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The Role Of Competitive Intelligence In Sustainable Travel And Responsible Tourism Initiatives
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By Rasul Abdul Jabbar Rasul Abdul Jabbar Skillet Priprits
The Future Of Events In 2023 — Pacific Asia Travel Association
Received: 4 November 2018 / Updated: 18 December 2018 / Accepted: 24 December 2018 / Published: 2 January 2019
The rapid development of Artificial Intelligence (AI) offers unprecedented opportunities for the development of various industrial and commercial activities, including the transportation sector. Recent innovations in AI include advanced computational methods that mimic the workings of the human brain. The application of AI in the field of transportation aims to overcome the challenges of increasing travel demand.
Emissions, safety risks and environmental degradation. Considering the presence of large numbers of statistics and AI in this digital age, it has become more appropriate to address these concerns in a more effective and efficient manner. Examples of AI methods that are finding their way into the logistics field include Artificial Neural Networks (ANN), Genetic Algorithm (GA), Simulated Annealing (SA), Artificial Immune System (AIS), Ant Colony Optimiser (ACO) and Professional Bee. (BCO) and Fuzzy Logic Model (FLM) Successful implementation of AI requires a good understanding of the relationship between AI and data on the one hand and the behavior of transport systems and variables on the other. In addition, it is promising that transportation authorities can use these technologies for rapid development to avoid congestion, make travel times safer for their customers, and improve the economy and provide their valuable assets. This article provides an overview of AI techniques used around the world to solve transportation problems, especially in traffic management, traffic safety, public transportation, and urban mobility. The paper concludes by addressing the challenges and limitations of AI applications in transportation.
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Artificial Intelligence; genetic algorithms; simulated annealing; Temporary immune system: Ant Colony Facilitator; Professional beekeeping; public transport; Motor vehicle mobility; Traffic control
Artificial Intelligence (AI) is a branch of computer science that makes machines work like human brains. It is used to solve problems that are difficult to explain using traditional mathematical techniques. AI was first discovered in 1956 by John McCarthy, but it failed to achieve its goals  and the lack of new technologies made it promising. From the 1960s to the 1970s, researchers explored AI with knowledge bases (KBS) and artificial neural networks (ANNs) . KBS systems are computers that make recommendations using rules based on knowledge provided by humans. On the other hand, ANNs are neural networks organized in different ways, modeled by the human brain and used in medicine, biology and language translation in engineering, law, industry, etc. [2,3]. During this period, interest in AI declined until the 1980s due to limited applications of ANNs and lack of data. Since the 1980s, many studies have been done using a method called gradient descent to reduce the prediction error. This technique is called Backpropagation Algorithm to train ANN and it has been used to solve problems in different domains using few hidden layers. Today, data mining introduces the concept of machine learning as a subset of AI. Machine learning means making computers act like human brains rather than teaching them anything. To solve complex problems, it allows computers to access large amounts of data and extract important information from them [7, 8].
ANN is a very versatile AI technique used in a variety of applications. One of the first and most common types of ANNs is the Feedforward Neural Network, where data is passed from an input layer to a hidden layer to an output layer. Other types of ANNs are convolutional neural network (CNN) [9, 10, 11] and recurrent neural network [12, 13, 14]. CNN works best for image processing tasks while RNN performs input data classification suitable for many applications; Language, text and text recognition. They are often referred to as deep learning technologies because of the hidden layers built into their architecture.
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There are many uncertainties and gaps in the data that cannot be resolved using traditional techniques. Therefore, AI uses those uncertainties and models the relationship between cause and effect of various life situations by combining the available data with assumptions and better analysis .
Transportation problems become challenging when the system and user behavior are difficult to estimate and predict travel conditions. Therefore, AI is considered suitable for transportation systems to overcome the challenges of increasing travel demand, CO
Emissions, safety risks and environmental degradation. These challenges are caused by population growth due to the expansion of rural and urban traffic, especially in developing countries. As the population grows to 30 million by 2031, the cost of congestion in Australia is expected to reach 53.3 billion. In Melbourne, Australia alone, there are over 640 kilometers of arterial roads in CO
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2.9 tons of production per year . In the 21st century, many researchers strive to find an efficient method of transportation that has less impact on people and the environment using cost-effective and reliable AI techniques. It has potential applications for road infrastructure, drivers, road users and vehicles.
AI applications in transportation are being developed and implemented in various ways. Among these, this research paper aims to highlight three main examples. (i) Use of AI in corporate decision-making, planning and management; This is essential to meet the continuous growth in demand along with road access. This includes the effective use of advanced forecasting and diagnostic models aimed at better predicting traffic rates, traffic conditions and incidents. (ii) AI applications aimed at improving public transport are also discussed. Because public transport is considered a sustainable mode of mobility. (iii) An exciting future application in transportation involves autonomous vehicles, which reduce the number of accidents on highways and increase productivity. Self-driving car and mini-autonomous bus trials launched in Finland, Singapore, and China are explored in this article.
The rest of this article is divided as follows: Part 2 explains the application of AI in transportation. This section is divided into AI applications for planning, design and decision making, public transport, intelligent self-driving cars. It also features real-time incident detection and future traffic forecasting. Part 3 shows that the future of AI is focused on deep learning. Section 4 describes the future research work of the authors, while Section 5 presents the conclusion of this paper.
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In many cases, it is difficult to fully understand the relationship between the characteristics of the transport system; Therefore, AI methods can be presented as smart solutions for such complex systems, which cannot be controlled using traditional methods. Many researchers have shown the benefits of AI in transportation. An example of this includes turning traffic sensors on the road into a smart agent that automatically detects accidents and predicts future traffic conditions . There are also many AI methods in transportation such as ANN. ANN for road planning , public transportation (20, 21) traffic accident detection [22, 23, 24, 25]; and predicting traffic conditions [26, 27, 28, 29, 30, 31, 32, 33]. It is divided into unsupervised learning methods. Care methods include support
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