criminal traffic offense expanded due to over movement on streets. The reason is rapid of vehicles. The speed of the vehicles is past the normal speed confine is called speed infringement. In this paper diverse issues are confronted that are given in issue detailing. Every one of these issues are in future with the assistance of the fortification learning issue and advancement issue the changed neural system is contemplated with NN calculations forward Chaining back spread . Omesh Goyal | Chamkour Singh ""A Review on Traffic Signal Identification"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23557.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23557/a-review-on-traffic-signal-identification/omesh-goyal
Optimization of smart traffic lights to prevent traffic congestion using fuzz...TELKOMNIKA JOURNAL
not been able to show the right time according to the existing traffic conditions. Time settings based on peak/off-peak traffic lights are not enough to handle unexpected situations. The fuzzy mamdani method makes decisions with several stages, the criteria used are the number of vehicles, the length of the queue and the width of the road to be able to optimize the time settings based on the real-time conditions required so that unwanted green signals (when there is no queue) can be avoided. The purpose of this research is to create a simulator to optimize traffic time management, so that the timers on each track have the intelligence to predict the right time, so that congestion at the intersection can be reduced by adding up to 15 seconds of green light from the previous time in the path of many vehicles.
Conflict-free dynamic route multi-agv using dijkstra Floyd-warshall hybrid a...IJECEIAES
This document summarizes a research study that proposes a hybrid algorithm using Dijkstra and Floyd-Warshall algorithms to find conflict-free routes for multiple autonomous guided vehicles (AGVs) in a warehouse distribution system. The goal is to optimize the shortest distances and schedules for AGVs performing pickup and delivery tasks within specified time windows. A grid topology route model is used to represent the warehouse environment. The hybrid algorithm combines dynamic programming approaches to determine optimized, conflict-free routes for multiple AGVs operating simultaneously while meeting time constraints for tasks at workstations. The algorithm aims to improve efficiency of autonomous vehicle systems for goods distribution in manufacturing warehouse applications.
A combined fuzzy AHP with fuzzy TOPSIS to locate industrial supporting bonded...TELKOMNIKA JOURNAL
This document proposes using a combined fuzzy analytic hierarchy process (FAHP) and fuzzy technique for ordering preferences by similarity to ideal solution (FTOPSIS) to locate an industrial supporting bonded logistics center. It establishes criteria and sub-criteria for site selection through literature review and expert discussion. Fuzzy AHP is used to obtain weighted values of the criteria and sub-criteria. Fuzzy TOPSIS then assesses alternative sites based on the weights to determine the preferred option closest to the positive ideal. The method allows for imprecise inputs from experts and ranks four potential sites - MM2100, Jababeka, Greenland International Industrial Center, and Marunda Center - to identify the optimal location.
The document summarizes research on intelligent transport systems in Europe. It discusses 6 key areas: 1) road traffic management
and control, 2) air traffic management and control, 3) maritime traffic management and control, 4) safety and emergency systems,
5) satellite-based technologies, and 6) decision support and data management systems. The research aims to apply technologies like
GPS, sensors, and wireless communications to transportation to improve safety, efficiency, and management of traffic by air, sea,
and land.
Evolutionary reinforcement learning multi-agents system for intelligent traf...IJECEIAES
The document summarizes a research paper that proposes a new approach called evolutionary reinforcement learning multi-agents system (ERL-MA) for intelligent traffic light control. The ERL-MA system combines computational intelligence and machine learning. It consists of two layers: a modeling layer that uses intersection modeling to understand junction constraints, and a decision layer that uses a novel greedy genetic algorithm and Q-learning to determine optimal phase sequences and signal timings. The approach is evaluated using a real-world traffic simulation scenario from Bologna, Italy. Results show the ERL-MA system achieves competitive performance compared to other adaptive traffic control systems in terms of various metrics.
This white paper presents a spatial decision support system (SDSS) aimed at generating optimized vehicles routes for multiple vehicles routing problems that involves serving the demand located at nodes of a transportation network. The SDSS incorporates MapPointTM (cartography and network data), a database and a metaheuristic developed generate routes.
Towards a new intelligent traffic system based on deep learning and data int...IJECEIAES
Time series forecasting is an important technique to study the behavior of temporal data in order to forecast the future values, which is widely applied in intelligent traffic systems (ITS). In this paper, several deep learning models were designed to deal with the multivariate time series forecasting problem for the purpose of long-term predicting traffic volume. Simulation results showed that the best forecasts are obtained with the use of two hidden long short-term memory (LSTM) layers: the first with 64 neurons and the second with 32 neurons. Over 93% of the forecasts were made with less than ±2.0% error. The analysis of variances is mainly due to peaks in some extreme conditions. For this purpose, the data was then merged between two different sources: electromagnetic loops and cameras. Data fusion is based on a calibration of the reliability of the sources according to the visibility conditions and time of the day. The integration results were then compared with the real data to prove the improvement of the prediction results in peak periods after the data fusion step.
With the development of the urbanization, industrialization and populace, there has been a huge development in the rush hour gridlock. With development in the rush hour gridlock, there got a heap of issues with it as well, these issues incorporate congested roads, mishaps and movement govern infringement at the overwhelming activity signals. This thusly adversy affects the economy of the nation and in addition the loss of lives. Thus, Speed control is in the need of great importance because of the expanded rate of mishaps announced in our everyday life. The criminal traffic offense expanded due to over movement on streets. The reason is rapid of vehicles. The speed of the vehicles is past the normal speed confine is called speed infringement. In this paper diverse issues are confronted that are given in issue detailing. Every one of these issues are in future with the assistance of the fortification learning issue and advancement issue the changed neural system is contemplated with NN calculations forward Chaining back spread . Omesh Goyal | Chamkour Singh ""A Review on Traffic Signal Identification"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23557.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23557/a-review-on-traffic-signal-identification/omesh-goyal
Optimization of smart traffic lights to prevent traffic congestion using fuzz...TELKOMNIKA JOURNAL
not been able to show the right time according to the existing traffic conditions. Time settings based on peak/off-peak traffic lights are not enough to handle unexpected situations. The fuzzy mamdani method makes decisions with several stages, the criteria used are the number of vehicles, the length of the queue and the width of the road to be able to optimize the time settings based on the real-time conditions required so that unwanted green signals (when there is no queue) can be avoided. The purpose of this research is to create a simulator to optimize traffic time management, so that the timers on each track have the intelligence to predict the right time, so that congestion at the intersection can be reduced by adding up to 15 seconds of green light from the previous time in the path of many vehicles.
Conflict-free dynamic route multi-agv using dijkstra Floyd-warshall hybrid a...IJECEIAES
This document summarizes a research study that proposes a hybrid algorithm using Dijkstra and Floyd-Warshall algorithms to find conflict-free routes for multiple autonomous guided vehicles (AGVs) in a warehouse distribution system. The goal is to optimize the shortest distances and schedules for AGVs performing pickup and delivery tasks within specified time windows. A grid topology route model is used to represent the warehouse environment. The hybrid algorithm combines dynamic programming approaches to determine optimized, conflict-free routes for multiple AGVs operating simultaneously while meeting time constraints for tasks at workstations. The algorithm aims to improve efficiency of autonomous vehicle systems for goods distribution in manufacturing warehouse applications.
A combined fuzzy AHP with fuzzy TOPSIS to locate industrial supporting bonded...TELKOMNIKA JOURNAL
This document proposes using a combined fuzzy analytic hierarchy process (FAHP) and fuzzy technique for ordering preferences by similarity to ideal solution (FTOPSIS) to locate an industrial supporting bonded logistics center. It establishes criteria and sub-criteria for site selection through literature review and expert discussion. Fuzzy AHP is used to obtain weighted values of the criteria and sub-criteria. Fuzzy TOPSIS then assesses alternative sites based on the weights to determine the preferred option closest to the positive ideal. The method allows for imprecise inputs from experts and ranks four potential sites - MM2100, Jababeka, Greenland International Industrial Center, and Marunda Center - to identify the optimal location.
The document summarizes research on intelligent transport systems in Europe. It discusses 6 key areas: 1) road traffic management
and control, 2) air traffic management and control, 3) maritime traffic management and control, 4) safety and emergency systems,
5) satellite-based technologies, and 6) decision support and data management systems. The research aims to apply technologies like
GPS, sensors, and wireless communications to transportation to improve safety, efficiency, and management of traffic by air, sea,
and land.
Evolutionary reinforcement learning multi-agents system for intelligent traf...IJECEIAES
The document summarizes a research paper that proposes a new approach called evolutionary reinforcement learning multi-agents system (ERL-MA) for intelligent traffic light control. The ERL-MA system combines computational intelligence and machine learning. It consists of two layers: a modeling layer that uses intersection modeling to understand junction constraints, and a decision layer that uses a novel greedy genetic algorithm and Q-learning to determine optimal phase sequences and signal timings. The approach is evaluated using a real-world traffic simulation scenario from Bologna, Italy. Results show the ERL-MA system achieves competitive performance compared to other adaptive traffic control systems in terms of various metrics.
DYNAMIC RESOURCE ALLOCATION IN ROAD TRANSPORT SECTOR USING MOBILE CLOUD COMPU...IAEME Publication
Literature review revealed application of various techniques for efficient use of existing resources in road transport sector vehicles, operators and related facilities. This issue assumes bigger dimensions in situations where there are multiple routes and the demand in the routes is highly fluctuating over the day. The application of the existing techniques as reported in literature addresses above issues to a considerable extent. However the main draw back in existing techniques is lack of
proper uninterrupted information about vehicles and demand available at a central place for allocation of vehicles in different roads and huge computational times required for processing. Cloud computing is a recently developed processing tool that is used in effective utilization of resources in transport sector under dynamic resource allocation.
A Computational Study Of Traffic Assignment AlgorithmsNicole Adams
The document summarizes a study comparing algorithms for solving traffic assignment problems. It classified algorithms as link-based (using link flows), path-based (using path flows), or origin-based (using link flows from origins). It reviewed literature on algorithms like Frank-Wolfe (link-based), path equilibration (path-based), and origin-based algorithm. It chose to implement representative algorithms from each class: Frank-Wolfe, conjugate Frank-Wolfe, bi-conjugate Frank-Wolfe (link-based), path equilibration, gradient projection, projected gradient, improved social pressure (path-based), and Algorithm B (origin-based) to compare their performance on benchmark problems.
A Computational Study Of Traffic Assignment AlgorithmsAlicia Buske
This document summarizes a research study that compares different algorithms for solving traffic assignment problems. The study performs a literature review of prominent traffic assignment algorithms, classifying them based on how the solution is represented (link-based, path-based, origin-based). It then implements representative algorithms from each class and conducts computational tests on benchmark networks of varying sizes. The results are analyzed to compare algorithm performance and identify the impact of different algorithm components on running time.
This document provides a comprehensive literature review and analysis of various traffic prediction techniques. It begins with an abstract that outlines the need for accurate traffic forecasting to address issues caused by increased road traffic. The document then reviews several existing traffic prediction methods and technologies, including fuzzy logic-based systems, intelligent traffic signal controllers, dynamic traffic information systems, and frameworks that utilize IoT, cloud computing, and machine learning. It identifies gaps in current literature, such as a lack of sensor data and advanced application frameworks for prediction. Finally, the document presents several comparison tables analyzing traffic prediction techniques based on the datasets, parameters, merits and demerits of each approach. The overall purpose is to conduct a systematic analysis of past work and identify future research
Classification Approach for Big Data Driven Traffic Flow Prediction using Ap...IRJET Journal
This document discusses a proposed system for predicting traffic flow using big data and classification approaches. The system uses K-Nearest Neighbors (KNN) classification to identify traffic patterns and routes. It then uses a Convolutional Neural Network (CNN) to predict traffic flow levels on particular routes. The KNN identifies travel times between locations while the CNN predicts flow levels. The proposed system is evaluated using metrics like root mean squared error and mean relative error, and is found to improve accuracy and reduce prediction time compared to existing methods. The system aims to provide route recommendations to users based on minimum predicted traffic flow.
A multi-objective evolutionary scheme for control points deployment in intell...IJECEIAES
One of the problems that hinder emergency in developing countries is the problem of monitoring a number of activities on inter-urban roadway networks. In the literature, the use of control points is proposed in the context of these countries in order to ensure efficient monitoring, by ensuring a good coverage while minimizing the installation costs as well as the number of accidents across these road networks. In this work, we propose an optimal deployment of these control points from several optimization methods based on some evolutionary multi-objective algorithms: the Non dominated sorting genetic algorithm-II (NSGA-II); the multi-objective particle swarm optimization (MOPSO); the strength pareto evolutionary algorithm-II (SPEA-II); and the pareto envelope based selection algorithm-II (PESA-II). We performed the tests and compared these deployments using pareto front and performance indicators like the spread and hypervolume and the inverted generational distance (IGD). The results obtained show that the NSGA-II method is the most adequate in the deployment of these control points.
MODERN TECHNOLOGIES USE IN TRANSPORTATION ENGINEERINGIRJET Journal
This document discusses modern technologies that can impact transportation engineering. It reviews areas where advanced technologies can improve transportation, including vehicular navigation and control using GPS, computer-aided planning and design systems, robotics and automation applications for construction and maintenance, and machine vision for vehicle detection instead of sensors embedded in roads. The technologies offer potential for increased efficiency, cost savings, quality improvements and safety benefits. Widespread adoption of these technologies in transportation could require changes to engineering practice and education.
Dynamic resource allocation in road transport sector using mobile cloud compu...IAEME Publication
This document discusses dynamic resource allocation in the road transport sector using mobile cloud computing techniques. It provides an overview of existing literature on dynamic resource allocation methods and their limitations in addressing high vehicle and route demand fluctuations. The document then proposes using mobile cloud computing to allow for real-time vehicle-route allocation with minimal processing time by installing mobile devices at stations to communicate demand data to nearby clouds and a central traffic manager for computation and order distribution. Simulation case studies are developed and results are compared to real data to validate the mobile cloud computing approach for improved dynamic resource allocation under heavy demand fluctuations.
Intelligent Traffic Management System using Shortest Pathijtsrd
Due to current significant increases in population and consequently in traffic congestion in most metropolitan cities in the world, designing of an intelligent traffic management system ITMS in order to detect the path with the shortest travel time is critical for emergency, health, and courier services. The aim of this research study was to develop a theoretical traffic detection system and capable of estimating the travel time associated with each street segment based on the traffic data updated every 20 seconds, which successively finds the path with the shortest travel time in the network by using a dynamic programming technique. Furthermore, in this study we model the travel time associated with each street segment based on the historical and real time data considering that the traffic speed on each road segment is piecewise constant. It would be useful to implement such algorithms in GIS systems such as Google map in such a way that the service delivery drivers can avoid congested routes by receiving real time traffic information. Bharti Kumari | Vinod Mahor "Intelligent Traffic Management System using Shortest Path" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50598.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/50598/intelligent-traffic-management-system-using-shortest-path/bharti-kumari
HITS: A History-Based Intelligent Transportation System IJDKP
Transportation is the driving force of any country. Today we are facing an explosion in the number of motor vehicles that affects our daily routines. Intelligent transportation systems (ITS) aim to provide efficient tools that solve traffic problems. Predicting route congestions during different day periods can help drivers choose better routes for their trips. In this paper we propose “HITS” a traffic control system that integrates moving object database techniques [30, 28] along with data warehousing techniques [15].
Our system uses historical traffic information to answer queries about moving objects on road network, and to analyze historical traffic conditions to enhance future traffic related decisions.
Big data traffic management in vehicular ad-hoc network IJECEIAES
Today, the world has experienced a new trend with regard to data system management, traditional database management tools have become outdated and they will no longer be able to process the mass of data generated by different systems, that's why big data is there to process this mass of data to bring out crucial information hidden in this data, and without big data technologies the treatment is very difficult to manage; among the domains that uses big data technologies is vehicular ad-hoc network to manage their voluminous data. In this article, we establish in the first step a method that allow to detect anomalies or accidents within the road and compute the time spent in each road section in real time, which permit us to obtain a database having the estimated time spent in all sections in real time, this will serve us to send to the vehicles the right estimated time of arrival all along their journey and the optimal route to attain their destination. This database is useful to utilize it like inputs for machine learning to predict the places and times where the probability of accidents is higher. The experimental results prove that our method permits us to avoid congestions and apportion the load of vehicles in all roads effectively, also it contributes to road safety.
A Systematic Literature Review Of Vehicle Speed Assistance In Intelligent Tra...Nat Rice
This document summarizes a systematic literature review of vehicle speed assistance systems in intelligent transportation systems. It identified 79 primary studies published between 2011-2020. After applying quality assessment criteria, 50 studies were selected for detailed analysis. The review found that vehicle speed assistance systems aim to achieve various driving goals like eco-driving, safety, comfort and travel time improvement. It analyzed the different methods proposed in the literature to provide speed assistance and the objectives addressed by these systems. The review identified challenges and opportunities for future research in intelligent vehicle speed assistance.
Driving cycle tracking device development and analysis on route-to-work for K...TELKOMNIKA JOURNAL
Driving cycle is a series of speed versus time profile used to represent driving patterns of a vehicle. research in this field guides vehicle manufacturers and environmentalists to investigate air quality through emissions. Study on driving cycle also aids manufacturers to manage vehicle emissions and to save energy released through exhaust. Also, driving cycles can provide information on road condition and driving behaviour of an individual. For that, a proper data collection method is crucial as it is solely based on real world driving. This research is an initiative to construct a prototype of driving cycle tracking device (DC-TRAD) in which it was implemented with internet-of-things (IoT) to manage big number of collected data. U-Blox global positioning system (GPS) neo 7 M sensor was used to increase the accuracy of data capturing and it was used on route-to-work for Kuala Terengganu city (RTW DC for KT city) for analysis.
Intelligent Transportation Systems across the worldAnamhyder1
This document provides an overview of intelligent transportation systems across different parts of the world. It discusses the history and development of ITS, including early systems in the US, Japan, Germany and other countries. It then covers the role of ITS in urban transportation systems, highlighting technologies like electronic toll collection, ramp metering, red light cameras, traffic signal coordination, and transit signal priority. The document also looks at ITS developments and applications in regions like the US, Europe, Middle East, India, and gaps in applying ITS to Indian traffic conditions.
This document summarizes an online traffic simulation service called Relteq Harmony that can help transportation agencies manage highway incidents. Key features include:
1) It allows users to simulate traffic scenarios and evaluate different response strategies to reduce delays from incidents.
2) It automatically generates traffic models from daily traffic data using sensors, requiring no manual calibration.
3) It runs simulations in the cloud, providing more resources and allowing collaboration between agencies.
Ever increasing number of vehicles on road imposes a due concern about road safety on the automobile manufacturers and the users as well. Cargo vehicle is a major part of automobile sector and attained a new look in the era of internet of things. The current paper pr esents various modern trends being incorporated in Cargo vehicles to monitor different vehicles and environmental par ameters to ensure road safety. Authors have extended the scope of study with due c onsideration to R&D efforts in advanced sensing,environmental perception and interactive driver ass istance systems to avoid road accidents due to une ven/over loading of cargo vehicles in specific. With this ki nd of challenging efforts,the authors aim to conve rge important technologies such as automotive-electronics,sensor s and mobile communication towards safe operation o f cargo vehicles while negotiating the road.
121808 - FINAL Report on the Potential Impact of Regional Transit on Metropol...John Crocker
This document examines the potential impacts of major investment in regional transit infrastructure in metropolitan Atlanta using the Concept 3 Vision Plan from the Transit Planning Board. It finds that Concept 3 could more than double transit ridership, increase accessibility of employment centers, reduce congestion and travel times on roadways, improve safety, and provide benefits that outweigh costs with a ratio of $4.9-$10.8 billion in annual benefits for an annual investment of $2.4 billion. The analysis also finds Concept 3 would help achieve state transportation goals around mobility, accessibility, congestion reduction, and optimizing existing infrastructure.
Adaptive traffic lights based on traffic flow prediction using machine learni...IJECEIAES
This document discusses using machine learning algorithms to predict traffic flow and reduce congestion at intersections. It compares linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor models on a UK road traffic dataset. All models performed well according to evaluation metrics, indicating they are suitable for an adaptive traffic light system. The system was implemented using a random forest regressor model and simulations showed it reduced traffic congestion by 30.8%, justifying its effectiveness.
With the continuous development and application of modern logistics technology, logistics cost has become one of the important factors of enterprise competition. For the special field of cold chain logistics distribution, cost control is particularly critical. By studying the control method of cold chain distribution cost, this paper introduces how to reasonably optimize the distribution cost while effectively controlling the distribution cost so as to improve the competitiveness of enterprises. This paper sorts out the relevant theoretical overview and conceptual analysis and analyses the current situation of cold chain distribution cost control in logistics companies. Then, the existing logistics cost control system is evaluated, and the hierarchical analysis method and model comprehensive evaluation method are used to analyse the current control system score and problems that require additional attention and find the cause of the problem. Finally, rectification suggestions are put forward to improve distribution costs to enhance the competitive strength of enterprises.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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DYNAMIC RESOURCE ALLOCATION IN ROAD TRANSPORT SECTOR USING MOBILE CLOUD COMPU...IAEME Publication
Literature review revealed application of various techniques for efficient use of existing resources in road transport sector vehicles, operators and related facilities. This issue assumes bigger dimensions in situations where there are multiple routes and the demand in the routes is highly fluctuating over the day. The application of the existing techniques as reported in literature addresses above issues to a considerable extent. However the main draw back in existing techniques is lack of
proper uninterrupted information about vehicles and demand available at a central place for allocation of vehicles in different roads and huge computational times required for processing. Cloud computing is a recently developed processing tool that is used in effective utilization of resources in transport sector under dynamic resource allocation.
A Computational Study Of Traffic Assignment AlgorithmsNicole Adams
The document summarizes a study comparing algorithms for solving traffic assignment problems. It classified algorithms as link-based (using link flows), path-based (using path flows), or origin-based (using link flows from origins). It reviewed literature on algorithms like Frank-Wolfe (link-based), path equilibration (path-based), and origin-based algorithm. It chose to implement representative algorithms from each class: Frank-Wolfe, conjugate Frank-Wolfe, bi-conjugate Frank-Wolfe (link-based), path equilibration, gradient projection, projected gradient, improved social pressure (path-based), and Algorithm B (origin-based) to compare their performance on benchmark problems.
A Computational Study Of Traffic Assignment AlgorithmsAlicia Buske
This document summarizes a research study that compares different algorithms for solving traffic assignment problems. The study performs a literature review of prominent traffic assignment algorithms, classifying them based on how the solution is represented (link-based, path-based, origin-based). It then implements representative algorithms from each class and conducts computational tests on benchmark networks of varying sizes. The results are analyzed to compare algorithm performance and identify the impact of different algorithm components on running time.
This document provides a comprehensive literature review and analysis of various traffic prediction techniques. It begins with an abstract that outlines the need for accurate traffic forecasting to address issues caused by increased road traffic. The document then reviews several existing traffic prediction methods and technologies, including fuzzy logic-based systems, intelligent traffic signal controllers, dynamic traffic information systems, and frameworks that utilize IoT, cloud computing, and machine learning. It identifies gaps in current literature, such as a lack of sensor data and advanced application frameworks for prediction. Finally, the document presents several comparison tables analyzing traffic prediction techniques based on the datasets, parameters, merits and demerits of each approach. The overall purpose is to conduct a systematic analysis of past work and identify future research
Classification Approach for Big Data Driven Traffic Flow Prediction using Ap...IRJET Journal
This document discusses a proposed system for predicting traffic flow using big data and classification approaches. The system uses K-Nearest Neighbors (KNN) classification to identify traffic patterns and routes. It then uses a Convolutional Neural Network (CNN) to predict traffic flow levels on particular routes. The KNN identifies travel times between locations while the CNN predicts flow levels. The proposed system is evaluated using metrics like root mean squared error and mean relative error, and is found to improve accuracy and reduce prediction time compared to existing methods. The system aims to provide route recommendations to users based on minimum predicted traffic flow.
A multi-objective evolutionary scheme for control points deployment in intell...IJECEIAES
One of the problems that hinder emergency in developing countries is the problem of monitoring a number of activities on inter-urban roadway networks. In the literature, the use of control points is proposed in the context of these countries in order to ensure efficient monitoring, by ensuring a good coverage while minimizing the installation costs as well as the number of accidents across these road networks. In this work, we propose an optimal deployment of these control points from several optimization methods based on some evolutionary multi-objective algorithms: the Non dominated sorting genetic algorithm-II (NSGA-II); the multi-objective particle swarm optimization (MOPSO); the strength pareto evolutionary algorithm-II (SPEA-II); and the pareto envelope based selection algorithm-II (PESA-II). We performed the tests and compared these deployments using pareto front and performance indicators like the spread and hypervolume and the inverted generational distance (IGD). The results obtained show that the NSGA-II method is the most adequate in the deployment of these control points.
MODERN TECHNOLOGIES USE IN TRANSPORTATION ENGINEERINGIRJET Journal
This document discusses modern technologies that can impact transportation engineering. It reviews areas where advanced technologies can improve transportation, including vehicular navigation and control using GPS, computer-aided planning and design systems, robotics and automation applications for construction and maintenance, and machine vision for vehicle detection instead of sensors embedded in roads. The technologies offer potential for increased efficiency, cost savings, quality improvements and safety benefits. Widespread adoption of these technologies in transportation could require changes to engineering practice and education.
Dynamic resource allocation in road transport sector using mobile cloud compu...IAEME Publication
This document discusses dynamic resource allocation in the road transport sector using mobile cloud computing techniques. It provides an overview of existing literature on dynamic resource allocation methods and their limitations in addressing high vehicle and route demand fluctuations. The document then proposes using mobile cloud computing to allow for real-time vehicle-route allocation with minimal processing time by installing mobile devices at stations to communicate demand data to nearby clouds and a central traffic manager for computation and order distribution. Simulation case studies are developed and results are compared to real data to validate the mobile cloud computing approach for improved dynamic resource allocation under heavy demand fluctuations.
Intelligent Traffic Management System using Shortest Pathijtsrd
Due to current significant increases in population and consequently in traffic congestion in most metropolitan cities in the world, designing of an intelligent traffic management system ITMS in order to detect the path with the shortest travel time is critical for emergency, health, and courier services. The aim of this research study was to develop a theoretical traffic detection system and capable of estimating the travel time associated with each street segment based on the traffic data updated every 20 seconds, which successively finds the path with the shortest travel time in the network by using a dynamic programming technique. Furthermore, in this study we model the travel time associated with each street segment based on the historical and real time data considering that the traffic speed on each road segment is piecewise constant. It would be useful to implement such algorithms in GIS systems such as Google map in such a way that the service delivery drivers can avoid congested routes by receiving real time traffic information. Bharti Kumari | Vinod Mahor "Intelligent Traffic Management System using Shortest Path" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-5 , August 2022, URL: https://www.ijtsrd.com/papers/ijtsrd50598.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/50598/intelligent-traffic-management-system-using-shortest-path/bharti-kumari
HITS: A History-Based Intelligent Transportation System IJDKP
Transportation is the driving force of any country. Today we are facing an explosion in the number of motor vehicles that affects our daily routines. Intelligent transportation systems (ITS) aim to provide efficient tools that solve traffic problems. Predicting route congestions during different day periods can help drivers choose better routes for their trips. In this paper we propose “HITS” a traffic control system that integrates moving object database techniques [30, 28] along with data warehousing techniques [15].
Our system uses historical traffic information to answer queries about moving objects on road network, and to analyze historical traffic conditions to enhance future traffic related decisions.
Big data traffic management in vehicular ad-hoc network IJECEIAES
Today, the world has experienced a new trend with regard to data system management, traditional database management tools have become outdated and they will no longer be able to process the mass of data generated by different systems, that's why big data is there to process this mass of data to bring out crucial information hidden in this data, and without big data technologies the treatment is very difficult to manage; among the domains that uses big data technologies is vehicular ad-hoc network to manage their voluminous data. In this article, we establish in the first step a method that allow to detect anomalies or accidents within the road and compute the time spent in each road section in real time, which permit us to obtain a database having the estimated time spent in all sections in real time, this will serve us to send to the vehicles the right estimated time of arrival all along their journey and the optimal route to attain their destination. This database is useful to utilize it like inputs for machine learning to predict the places and times where the probability of accidents is higher. The experimental results prove that our method permits us to avoid congestions and apportion the load of vehicles in all roads effectively, also it contributes to road safety.
A Systematic Literature Review Of Vehicle Speed Assistance In Intelligent Tra...Nat Rice
This document summarizes a systematic literature review of vehicle speed assistance systems in intelligent transportation systems. It identified 79 primary studies published between 2011-2020. After applying quality assessment criteria, 50 studies were selected for detailed analysis. The review found that vehicle speed assistance systems aim to achieve various driving goals like eco-driving, safety, comfort and travel time improvement. It analyzed the different methods proposed in the literature to provide speed assistance and the objectives addressed by these systems. The review identified challenges and opportunities for future research in intelligent vehicle speed assistance.
Driving cycle tracking device development and analysis on route-to-work for K...TELKOMNIKA JOURNAL
Driving cycle is a series of speed versus time profile used to represent driving patterns of a vehicle. research in this field guides vehicle manufacturers and environmentalists to investigate air quality through emissions. Study on driving cycle also aids manufacturers to manage vehicle emissions and to save energy released through exhaust. Also, driving cycles can provide information on road condition and driving behaviour of an individual. For that, a proper data collection method is crucial as it is solely based on real world driving. This research is an initiative to construct a prototype of driving cycle tracking device (DC-TRAD) in which it was implemented with internet-of-things (IoT) to manage big number of collected data. U-Blox global positioning system (GPS) neo 7 M sensor was used to increase the accuracy of data capturing and it was used on route-to-work for Kuala Terengganu city (RTW DC for KT city) for analysis.
Intelligent Transportation Systems across the worldAnamhyder1
This document provides an overview of intelligent transportation systems across different parts of the world. It discusses the history and development of ITS, including early systems in the US, Japan, Germany and other countries. It then covers the role of ITS in urban transportation systems, highlighting technologies like electronic toll collection, ramp metering, red light cameras, traffic signal coordination, and transit signal priority. The document also looks at ITS developments and applications in regions like the US, Europe, Middle East, India, and gaps in applying ITS to Indian traffic conditions.
This document summarizes an online traffic simulation service called Relteq Harmony that can help transportation agencies manage highway incidents. Key features include:
1) It allows users to simulate traffic scenarios and evaluate different response strategies to reduce delays from incidents.
2) It automatically generates traffic models from daily traffic data using sensors, requiring no manual calibration.
3) It runs simulations in the cloud, providing more resources and allowing collaboration between agencies.
Ever increasing number of vehicles on road imposes a due concern about road safety on the automobile manufacturers and the users as well. Cargo vehicle is a major part of automobile sector and attained a new look in the era of internet of things. The current paper pr esents various modern trends being incorporated in Cargo vehicles to monitor different vehicles and environmental par ameters to ensure road safety. Authors have extended the scope of study with due c onsideration to R&D efforts in advanced sensing,environmental perception and interactive driver ass istance systems to avoid road accidents due to une ven/over loading of cargo vehicles in specific. With this ki nd of challenging efforts,the authors aim to conve rge important technologies such as automotive-electronics,sensor s and mobile communication towards safe operation o f cargo vehicles while negotiating the road.
121808 - FINAL Report on the Potential Impact of Regional Transit on Metropol...John Crocker
This document examines the potential impacts of major investment in regional transit infrastructure in metropolitan Atlanta using the Concept 3 Vision Plan from the Transit Planning Board. It finds that Concept 3 could more than double transit ridership, increase accessibility of employment centers, reduce congestion and travel times on roadways, improve safety, and provide benefits that outweigh costs with a ratio of $4.9-$10.8 billion in annual benefits for an annual investment of $2.4 billion. The analysis also finds Concept 3 would help achieve state transportation goals around mobility, accessibility, congestion reduction, and optimizing existing infrastructure.
Adaptive traffic lights based on traffic flow prediction using machine learni...IJECEIAES
This document discusses using machine learning algorithms to predict traffic flow and reduce congestion at intersections. It compares linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor models on a UK road traffic dataset. All models performed well according to evaluation metrics, indicating they are suitable for an adaptive traffic light system. The system was implemented using a random forest regressor model and simulations showed it reduced traffic congestion by 30.8%, justifying its effectiveness.
With the continuous development and application of modern logistics technology, logistics cost has become one of the important factors of enterprise competition. For the special field of cold chain logistics distribution, cost control is particularly critical. By studying the control method of cold chain distribution cost, this paper introduces how to reasonably optimize the distribution cost while effectively controlling the distribution cost so as to improve the competitiveness of enterprises. This paper sorts out the relevant theoretical overview and conceptual analysis and analyses the current situation of cold chain distribution cost control in logistics companies. Then, the existing logistics cost control system is evaluated, and the hierarchical analysis method and model comprehensive evaluation method are used to analyse the current control system score and problems that require additional attention and find the cause of the problem. Finally, rectification suggestions are put forward to improve distribution costs to enhance the competitive strength of enterprises.
Similar to A transportation scheduling management system using decision tree and iterated local search techniques (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
Supermarket Management System Project Report.pdfKamal Acharya
Supermarket management is a stand-alone J2EE using Eclipse Juno program.
This project contains all the necessary required information about maintaining
the supermarket billing system.
The core idea of this project to minimize the paper work and centralize the
data. Here all the communication is taken in secure manner. That is, in this
application the information will be stored in client itself. For further security the
data base is stored in the back-end oracle and so no intruders can access it.
Open Channel Flow: fluid flow with a free surfaceIndrajeet sahu
Open Channel Flow: This topic focuses on fluid flow with a free surface, such as in rivers, canals, and drainage ditches. Key concepts include the classification of flow types (steady vs. unsteady, uniform vs. non-uniform), hydraulic radius, flow resistance, Manning's equation, critical flow conditions, and energy and momentum principles. It also covers flow measurement techniques, gradually varied flow analysis, and the design of open channels. Understanding these principles is vital for effective water resource management and engineering applications.
Sri Guru Hargobind Ji - Bandi Chor Guru.pdfBalvir Singh
Sri Guru Hargobind Ji (19 June 1595 - 3 March 1644) is revered as the Sixth Nanak.
• On 25 May 1606 Guru Arjan nominated his son Sri Hargobind Ji as his successor. Shortly
afterwards, Guru Arjan was arrested, tortured and killed by order of the Mogul Emperor
Jahangir.
• Guru Hargobind's succession ceremony took place on 24 June 1606. He was barely
eleven years old when he became 6th Guru.
• As ordered by Guru Arjan Dev Ji, he put on two swords, one indicated his spiritual
authority (PIRI) and the other, his temporal authority (MIRI). He thus for the first time
initiated military tradition in the Sikh faith to resist religious persecution, protect
people’s freedom and independence to practice religion by choice. He transformed
Sikhs to be Saints and Soldier.
• He had a long tenure as Guru, lasting 37 years, 9 months and 3 days
Digital Twins Computer Networking Paper Presentation.pptxaryanpankaj78
A Digital Twin in computer networking is a virtual representation of a physical network, used to simulate, analyze, and optimize network performance and reliability. It leverages real-time data to enhance network management, predict issues, and improve decision-making processes.
Null Bangalore | Pentesters Approach to AWS IAMDivyanshu
#Abstract:
- Learn more about the real-world methods for auditing AWS IAM (Identity and Access Management) as a pentester. So let us proceed with a brief discussion of IAM as well as some typical misconfigurations and their potential exploits in order to reinforce the understanding of IAM security best practices.
- Gain actionable insights into AWS IAM policies and roles, using hands on approach.
#Prerequisites:
- Basic understanding of AWS services and architecture
- Familiarity with cloud security concepts
- Experience using the AWS Management Console or AWS CLI.
- For hands on lab create account on [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
# Scenario Covered:
- Basics of IAM in AWS
- Implementing IAM Policies with Least Privilege to Manage S3 Bucket
- Objective: Create an S3 bucket with least privilege IAM policy and validate access.
- Steps:
- Create S3 bucket.
- Attach least privilege policy to IAM user.
- Validate access.
- Exploiting IAM PassRole Misconfiguration
-Allows a user to pass a specific IAM role to an AWS service (ec2), typically used for service access delegation. Then exploit PassRole Misconfiguration granting unauthorized access to sensitive resources.
- Objective: Demonstrate how a PassRole misconfiguration can grant unauthorized access.
- Steps:
- Allow user to pass IAM role to EC2.
- Exploit misconfiguration for unauthorized access.
- Access sensitive resources.
- Exploiting IAM AssumeRole Misconfiguration with Overly Permissive Role
- An overly permissive IAM role configuration can lead to privilege escalation by creating a role with administrative privileges and allow a user to assume this role.
- Objective: Show how overly permissive IAM roles can lead to privilege escalation.
- Steps:
- Create role with administrative privileges.
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- Differentiation between PassRole vs AssumeRole
Try at [killercoda.com](https://killercoda.com/cloudsecurity-scenario/)
A high-Speed Communication System is based on the Design of a Bi-NoC Router, ...DharmaBanothu
The Network on Chip (NoC) has emerged as an effective
solution for intercommunication infrastructure within System on
Chip (SoC) designs, overcoming the limitations of traditional
methods that face significant bottlenecks. However, the complexity
of NoC design presents numerous challenges related to
performance metrics such as scalability, latency, power
consumption, and signal integrity. This project addresses the
issues within the router's memory unit and proposes an enhanced
memory structure. To achieve efficient data transfer, FIFO buffers
are implemented in distributed RAM and virtual channels for
FPGA-based NoC. The project introduces advanced FIFO-based
memory units within the NoC router, assessing their performance
in a Bi-directional NoC (Bi-NoC) configuration. The primary
objective is to reduce the router's workload while enhancing the
FIFO internal structure. To further improve data transfer speed,
a Bi-NoC with a self-configurable intercommunication channel is
suggested. Simulation and synthesis results demonstrate
guaranteed throughput, predictable latency, and equitable
network access, showing significant improvement over previous
designs
AI in customer support Use cases solutions development and implementation.pdfmahaffeycheryld
AI in customer support will integrate with emerging technologies such as augmented reality (AR) and virtual reality (VR) to enhance service delivery. AR-enabled smart glasses or VR environments will provide immersive support experiences, allowing customers to visualize solutions, receive step-by-step guidance, and interact with virtual support agents in real-time. These technologies will bridge the gap between physical and digital experiences, offering innovative ways to resolve issues, demonstrate products, and deliver personalized training and support.
https://www.leewayhertz.com/ai-in-customer-support/#How-does-AI-work-in-customer-support
Blood finder application project report (1).pdfKamal Acharya
Blood Finder is an emergency time app where a user can search for the blood banks as
well as the registered blood donors around Mumbai. This application also provide an
opportunity for the user of this application to become a registered donor for this user have
to enroll for the donor request from the application itself. If the admin wish to make user
a registered donor, with some of the formalities with the organization it can be done.
Specialization of this application is that the user will not have to register on sign-in for
searching the blood banks and blood donors it can be just done by installing the
application to the mobile.
The purpose of making this application is to save the user’s time for searching blood of
needed blood group during the time of the emergency.
This is an android application developed in Java and XML with the connectivity of
SQLite database. This application will provide most of basic functionality required for an
emergency time application. All the details of Blood banks and Blood donors are stored
in the database i.e. SQLite.
This application allowed the user to get all the information regarding blood banks and
blood donors such as Name, Number, Address, Blood Group, rather than searching it on
the different websites and wasting the precious time. This application is effective and
user friendly.
A transportation scheduling management system using decision tree and iterated local search techniques
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 3, June 2023, pp. 2899~2907
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i3.pp2899-2907 2899
Journal homepage: http://ijece.iaescore.com
A transportation scheduling management system using decision
tree and iterated local search techniques
Thittaporn Ganokratanaa1
, Mahasak Ketcham2
1
Applied Computer Science Program, Department of Mathematics, Faculty of Science, King Mongkut’s University of Technology
Thonburi, Bangkok, Thailand
2
Department of Management Information Systems, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand
Article Info ABSTRACT
Article history:
Received May 19, 2022
Revised Sep 17, 2022
Accepted Oct 1, 2022
This paper aimed to develop a delivery truck scheduling management
system using a decision tree to support decision-making in selecting a
delivery truck. First-in-first-out (FIFO) and decision tree techniques were
applied to prioritize loading doors for delivery trucks with the use of
iterated local search (ILS) in recommending the route for the transport of
goods. Besides, an arrangement of loading doors can be assigned to the
door that meets the specified conditions. The experimental results showed
that the system was able to assign the job to a delivery truck under the
specified conditions that were close to the actual operation at a similarity of
0.80. In addition, the application of ILS suggested the route of the food
delivery truck in planning the most effective transportation route with the
best total distance.
Keywords:
Decision tree
First-in-first-out
Iterated local search
Scheduling management
Transportation This is an open access article under the CC BY-SA license.
Corresponding Author:
Mahasak Ketcham
Department of Management Information Systems, King Mongkut’s University of Technology North
Bangkok
1518 Pracharat 1 Rd., Wongsawang, Bangsue, Bangkok, 10800, Thailand
Email: mahasak.k@itd.kmutnb.ac.th
1. INTRODUCTION
Currently, logistics systems have been growing in high popularity and competitiveness. Modern
technology has been applied to businesses that use logistics systems to enhance their efficiency using
systems, robots, and artificial intelligence (AI). One of the reasons for the growth of logistics is due to
changes in consumer behavior, the growth of e-commerce businesses as well as the evolution of
technological innovation [1], [2]. Transportation is a key factor for almost all kinds of business. The
organization focuses on transportation management, affecting the management of various parts of the
organization. Good logistics management is essential to efficiency and reducing logistics costs. In addition,
the management of quality transportation operations, on-time delivery, and complete delivery without any
loss allows the service provided to the customers to become better [3]. Lack of information technology
systems in logistics management may cause the following effects: i) it is unable to reduce the amount of
paper to be on par with developed countries such as recording of work data that still needs to be written in a
notebook; ii) it lacks data links, resulting in inefficient communication and the exchange of information
within the organization; iii) it lacks planning for transportation of goods, causing the cost of transportation to
be higher whether the cost of labor and cost of transportation; and iv) it lacks performance measurement to
assess potential and performance. The use of information technology helps reduce logistics costs and increase
the efficiency of transportation, to achieve the goal of the logistics business, which is the fastest and most
cost-effective. Therefore, choosing an information technology system for logistics must consider the ability
2. ISSN: 2088-8708
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to reduce costs, travel time, and safety which is the main consideration that must also be given to linking the
data to other relevant systems to ensure the accuracy of the results and their practicality [4].
Current research reveals that a decision support system has been analyzed and designed to assist
timber transport businesses in managing trucks and increasing operational efficiency by scheduling trucks to
reduce turnover times. Rotkanok [5] found that the model in the truck scheduling can reduce the time of
transportation of wood chips. Limwattanakul [6] used a circular roulette metaheuristic approach to route
management in conjunction with Google Maps. The results show that it can reduce the risk of loss of income
and trade opportunities if the specialist is unable to work. The lowest total distance can be found and the
transportation is optimally managed. Jia et al. [7] proposed ant colony optimization (ACO) as a solution to
routing problems. Multi-point delivery with time frame conditions and time constraints was found to test the
program’s best ability to find answers. The data was tested on four client samples of 24 possible answers,
with the two best answers out of five tests. Sriwichai [8] presented the development of transportation
management systems by applying risk management techniques in conjunction with Tabu Search and Google
Maps to find the shortest route. Rolko and Friedrich [9] presented a new freight transport generation model
using the locations of logistics service providers (LSP). This database contains manufacturers, retailers,
wholesalers, the locations of German LSPs, and transport infrastructure nodes. Pečený et al. [10] proposed
the optimization of transport and logistics processes. This work was based on Vogel’s method of the nearest
neighbor and approximation. The nearest neighbor method simplifies the calculations used to optimize a
single route. The experimental results showed that Vogel’s estimation method is more efficient than the
nearest neighbor method as 3 of the 5 distribution paths are resolved. Kauf [11] proposed a study on smart
logistics for smart city development. The effective management of the infrastructure, the monitoring of
environmental pollution, and the management of the lightning traffic can achieve cost and pollution
reductions. In critical situations, it enables rapid response by utilizing automated analysis and incident
observation, and intelligent alerts.
Stopka et al. [12] focused on the specified transport network as a city logistics scale. This paper
aims to find the shortest path during customer delivery activities. The relevant (receiving and delivery) data
can be displayed in graphs using graph theory. A finite set of vertices and edges represent the roads’
infrastructure in the specific transport network. The results showed a comprehensive optimization in the
transport network, including addressing distributed jobs mathematically above all operational research
methods. Ardakani et al. [13] proposed a multi-door cross-docking system based on truck-to-door
sequencing. The results showed that the heuristic algorithm with SC1 and SC4 compared to the other storage
cost (SC) provided better solutions. The metaheuristic method [14] is a reliable method of obtaining effective
and good-quality answers to be used in various planning in dealing with logistics problems. Metaheuristic
methods are therefore used to solve problems. One of the problems that are often encountered is the problem
of routing transportation routes. Many methods of metaheuristics can be applied in problem-solving such as
the roulette wheel selection method [15], local search method [16], a preliminary answer with constructive
heuristics [17], [18], simulated annealing (SA) [19], and iterated local search (ILS) [20]. The implementation
of each method depends on the nature of the desired answer, aptitude, or user preference.
Therefore, we proposed the development of a transportation scheduling management system to
increase the efficiency of transportation management by enhancing the truck daily scheduling by using the
decision tree technique to support decision-making on how to assign delivery trucks. The first-in-first-out
(FIFO) technique [21] is applied in conjunction with the decision tree to prioritize the loading doors and
delivery trucks after the job has been assigned. In this work, ILS technique is used to suggest routes for
goods transportation and prepare daily reports via the system for users. Efficient truck scheduling
management can help increase the efficiency of goods transportation and will lead to better business
development.
2. METHOD
In this section, we introduce the procedure of our proposed method. The implementation of the
scheduling management system development is shown in Figure 1. The details of operations are described as
follows.
2.1. Master data
The data structure in the transportation truck scheduling management system can be divided into
two parts, including internal and external data. The internal data includes data on loading doors,
transportation trucks, and truck drivers and assistants. The external data, namely transport management
system (TMS) [22], receives data from TMS to be processed in the system such as a list of items to be
delivered.
3. Int J Elec & Comp Eng ISSN: 2088-8708
A transportation scheduling management system using decision tree … (Thittaporn Ganokratanaa)
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Figure 1. System conceptual framework
2.2. Decision tree
The decision tree technique [23] is applied to determine the criteria for assigning the list of goods to
be delivered to the delivery truck, which are shown in Table 1. The selection criteria for the truck assignment
are based on the company’s actual work and a case study related to data of the delivery truck (status of
availability, loading capacity, and transport line). This process includes driver information (performance
evaluation rating) and a data set of goods to be delivered (number, weight, and transport lines). The
procedure is ranked; i) a transport truck is ready for transportation from check-in through the system; ii) the
loading capacity of the truck is sufficient for the weight of the goods to be delivered; iii) the delivery truck is
in a line of transport that corresponds to the line of the goods to be delivered; iv) the employee with the
highest rating will receive the job of delivering the goods first.
Table 1. Good assignments for delivery truck using decision tree technique
Delivery Truck Event: List of goods to be delivered, Transport Line: A1, Weight: 250 kg
Status Loading Transport Line A1 Rating (5 is the highest)
Mr. A Available <=300 KG Yes 4.75
Mr. B Available <=100 KG Yes 5.00
Mr. C Not Available <=500 KG No 4.85
Mr. D Available <=300 KG Yes 4.69
Mr. E Available <=500 KG No 4.52
2.3. FIFO with decision tree for prioritizing the loading door for the transport truck
When a job is assigned to any delivery truck, the next step is to assign a delivery truck to wait for
the goods to be loaded at the loading door. The FIFO in conjunction with the decision tree technique is
applied in this procedure as shown in Figure 2. In this process, the consignment set is assigned to the driver.
It will be queued at the loading door as per the time assigned to such a driver. The organizer has set the
criteria for queuing from the data of loading doors (door status, the number of door queues, and the quantity
of work on each door) and the data set of goods to be delivered (delivery truck and the amount of work).
These can be divided into two cases: i) the delivery truck has no queue at the loading door and ii) there is a
queue at the loading door. In the first case, the delivery truck has no queue at the loading door. This means
that there is only one set of goods to be delivered. The procedure of the criteria for selecting the loading door
for the delivery truck is listed as: i) there is an available loading door, ii) the queue of the delivery truck must
not be found at any previous loading door, and iii) the loading door with the lowest queue must be found and
selected for the loading queue. In the second case, the delivery trunk has a queue at the loading door. It
means that there is more than one set of goods to be delivered. The procedure of the criteria for selecting the
loading door for the delivery truck is listed as i) there is an available loading door and ii) the loading door
with a queue of a delivery truck will be searched. This door will be selected to wait for the loading queue.
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Figure 2. FIFO framework
2.4. ILS for delivery route planning
The system assists in delivery route planning for each delivery truck. It suggests the routing
sequence of the trucks by applying the ILS technique. We assume that there are five stores on one particular
route. The process of ILS for assisting the delivery route planning is described as: i) Google Maps is used as
a tool to measure the distance between each delivery point. In this case, we measure the distance for all five
stores. The distance from one store to another store can be varied as shown in Table 2; ii) To find the shortest
distance, the ILS method is used as shown in a pseudocode in Figure 3. We assume that the initial answer is
𝑆 = 𝑠𝑒𝑞𝑢𝑒𝑛𝑐𝑒 of the store for goods delivery sequence, 𝑍(𝑆) = 𝑡𝑜𝑡𝑎𝑙 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒. Therefore, the initial
answer is 𝑆∗ = 1 − 3 − 4 − 2 − 5 − 1 𝑍(𝑆∗) = 83, which is the shortest total distance; iii) We interfere with the
answer 𝑆∗
for the 1st time with random positions 1 and 2, alternatingly, as shown in Table 3; iv) When the
shortest total distance is obtained, interfere with the answer 𝑆∗
for a second time with random positions 1 and
3 and alternate the positions as shown in Table 4; v) When the shortest total distance is obtained, interfere
with the answer 𝑆∗
for a third time with random positions 2 and 4 and alternate positions as shown in
Table 5. Finally, we observe that the shortest total distance is 83. Thus, the route {3-1-2-5-4-3} represents the
best answer.
Table 2. Distance from one store to another store
Store 1 2 3 4 5
1 0 17 16 19 20
2 17 0 20 15 17
3 15 97 0 18 19
4 16 15 19 0 16
5 17 17 18 15 0
Figure 3. ILS pseudocode
Iterated Local Search
input: starting solution, S 0
input: Local Search procedure, LS
current = LS (S 0)
while stopping criterion not met do
S = perturbation of current based on search history
S * = LS (S )
if S* is accepted as the new current solution then
current = S*
end if
end while
5. Int J Elec & Comp Eng ISSN: 2088-8708
A transportation scheduling management system using decision tree … (Thittaporn Ganokratanaa)
2903
Table 3. Example of distance measurement (1)
The 1st
finding of N(S) by alternating stores with a total of 6 routes
Alternating position Alternating store Route Total distance
(2,3) (3,4) 1-4-3-2-5-1 83
(2,4) (3,2) 1-2-4-3-5-1 87
(2,5) (3,5) 1-5-4-2-3-1 85
(3,4) (4,2) 1-3-2-4-5-1 161
(3,5) (4,5) 1-2-5-2-4-1 83
(4,5) (2,5) 1-3-4-5-2-1 84
Table 4. Example of distance measurement (2)
Alternating position Alternating store Route Total distance
(1,2) (1,3) 3-1-5-2-4-3 86
The 2nd
finding of N(S) by alternating the position of 3-1-5-2-4-3
Alternating position Alternating store Route Total distance
(2,3) (1,5) 3-5-1-2-4-3 87
(2,4) (1,2) 3-2-5-1-4-3 169
(2,5) (1,4) 3-4-5-2-1-3 84
(3,4) (5,2) 3-1-2-5-4-3 83
(3,5) (5,4) 3-1-4-2-5-3 84
(4,5) (2,4) 3-1-5-4-2-3 85
Table 5. Example of distance measurement (3)
Alternating position Alternating store Route Total distance
(2,4) (1,5) 3-5-2-1-4-3 96
The 3rd
finding of N(S) by alternating the position of 3-1-5-2-4-3
Alternating position Alternating store Route Total distance
(2,3) (5,2) 3-2-5-1-4-3 169
(2,4) (5,1) 3-1-2-5-4-3 83
(2,5) (5,4) 3-4-2-1-5-3 88
(3,4) (2,1) 3-5-1-2-4-3 87
(3,5) (2,4) 3-5-4-1-2-3 87
(4,5) (1,4) 3-5-2-4-1-3 83
3. RESULTS AND DISCUSSION
I this section, we present the results and discussion of our proposed method in this section. We
show the decision for the job assigned to the delivery trunk, the sequence of loading doors assigned to the
delivery truck, route planning, delivery goods, and driver assessment. The detail of each procedure is
described below.
3.1. Decision for job assigned to the delivery truck
In this task, the decision tree technique is applied to determine the criteria for assigning the list of
goods to be delivered to the delivery truck. The detail of the job assignment to drivers is shown in Table 6.
The table shows that the system was able to assign the job to the delivery truck according to the specified
conditions based on a random ten sets of the goods delivery. Compared to the results obtained by the
transport operators, it is close to the actual work with a similarity of 0.80. Differently, it comes from the
fact that the system can assign the job more correctly according to the conditions than the staff who
arrange the transport themselves. The table shows that the driver with a high rating receives a job
assignment before the driver with a lower rating. Besides, there is no job assigned in a jumping manner
from one job number to another. The driver with a lower rating received a job assignment whose
frequency is random.
3.2. The sequence of loading doors assigned to the delivery truck
The FIFO technique in conjunction with the decision tree technique can assign the delivery trucks
to wait at the loading door. The queue of delivery trucks at the loading door is shown in Table 7. The
system selects the door according to the specified conditions. For example, in the case of a DC in Bangkok,
there were three sets of goods to be delivered. We found that all sets were queued to load the goods at the
same door rather than loading at others, making it more convenient and faster.
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Table 6. Job assignment to drivers
Goods
delivery No.
Route
Zone
Total
weight
of goods
Results from the system Result from employee Similarity
Driver’s
name
Rating Truck’s
Plate No.
Driver’s
name
Rating Truck’s
Plate No.
0000329372 B13 398.14 Nikhom 5 Bor.Phor-
2171
Nikhom 5 Bor.Phor-
2171
1
0000329374 B15 302.30 Nikhom 5 Bor.Phor-
2171
Phraphan 4.75 Bor.Bor-
6082
0
0000329376 B11 16.28 Chansit 5 Bor.Bor-
6175
Nikhom 5 Bor.Phor-
2171
0
0000371294 DC5 4,356.38 DC5
Songkhla
5 DC5
Songkhla
DC5
Songkhla
5 DC5
Songkhla
1
0000371324 DC1 1,537.48 DC1
Chaing Mai
5 DC1 Chaing
Mai
DC1 Chaing
Mai
5 DC1 Chaing
Mai
1
0000371325 DC2 374.17 DC2
Phitsanulok
5 DC2
Phitsanulok
DC2
Phitsanulok
5 DC2
Phitsanulok
1
0000371327 DC4 603.07 DC4 Surat 5 DC4 Surat DC4 Surat 5 DC4 Surat 1
0000370935 B01 69.66 Thawihiat 5 Bor.Bor.-
6075
Thawihiat 5 Bor.Bor.-
6075
1
0000370938 B04 5.27 Somsak 5 Bor.Bor.-
6076
Somsak 5 Bor.Bor.-
6076
1
0000371126 B04 0.50 Somsak 5 Bor.Bor.-
6076
Somsak 5 Bor.Bor.-
6076
1
0.80
Table 7. Queue of delivery trucks at the loading door
No. Goods
delivery No.
Number of
Invoices
Driver Plate No. Door Door
Queue No.
1 0000370998 17 DC Bangkok DC Bangkok Door 1 1
2 0000370999 13 DC Bangkok DC Bangkok Door 1 2
3 0000371000 24 DC Bangkok DC Bangkok Door 1 3
4 0000371294 48 DC1 Songkhla DC1 Songkhla Door 3 1
5 0000371324 9 DC1 Chaing Mai DC1 Chaing Mai Door 2 1
6 0000371325 9 DC2 Phitsanulok DC2 Phitsanulok Door 3 2
7 0000371327 8 DC4 Surat DC4 Surat Door 2 2
3.3. Route planning for delivery goods
To deliver goods, the ILS technique was applied to suggest the route. The route search results using
ILS for specific answers are shown in Table 8. The best result was Route 1 which had a total distance of
46.7 kilometers, including W101 Warehouse → 1. Prachan Phesat → 2. Phisan Osot → 3. Kledthong Osot →
4. Sirichai Phesat. The results were displayed by pinning them on Google Maps, as shown in Figure 4(a). The
next route search is route 2 with a total distance of 49.1 kilometers, consisting of W101Warehouse →
1. Kledthong Osot → 2. Sirichai Phesat → 3. Phisan Osot → 4. Prachan Phesat. It was displayed by pinning
it on Google Maps, as shown in Figure 4(b). Therefore, the management system for scheduling of freight
vehicles based on the ILS technique can help the driver plan a trip conveniently using the shortest total
distance.
Table 8. Route search results by using iterated local search (ILS)
Distance from each point (KM)
Store W101 Kledthong Osot Sirichai Phesat Phisan Osot Prachan Phesat
W101 0 16.3 16.3 13.3 14.3
Kledthong Osot 19.5 0 0 30.2 31.2
Sirichai Phesat 19.5 0 0 30.2 31.2
Phisan Osot 11.9 27.6 27.6 0 2.6
Prachan Phesat 16.8 34.9 34.9 4.8 0
1.W101 → Prachan Phesat → Phisan Osot → Kledthong Osot → Sirichai Phesat
14.3 KM → 4.8 KM → 27.6 KM → 0 KM
Total distance 46.7 KM
2.W101 → Kledthong Osot → Sirichai Phesat → Phisan Osot → Prachan Phesat
16.3 KM → 0 KM → 30.2 KM → 2.6 KM
Total distance 49.1 KM
7. Int J Elec & Comp Eng ISSN: 2088-8708
A transportation scheduling management system using decision tree … (Thittaporn Ganokratanaa)
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(a) (b)
Figure 4. Pinning on Google Maps (a) route 1 with the total distance of 46.7 km and (b) route 2 with a total
distance of 49.1 km
3.4. Planning the delivery
The system can select the door for the arrangement of transport vehicles due to the conditions set in
the system. Besides, for planning the delivery, the delivery route was suggested using a recursive local search
technique [24], [25] in combination with the use of the Google Maps API service [26]. From the experiment,
we found that the total distance of manual routing from the staff was 3,161 kilometers, while the total
distance of manual routing from the system was 2,451.4 kilometers. The percentage of total mileage was
22.45% shorter than before. The actual trip has a fuel consumption rate of 203.02 liters, and the estimated
fuel cost for delivery is 4,457.55 baht. The total distance traveled from the system has a fuel consumption
rate of 157.44 liters and estimated fuel costs for delivery at 3,456.51 baht. This can be seen that the total
distance traveled from the system has a lower fuel consumption rate than the driver planning trip at
45.58 liters and saves about 1,001.04 baht in fuel costs, as shown in Tables 9 and 10.
Table 9. Summary of total distances of manual and system routing
Shipping summary for the
period 5/1/2021-28/01/2021
Total distance (km) Difference
(km)
Manual routing from staff Routing from the system
Total distance 3,161.00 2,451.4 709.7
Table 10. Summary of fuel consumption rates and fuel estimation of manual and system route arrangements
Shipping
Summary During
the 5/1/2021-
28/01/202
Manual routing Routing from the system Difference
Total
distance
(km)
Fuel
consumption
(liters) 15.57
km/liter
Estimated
delivery
cost (baht)
Total
distance
(km)
Fuel
consumption
(liters) 15.57
km/liter
Estimated
delivery
cost
(baht)
Fuel
consumption
(liters)
Estimated
delivery
cost
(baht)
Total 3,161.00 203.02 4,457.55 2,451.4 157.44 3,456.51 45.58 1,001.04
3.5. Driver assessment
The system has a record of the driver's assessment in case of a shipping error. The scores were
recorded from the assessment. This score is taken as part of the terms of the delivery of the truck.
4. CONCLUSION
This paper introduced the development of a transportation scheduling management system to
increase the efficiency of transportation management by enhancing the truck’s daily scheduling using the
decision tree with FIFO to prioritize the loading doors and delivery trucks. It can suggest routes for goods
transportation and prepare daily reports via the system for users efficiently using ILS. Efficient truck
8. ISSN: 2088-8708
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scheduling management can help increase the efficiency of goods transportation and will lead to better
business development. This paper is beneficial in the case study for the companies to optimize the
performance to be more accurate in the decision-making process. It starts by assigning the work to trucks
following the specified conditions to make the deliveries more efficient and then arranging the loading door
for the delivery truck so that the work process is systematic for both the employee and the driver. This work
can be adjusted in the workflow to use for a shorter time. It also recommends the shortest travel distance to
help drivers plan transportation routes.
ACKNOWLEDGEMENTS
This research was funded by National Science, Research and Innovation Fund (NSRF), King
Mongkut’s University of Technology North Bangkok with Contract no. KMUTNB-FF-66-43, and
Department of Mathematics, King Mongkut’s University of Technology Thonburi.
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BIOGRAPHIES OF AUTHORS
Thittaporn Ganokratanaa received a B.Sc. degree (Hons.) in Media Technology
from King Mongkut’s University of Technology Thonburi, Thailand, in 2015 and the M.Eng.
and Ph.D. degrees in electrical engineering from Chulalongkorn University, Thailand, in 2018
and 2021, respectively. She is a lecturer at the Applied Computer Science, Department of
Mathematics, King Mongkut’s University of Technology Thonburi. She can be contacted at
thittaporn.gan@kmutt.ac.th.
Mahasak Ketcham received the B.B.A. in Business Computer Siam University,
Thailand, M.S.I.Ed. Computer Technology, King Mongkut’s University of Technology North
Bangkok, Thailand, and Ph.D. in Computer Engineering, Chulalongkorn University, Thailand.
Dr. Mahasak Ketcham is an assistant professor in Department of Information Technology
Management, King Mongkut’s University of Technology North Bangkok, Thailand. He can be
contacted at mahasak.k@itd.kmutnb.ac.th.