Application framework of Artificial Intelligence (AI) in the

Writer： admin Time：2021-09-23 10:00 Browse：℃

Application framework of Artificial Intelligence (AI) in the field of Intelligent Transportation System (ITS)
 
It can be seen from the early research on the topic of ITS, that the effectiveness of artificial intelligence in the construction of intelligent transportation systems has not been fully explored. Current research explores ITS applications in the transportation industry deployed in different countries.
As the lifeline of the economy, the transportation industry seems to be struggling to solve various operational problems on a global scale. Problems related to the transportation industry have slowed down the progress of a city and even a country. TMS is good news, it is a system that can use various technologies to overcome transportation problems.
The main functions of TMS include route determination, outbound/inbound logistics processes, route scheduling, third-party logistics provider services, freight forwarders, service agents, transportation tracking and batch processing of route scheduling and transportation plans (Figure 1). It can be seen that the functions related to TMS are related to cargo transportation. TMS integrates multiple transportation applications into one package for better use.
 

Figure 1 Functions of TMS (Source: ValuecConsulting, 2013)
Use AI and machine learning to make TMS smarter to provide accurate predictions. Some of the technologies currently in use include: Internet of Things (IoT) devices and sensors, digital assistants, delivery time forecasting solutions, transportation planning solutions, blockchain, etc. Intelligent Transportation System (ITS) is developed from TMS. A system that can make appropriate decisions based on a given scenario using data generated by equipment installed on a vehicle is called an Intelligent Transportation System (ITS). Past studies have shown that the comprehensive approach to ITS includes transportation infrastructure and transportation management. As a new type of TMS, ITS has been gradually replaced by automated control systems. They have developed into the prediction of dangerous situations, and it is possible to use a large amount of complex data as a decision-making tool.
The intelligent transportation system consists of public transportation, traffic information, parking management, traffic management and control, safety management and emergency response, and road management (Figure 2). This is unique to smart cities (Agarwalet al., 2015) [3]. In order to build an effective smart city through ITS, it is important to build system capabilities into the various operational activities of the city. As shown in Figure 2, some of the activities in the city include public transportation, traffic management, parking management, pavement management, and safety management. Through ITS, commuters, pedestrians, transportation and society at large benefit.
 
Figure 2 Development of various subsystems of intelligent transportation system (Source: Agarwal et al., 2015)
The research conducted by Hamida et al. in 2015 divided various applications of intelligent transportation systems into four main categories, as shown in Figure 3.
Infotainment and comfort;
Traffic management
Road traffic safety;
Autopilot.
 
 
Figure 3 Classification of ITS applications (Source: Hamida et al., 2015)
These applications collect data from vehicles to improve their utility, thereby ensuring driver safety and enhancing public transportation facilities. The ITS application is a generator of data, which in turn contributes to the decision-making process of the management department to better manage public places. Some of these applications are related to passenger comfort, improved driver experience, and efficient road management. The ultimate beneficiaries of the public transportation system are road users. The Intelligent Transportation System (ITS) framework of a sustainable public transportation system considers ICT technology, automated transportation systems, transportation management centers, and advanced passenger information systems [60]. The framework given in Figure 4 is divided into four stages, starting with road users as the data source, and achieving ultimate economic growth through ITS. Applications built around transportation systems need to keep in mind the beneficiaries of the generated data. Once ICTs are used to build applications, they can not only improve process efficiency, but also contribute to the sustainability of the transportation system, leading to better economic growth.
 
Figure 4 Implementation of the ITS framework for the public transportation system (Source: Abijede O [55])
Some applications built with ITS can ensure traffic management, traffic signal control, vehicle navigation systems, intelligent parking management, etc. ITS needs a technology network that operates across urban infrastructures (Shaheen & Finson, 2019). The categories of ITS issues discussed include performance monitoring, traffic management, improved transportation processes, information support for traffic participants, and transportation infrastructure management. ITS follows a systematic approach. It is logical that current research treats the various subsystems of ITS as a category to explore the effectiveness of artificial intelligence solutions.
QCIT Intelligent Transportation System AI solutions, the contribution of artificial intelligence to the transportation industry is huge and extensive. These solutions include self-driving cars, traffic management, route optimization and logistics to provide safety for vehicles and drivers. ITS is constructed using AI technology using data generated by equipment installed on the vehicle. The current research focuses on four subsystems related to transportation-namely, intelligent transportation management system, intelligent public transportation system, intelligent safety management system, and intelligent manufacturing and logistics system.