A new traffic intersection opens and initially eases congestion - only for improved traffic flow to attract more vehicles, eventually recreating the same bottleneck. A street zoned for residential use gradually transforms under commercial pressure. These are familiar patterns in urban planning, and they point to a challenge that goes well beyond designing and building a project: anticipating what will happen around it years later, as populations shift, traffic volumes change, land use evolves and demand for transport and services grows.
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Technology offers a different approach to city management - one built on constructing a digital replica of a city in which a project can be tested before a single shovel hits the ground. Planners can observe what might happen if the population rises, traffic patterns change, or a new development is added nearby, then compare multiple options before committing public funds, selecting a site, or finalising a design.
This concept is known as the city digital twin. It converts data on residents, buildings, roads, services and infrastructure into a simulable digital model, shifting the planning process from simply reading the existing situation to testing future scenarios and attempting to anticipate pressures before they materialise - according to experts in city management and artificial intelligence.
The need for a strong data foundation
Most municipal projects in Jordan focus on traffic-related work, such as building and upgrading intersections, alongside projects such as major parks - all of which can be assessed in advance for likely visitor numbers and activity types, said Fawzi Masaad, a city management expert.
Speaking to Al Ghad, Masaad said traffic intersections may function well when they open, with traffic appearing to move freely - but that over time the improved conditions draw more vehicles, eventually producing denser traffic and renewed congestion.
He said the use of artificial intelligence and modern technologies in planning was highly feasible, but that the main obstacle was the weakness of available data on residents, their movements and related factors, which prevented planners from reaching conclusions they could reliably act on.
Masaad noted that urban planning requires a broad information base - one that extends beyond population figures to include elements such as wind patterns, wind corridors and the effect of buildings on airflow across different areas.
He said that where data is available, digital simulation can reveal residential and urban trends, identify where people prefer to live, and track patterns of economic activity, including the expansion of industrial zones and commercial streets.
He gave examples of streets in Amman that had shifted from residential to commercial use, among them King Abdullah Street - the Medical City area - as well as Mecca Street and Medina Street. In most cases, he said, these transformations were not the product of advance planning but the result of pressures and changes that emerged later.
Masaad stressed the importance of predictive tools in planning, particularly when dealing with future risks, noting that identifying the risks a city faces depends first on having the relevant data. He criticised the insufficient study of social factors when planning major projects - including identifying which groups will use a project, how far they will travel to reach it, how they will get there, and whether the expected demand justifies the proposed scale and location.
On large-scale projects, he emphasised the importance of engaging the public before implementation - not to cancel a project, he said, but to open dialogue and use the results to improve and refine it. Prior public engagement, he added, could also help reduce economic costs and direct spending to where it is most appropriate.
He said digital simulation would help compare building a project at its proposed location against building it elsewhere, with the overall cost of each option calculated.
The challenges of planning for growing cities
Municipalities face a growing challenge in planning for cities experiencing continuous population and urban growth. Population increases mean not only more buildings but also rising demand for roads, transport, services, utilities and infrastructure networks, said Wasfi al-Safadi, an expert in the communications and information technology sector.
Speaking to Al Ghad, Safadi said it was not enough to assess a project solely on its direct impact. Planners must also understand whether the surrounding area and its infrastructure can absorb the expected growth over the coming years.
This, Safadi said, is where the city digital twin comes in - a digital model representing the city, its components and its data, which can be used to simulate projects and changes before they are implemented.
He said that integrating the model with artificial intelligence, geographic information systems (GIS) and building information modelling (BIM) would allow a municipality to move from assessing current conditions to testing future needs and comparing alternatives before investment and implementation. The goal, he said, was not to replace planning expertise but to provide a tool that helps answer a fundamental question: how to plan infrastructure and services today so they can absorb the city's growth tomorrow.
On how artificial intelligence converts city data into a predictive model, Safadi explained that the process begins by gathering and linking core city datasets - population, land use, buildings, roads, transport, services, infrastructure and environmental data. GIS links these datasets to their locations, he said, while BIM provides detailed information on buildings and facilities.
Describing the next stage, he said: "After that, AI analyses historical and current data to discover patterns of growth and demand - such as population and urban growth trends, shifts in density and land use, demand for mobility and services, pressure on infrastructure networks, and areas that need new services or investment."
When a new project or growth forecast is added, the system can simulate the area's needs and compare alternatives - though the results remain estimates that depend on the quality of the data and the assumptions used, he said. He noted that the system can test multiple variables simultaneously, such as population and urban growth alongside changing demand for services, mobility and infrastructure, within a single model.
As an example, he described testing a district expected to see its population rise over ten years, and studying the effect on road network capacity, public transport, schools, health centres, parking, water and energy networks, drainage, public spaces and local services. Different alternatives could then be compared - phased infrastructure expansion, redistribution of services, improvements to the transport network, or a combination of solutions.
Safadi said the value of artificial intelligence lay in its ability to analyse large volumes of data and connect variables that are difficult to analyse together using traditional methods. Among its most important applications in this field, he said, are identifying areas likely to see the highest future growth and demand, detecting potential pressure points on infrastructure, pinpointing anticipated service gaps, analysing the indirect effects of projects, comparing large numbers of alternatives, and studying the cumulative effects of multiple projects.
He cautioned, however, that this approach does not eliminate the planner's role - it frees the planner to focus on interpreting results and selecting appropriate solutions. He warned against relying on incomplete or outdated data, noting that the quality of a model cannot exceed the quality of the data it draws on.
He said outdated population data could lead to an underestimate of future demand, while unupdated infrastructure records could conceal the need for future expansions. He called for data to include its source, date of update, accuracy and completeness levels, and the responsible authority - and said models should display a confidence level in their results, so that decision-makers know not only what the result is but how reliable it is.
If a city becomes a digital replica, he said, it allows the municipality to identify needs before they arise, determine the investments required, and develop projects in phases aligned with expected growth - making planning more proactive, with infrastructure and services developed in step with population and urban expansion.
Safadi cited the experiences of cities including Singapore, Dubai, Helsinki, London, Barcelona and Rotterdam as evidence that value comes not from the digital model alone but from linking it to data, planning processes and decisions. To derive maximum benefit from a city digital twin, he recommended selecting a pilot area with clear population or urban growth, building a unified urban database connecting residents, buildings, roads, transport, services and infrastructure, and adopting a data quality standard that specifies source, update frequency, accuracy and completeness.
He also recommended building an updatable rather than a static model; selecting use cases with a direct impact, such as forecasting road, transport, service and infrastructure needs; identifying alternatives before approving projects and comparing their costs, capacity to absorb growth, and future effects; and linking simulation results to decision-making procedures so they become part of the assessment of major projects. Differences between forecasts and outcomes after implementation, he added, should be used to refine the model.
This article was originally written in Arabic for Al Ghad. It was translated into English with AI assistance and edited by Jordan News.
