Despite the expansion of the use of bus tracking systems, electronic payment, surveillance cameras, and control centers in Jordan’s transport sector in recent years, experts say these tools essentially represent an initial digital infrastructure for the sector and do not necessarily mean that Jordanian transport has entered the stage of integrated and mature use of artificial intelligence (AI).
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Experts stress that the real transition from digitalization to AI begins when available data is transformed from mere operational records and information into tools capable of forecasting demand, congestion, breakdowns, and accidents.
The results of such analysis can then be used to improve bus allocation, scheduling, operational efficiency, and decision-making, alongside clear safeguards to protect passenger and vehicle data.
This transition is particularly important given that the transport sector has been identified as one of the priority sectors for applied projects under the implementation plan of Jordan’s National Artificial Intelligence Strategy 2023–2027.
The strategy focuses on capacity building, supporting research and investment, and developing the legislative and regulatory environment needed for the use of AI technologies.
Digitalization Paves the Way for Artificial Intelligence
Former Minister of Transport Dr. Lina Shbeeb said Jordan has gradually begun implementing intelligent transport systems at an initial level through electronic fare collection and the provision of route and service information through digital systems and navigation platforms.
These tools have helped passengers identify available services and plan their journeys.
Shbeeb explained that these applications provide a foundation for moving toward a more advanced stage involving more accurate tracking and analysis of trips, demand levels, and adherence to schedules. She said this process should begin within cities before gradually expanding to travel between urban areas.
According to experts, having payment, tracking, monitoring, and trip-data collection systems does not automatically constitute the use of AI. Rather, it represents a necessary stage in providing the data that models and algorithms require for learning, analysis, and forecasting.
From Monitoring Buses to Predicting What Will Happen
Technology expert Wasfi Al-Safadi said Jordan’s transport sector is at an important stage of digital transformation but has not yet reached mature AI adoption.
He explained that tracking and monitoring systems, electronic payment, and the collection of trip data provide the basic material for analysis, but do not in themselves represent advanced use of the technology.
The real difference, he said, lies in a system’s ability to learn from data and use it to forecast demand, congestion, and breakdowns and improve bus allocation, rather than simply monitoring vehicles and displaying their locations electronically.
Al-Safadi believes the economic and operational value of AI emerges when accumulated data is transformed into actual operational decisions.
Authorities could then determine where and when bus frequencies need to be increased, which trips can be reduced, and how to respond to changing demand and traffic conditions before they affect service quality.
Demand Forecasting Among Public Transport’s Top Priorities
Experts differed in their ranking of AI priorities according to the sector’s needs and its current stage of development.
Al-Safadi believes the starting point should be demand forecasting and improving public transport operations by analyzing passenger data, peak hours, holidays, and traffic conditions.
Such analysis could help increase bus frequency on routes experiencing high demand and reduce unnecessary trips on routes with limited demand, improving fleet efficiency and reducing operational waste.
Demand forecasting would not only affect bus allocation but could also help redesign schedules and determine operating hours, reducing passenger waiting times, improving the public transport experience, and lowering operating costs.
Safety and Predictive Maintenance: Applications Closer to Reality
Shbeeb, meanwhile, considers safety and predictive maintenance to be areas that are closer to practical implementation at the current stage, given their reliance on operational data related to vehicle conditions and patterns of breakdowns.
She said analyzing such data could enable a shift from maintenance performed after a breakdown or according to fixed schedules to proactive maintenance based on actual indicators of vehicle performance and the likelihood of failure.
The next step could then be broader demand forecasting aimed at improving schedules, reducing waiting times, and increasing the efficiency of public transport routes.
Engineer Khaled Haddadin agrees on the priority of proactive road safety through the integration of accident, speed, traffic, weather, and road-design data.
This could help identify dangerous locations and detect unsafe behavior and maneuvers before they turn into accidents.
Haddadin also pointed to the potential use of AI to forecast demand on routes, address bus bunching and gaps between buses, and improve the accuracy of arrival times.
Image-analysis technologies could also be used to detect potholes and cracks in roads, while vehicle data could be analyzed to predict breakdowns before they occur, reducing the likelihood of buses stopping during service and improving safety and reliability.
Congestion Management Requires Real-Time Data Integration
Shbeeb considers congestion management one of the more complex applications because it requires real-time integration of traffic-light data, accidents, roadworks, and traffic capacity, as well as rapid analytical capabilities that allow decisions to be made at the right time.
For this reason, she said these technologies could initially be applied to specific traffic corridors before gradually expanding across the wider road network.
Haddadin said the Greater Amman Municipality’s Traffic Control Center uses the SCATS adaptive traffic-control system to coordinate signals according to traffic volumes.
However, he believes the system’s capabilities need to be leveraged more extensively and integrated with accident and congestion management systems, while giving greater priority to public transport and emergency vehicles.
Developing traffic management in this way would go beyond simply regulating traffic signals and could lead to a more integrated system capable of responding to changes in vehicle movement and adjusting priorities according to prevailing traffic conditions.
AI: Predicting Congestion Before It Happens
Smart-city and digital-economy developer Dr. Marwa bint Salman Al-Salah said the real value of AI does not lie in informing road users about congestion after it has occurred, but in the ability to predict congestion before it develops.
She explained that this requires linking traffic-flow data with accidents, weather, events, and vehicle movements, enabling control centers to intervene early and take preventive measures before problems worsen.
Al-Salah noted that congestion is not merely a traffic problem. It also carries direct and indirect economic costs, including lost time and productivity, higher fuel consumption, increased pressure on vehicles, and greater emissions.
Traffic forecasting can therefore have a direct impact on urban management by reducing congestion-related waste and improving the use of existing infrastructure.
Transport Projects Expand the Digital Data Base
Existing and planned transport projects demonstrate the expansion of the sector’s digital infrastructure.
Among them is the regular intercity transport project, which relies on fixed schedules and timetables, alongside electronic payment and tracking and surveillance cameras inside buses.
These systems enable the collection of trip data and monitoring of service regularity, providing a foundation for improving operations, planning routes, and evaluating the quality of services provided to passengers.
In addition, there is a project to equip 376 medium-sized buses operating on 29 routes and serving six public universities with electronic payment, GPS tracking, cameras, passenger information screens, and a financial clearing system.
The Land Transport Regulatory Commission announced that implementation of the project would begin during the third quarter of this year, adding further operational data to the public transport digital ecosystem.
These projects are important from an AI perspective not only because of the technologies they use, but also because of the volume of data they can generate regarding bus and passenger movements, operating times, vehicle locations, and service regularity.
Plans to Develop Smart Platforms for Planning, Maintenance and Safety
In a development directly related to AI applications, the Land Transport Regulatory Commission announced, through a memorandum of understanding with Al Hussein Technical University, plans to develop platforms for trip planning and tracking buses and trucks.
The plans also include developing smart maintenance and safety-monitoring systems, as well as analyzing data to forecast demand and improve the geographic distribution of transport services.
This represents a shift from using data for monitoring and oversight purposes toward employing it to support decision-making one of the key differences between traditional digital transformation and AI-driven transformation.
The Need for a Unified National Transport Data Platform
Regarding the infrastructure needed to expand AI use, Al-Salah said Jordan does not need more isolated applications as much as it needs a national transport data infrastructure linking information on vehicles, buses, roads, traffic signals, accidents, and demand according to unified and secure standards.
She stressed that a citizen’s journey is a single journey even when responsibility for it is divided among several institutions.
Therefore, data should be integrated into one system that does not merely record what happened but learns from it to predict what will happen and support timely decision-making.
This is consistent with Shbeeb’s call for a shared platform connecting fare, trip, mapping, operations, maintenance, and traffic data, with models tested before being deployed on a wider scale.
The challenge, therefore, is not simply having AI technologies, but the ability of different institutions to exchange data, standardize it, and connect it within an integrated system that can be relied upon for planning and operations.
As data use expands, however, challenges related to privacy and the protection of personal information also emerge.
Al-Salah warned of the risks of location-data leaks or the re-identification of users when multiple data sources are combined.
She also highlighted the possibility of models becoming biased toward one area or route over another because of insufficient data or weak collection mechanisms.
She noted that transport data is not merely operational information. Repeated location patterns can reveal where people live and work and when they travel, becoming even more sensitive when linked to payment information or phone numbers.
She called for the implementation of the principle of “privacy by design” by limiting data collection to what is necessary, separating user identities from operational information, defining access permissions, and establishing clear accountability mechanisms for the use of algorithms.
She stressed that improving traffic management should not turn into open-ended surveillance of society, making data protection a fundamental component of intelligent transport-system design rather than a measure introduced after the system becomes operational.
On the legislative front, Jordan’s Personal Data Protection Law No. 24 of 2023 provides a general framework for dealing with personal data.
However, experts believe that the expansion of AI applications in transport requires greater clarity regarding ownership of transport data, the purposes for which it is used, data-retention periods, authorized access, and ensuring human oversight of sensitive decisions generated by intelligent systems.
These safeguards become increasingly important as the sector moves from simply collecting data to using it in decisions that directly affect citizens’ mobility, service distribution, and operational priorities.
Measuring Impact: No Ready-Made Success Rates
Regarding measuring outcomes, Al-Salah cautioned against presenting ready-made percentages for AI’s ability to reduce travel times or operating costs in Jordan without conducting actual trials and measuring performance before and after implementation.
She said any figures not based on local results would be closer to promotion than analysis, particularly because the outcomes of AI applications vary depending on the nature of the traffic network, data quality, mobility patterns, infrastructure, and the level of integration between institutions.
She explained that impact measurement should rely on a set of indicators, including average journey time, passenger waiting time, bus schedule adherence, fuel consumption, number of breakdowns, downtime, and operating cost per trip.
Ultimately, experts believe the success of AI in Jordan’s transport sector should not be measured merely by the number of systems and applications installed, but by their ability to actually improve services and transform data into more accurate operational decisions.
Al-Salah stressed that the most important outcome for citizens is restoring confidence in public transport by knowing when a bus will arrive and whether it will actually be available at the required time and location.
The next stage for Jordan’s transport sector, therefore, is not simply about adding more cameras, tracking devices, or electronic payment applications. It is about moving toward a system capable of predicting, learning, and responding, making data a tool for improving services before problems occur rather than merely recording what happened afterward.
The success of this transformation appears to depend on three parallel tracks: developing digital infrastructure and integrating data; building AI applications linked to the sector’s actual transport needs; and establishing clear frameworks for data governance and privacy protection.
Once these tracks are fully developed, the current digitalization of Jordan’s transport sector can evolve from a technological infrastructure into a more efficient, safer, and more responsive transport system that better meets citizens’ needs.
