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Introduction

 
Artificial Intelligence allows a machine to accomplish tasks traditionally done by humans. Simply put, AI is capable of programming like humans, but it operates on much more powerful hardware. While humans struggle to process even moderate volumes of data in real-time, AI has no such problems. The transportation industry is seeing radical shifts due to the advent of AI. It is already used in various transportation contexts, from facilitating autonomous vehicles to improving traffic flow [1]. In addition to improving our quality of life, technology has the potential to make all forms of transportation safer, cleaner, smarter, and more efficient. One potential benefit of AI-driven autonomous transportation is decreased accidents caused by human mistakes. However, these benefits have significant drawbacks, such as increased vulnerability to cyberattacks and transit options influenced by prejudice. Another aspect that might be impacted is employment, as well as ethical questions regarding who should be held accountable for decisions that are made by robots.
 

The government of Canada is taking action to adjust its regulatory environment to these advances to foster innovation while simultaneously ensuring that basic values and rights are respected. These actions will be taken shortly. Among the actions that have previously been made are broad policies regarding artificial intelligence and guidelines that support technology that enable the deployment of artificial intelligence in transportation. In addition, the government of Canada offers financial assistance, particularly for endeavours related to research. This research dissects and discusses the psychological effects of artificial intelligence in transportation technology.
 

Literature Review

 
Artificial Intelligence (AI) gives robots human intelligence to automate manual jobs and learn in the field. Artificial intelligence (AI)-powered systems learn over time, allowing them to ultimately carry out vital activities and make choices on their own. Because of this unprecedented opportunity, transportation companies have begun investing in AI to boost profits and maintain a competitive edge. The transportation sector has recently started using AI for mission-critical operations, raising concerns about its dependability and safety. There's a lot of room for AI advancement in the transportation sector, namely regarding solving pressing problems like safety, capacity, pollution, dependability, etc.
 

According to [2], machine learning, essentially a subfield of artificial intelligence, has been around for a while. Gupta illustrates how crucial machine learning technology is in simplifying the complicated logistics process, particularly in delivering consumer products and handling sensitive items. This is especially true in the case of handling sensitive goods. Transportation includes weighing, packing, and scheduling, which may be time-consuming even before the entire transport operation starts. Machine learning allows the manipulation of procedures to be automatically generated on robots and other technical equipment without any need for human interaction or decreased workforce while simultaneously achieving error-free results. The term "machine learning" refers to the technology used to teach computers to carry out tasks that people were previously capable of, but with greater precision and a lower mistake rate.  According to [3], ensuring that artificial intelligence is very wide and has the potential to take over all sectors soon; thus, the only way to be secure is to keep ahead of the pack in technological problems.
 

According to [4], discuss the logistical problems that can be solved by implementing AI into a company's processes. Studies focus on examples that demonstrate AI's value, as well as academics who have researched the topic throughout time and offered advice based on developments in the field. It has been argued that defining the origins of the term "Artificial Intelligence" is the first step in comprehending the nature and operation of AI. That's a word for explaining how the tech connects to our minds. With the aid of sophisticated programmes, computers can mimic human cognition in specific contexts and do complex tasks. They outperform humans in every manner, including precision of outcomes, since they never fatigue and can be operated indefinitely without pausing for rest. Therefore, it is undeniable that AI is superior to the human brain at performing crucial tasks in the corporate sector [5]. It illustrates how artificial intelligence helps organizations, particularly in the transport and logistics sectors. Using this technology in the transport department may help businesses save time and money by reducing the need for human labour and storage space, allowing for the rapid sorting and fast shipping of products. The massive amounts of data processing required for both intelligent response and operation are the cost for businesses to pay to benefit from AI.
 

According to [6], explains how artificial intelligence has been used in transportation to simplify things via automated systems such as customer relationship management (CRM). With the help of AI, businesses may streamline their internal communications by centralizing all of their correspondence in one easily accessible location. From automated answers to robotically executed communication processes, this method improves machine-human interaction and, by extension, productivity. The technical scope of artificial intelligence (AI) is enormous, and it finds widespread application in many forms of discourse. The article [7], explains how AI has facilitated better communication and assisted in content production in messaging and personalized customer experience through pattern and character learning, reducing the exposure and vulnerability in the transportation field and other online threats on the networks.
 

Current Challenges

 
The use of technology has caused a significant major shift in the transportation industry in recent decades. This industry has seen significant development since the first automobiles were manufactured up to the autonomous cars that are now being researched and used today. Autonomous cars use algorithms based on artificial intelligence to perform various tasks that humans, such as navigation, traditionally carry out. Tesla and Ford are just two of the many automobile manufacturers that have recently begun to experiment with artificial intelligence (AI) [8]. In addition to these businesses, others, such as Uber, a worldwide taxi firm that enables customers to request trips from various regions of the world, have made great progress in implementing AI technology. Numerous businesses located all over the world have put a variety of AI algorithms through their paces in the process of developing and deploying autonomous cars. These algorithms have been tested and implemented. Over many years, research and development have been conducted on these algorithms to improve navigational features to facilitate transit between various locations. Light detection and ranging are examples of some of those AI technologies (LiDAR). As the name suggests, it combines a variety of characteristics, one of which is the detection of light by lidar systems. At the same time, another feature is the determination of distance through the application of radar technology. The second well-known detection method utilizes a system that combines radars, ultrasound, and 2D camera components [8]. Because of the disparity between the costs of these technologies and the Imagefunctions they provide, there has been a significant amount of discussion on their overall efficacy.
 
 
Figure: Tesla AI system [16]
 
Compared to 2D camera gadgets, radar, and ultrasonic, the cost of using a LiDAR algorithm is higher. Since the former uses more costly components to acquire or create, it is more expensive overall. According to [9] Elon Musk, CEO and creator of Tesla Motors, claims that using 2D cameras, radars, and ultrasonic is more practical and cost-effective for mass manufacturing. Successful and thoroughly validated LiDAR algorithms are preferred by most businesses, despite the higher cost of using them in mass-produced autonomous cars. Even so, these innovations help autonomous cars see and avoid hazards on the road. This demonstrates how AI may successfully mimic the functions and skills of human drivers, improving security. Autonomous cars use artificial intelligence for more than just navigation it also helps them spot nearby people [10]. With supervised learning, machine learning, and artificial intelligence, businesses have increased the effectiveness of autonomous cars on the road, making them safer for everyone. For instance, Waymo uses deep neural networks (DNN) to improve the quality of sensor data production. With this information, autonomous vehicles may behave more like human drivers, taking all necessary safety measures while on the road. After millions of kilometres of testing, DNN has proved to be a useful tool for Waymo [11]. This system can scan road signals and traffic lights and analyze tough scenarios, such as making way for ambulances.
 

Feasible Technology

The economic losses caused by traffic congestion are compounded by the fact that it also raises global carbon emission levels. Allowing AI to control traffic flow will drastically reduce congestion and pollution. Existing sustainable practices include the use of smart traffic lights and real-time monitoring to manage peak and off-peak traffic volumes [12]. It may also be used in public transportation to better plan trips.
 

AI may help law enforcement agencies maintain a more accurate real-time crime database. With this data in hand, police may boost their effectiveness and contribute to public safety by increasing the frequency and effectiveness of their patrols. As a result of AI, transportation in the commercial sector has undergone a sea change. Previously, there were many obstacles caused by the inefficiency of utilizing manual techniques to organize transportation systems and logistical operations; however, with the advent of this new technology, everything is now automated with much improved prospects and lower costs. For instance, AI is used to automate the labelling, accounting, and processing of shipment data such that no human workers in the existing warehouse are required. Robots and machines scan specially printed codes on all incoming packages to determine the product's category, country of origin, and final mailing address.
 

Instead of having people read each package label individually, interpret the details, and then transmit the data to a computer system for processing, an automated system can handle all this with a single click and scan. In the transport and logistics sector, AI helps with recordkeeping, inventory, and other aspects like load capacity since this data are kept and promptly made accessible to all necessary people [13]. It simplifies preparation, lessens the possibility of mistakes, and lowers the expense of implementation. In addition to keeping track of inventory, warehouse space utilization, relocation, and scheduling are all kept automatically organized. Better efficiency and performance ultimately lead to the trustworthiness and expanded clientele.
 

Figure: Ai model of a Transportation system
 
Using AI in transportation and logistics also allows for an automatic tracking and relay system. Hence, businesses always know where their items are and can plan without worrying about catastrophic mistakes like lost deliveries or excessive delays. Airlines and other carriers also employ this approach to ensure the safety and simplicity of handling their cargo [15]. It is indeed simple to access boards in transit if anything has to be done, reducing the need to filter through goods to find the appropriate product physically.
 

Cloud technology is another way and instrument used to secure communication and improve effective communication inside a transportation system and with its stakeholders. With cloud computing, employees don't have to be physically present at the office to access vital corporate resources remotely and on demand. When just a few quick steps are required, having workers complete those steps online rather than making the trip to the office saves time and improves dependability. Multiple layers of security prevent malicious actors from disrupting online services. These systems can intelligently identify and investigate suspicious behaviour and then take corrective action to prevent further damage to the network and the loss of sensitive data. This multi-layer security technology is integrated into the company's Artificial Intelligence infrastructure to protect critical information through encrypted messaging.
 

Conclusion

 
The use of AI helps achieve improvements in the psychological effect of technological advancements in the transportation sector. Increasing AI's beneficial impact on transportation can be accomplished healthily by improving the working environment. Consumers are gaining a greater awareness of transportation's negative consequences on the environment, and it is becoming harder to refute the repercussions of pollution. As a consequence of this, a significant number of customers as well as communities are interested in environmentally friendly methods of transportation. The transportation sector is one of the many disrupted by artificial intelligence. Artificial intelligence is the engine that drives self-driving cars, which have the potential to cut down on the number of automobile accidents significantly. There are various applications for artificial intelligence in the transport industry, including traffic management, environmentally friendly transportation, fleet connectivity, and crewless cargo ships.
 

References

 
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Hou, Jinling. "Application of New Generation Artificial Intelligence in Traffic Informatization." In Journal of Physics: Conference Series, vol. 1881, no. 2, p. 022070. IOP Publishing, 2021.
 
 
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Wang, Fei. "Application of artificial intelligence-based video image processing technology in security industry." In International Conference on Cloud Computing, Internet of Things, and Computer Applications (CICA 2022), vol. 12303, pp. 323-327. SPIE, 2022.
 
 
Merrefield, Clark. Transportation in the Age of Artificial Intelligence and Predictive Analytics. No. DOT-VNTSC-19-01. John A. Volpe National Transportation Systems Center (US), 2019.
 
 
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