AI predicts fleet failures and fuel use

Mehdi Arabzadeh, a technology expert at a knowledge-based company, said three knowledge-based companies in Hamadan, Tehran and Kish Free Zone are collaborating on AI projects focused on transport fleets, logistics and navigation systems.

He said the group's latest project is based on "organizational intelligence," aiming to convert the knowledge and experience of employees and experts into an accessible organizational intelligence system.

For example, an expert working in a remote part of Iran may have valuable knowledge gained through years of experience. Under traditional methods, such expertise would typically be transferred verbally to another employee. Under the new approach, data derived from employees' knowledge and experience is collected and converted into organizational intelligence.

This would allow a newly recruited employee with only one month of experience to benefit from the knowledge of an expert with 28 or 29 years of professional experience, Arabzadeh said.

He added that the system can generate reports for managers and different organizational departments to support decision-making. A manager, for example, can ask which parts of the organization face the most problems or request possible solutions to a specific problem.

According to Arabzadeh, the proposed solutions are based on data collected from employees and organizational experts.

He also said the technology can support organizational planning by analyzing previous years' data. For example, managers can use the system to estimate the budget required for the following year and identify necessary actions from the beginning of the year.

Arabzadeh said the group is working to convert the knowledge and experience available within organizations into an organizational intelligence structure and make it accessible to managers and different departments.

He added that negotiations and preliminary steps had begun with several organizations, including Iran's Administrative and Employment Organization, although the cooperation had not yet resulted in a contract.

Referring to other projects, Arabzadeh said the group develops both hardware and software and provides its software to organizations and government agencies.

He explained that GPS and navigation data transmitted to the fleet monitoring system can be analyzed to provide information on various aspects of fleet performance.

For example, data from a vehicle can be used to predict potential future failures, travel distance, stopping time and fuel consumption. The system can determine how far a vehicle has traveled during a day and how much time it has spent stopped during a week, then use this information to forecast its fuel consumption for the following month.

"The system can, for example, predict that a vehicle may consume around 40 liters of fuel in the following month," Arabzadeh said.

He said such analysis and forecasting are among the applications of AI-based intelligent transport fleet management.

Arabzadeh also referred to the group's activities in smart city development, including an intelligent transport fleet project implemented on Kish Island in 2020, which is currently suspended.

Under the project, the number of passengers waiting at bus stops was counted and the information was transmitted to drivers. This enabled drivers to determine how many passengers were waiting at each stop and plan their routes accordingly, potentially reducing travel time and fuel consumption.

He said citizen applications were also developed as part of the project, allowing passengers to evaluate drivers and vehicle conditions, including cleanliness and heating or cooling systems.

Arabzadeh added that a similar project was implemented in Hamadan but was partially suspended because of administrative issues and bureaucracy.

During the project, nearly 180 buses were equipped with GPS systems, while fleet inspection and management systems were also provided.

The project also included a citizen application and urban variable message signs (VMS) displaying the expected arrival times of buses.