New GeoAI Framework Helps Cities Forecast and Map Future Traffic Congestion

Traffic congestion has long been treated as a problem that cities measure only after it has already formed, a phenomenon to be counted, charted and complained about rather than anticipated. A team of researchers at New York University’s Tandon School of Engineering is now arguing for a fundamentally different posture: giving urban planners tools that can anticipate congestion before it materializes, pinpoint where it is most likely to emerge, and translate dense, technical traffic models into plain-language guidance that a planning department can actually use. The work, led by Anton Rozhkov with contributions from Pranav Nitin Motarwar and Rudra Patil, all affiliated with NYU Tandon, appears in the special issue of Transactions in GIS devoted to ethical and explainable GeoAI, a growing subfield concerned with making geospatial artificial intelligence transparent, accountable and useful to the public institutions that increasingly depend on it.

At the heart of the project is a geospatial artificial intelligence, or GeoAI, framework that weaves together three components that are usually handled separately: traffic forecasting, spatial mapping and a large language model. The forecasting engine projects future traffic volumes from historical observations. The spatial engine maps those projections onto a hierarchical grid so that congestion can be examined at scales ranging from entire boroughs down to individual corridors and hotspots. The conversational layer, built on a large language model hosted locally rather than in the cloud, sits on top of both, allowing a planner to pose ordinary questions and receive answers grounded in the city’s own data. The system was tested using fifteen years of New York City traffic data, a dataset of unusual depth made possible by the city’s extensive open transportation data programs.

The motivation, Rozhkov explains, came partly from conversations with urban planning practitioners. Cities increasingly want to incorporate artificial intelligence into their daily work, but planning departments often lack the specialized technical expertise needed to build and maintain custom systems. There is also a deeper institutional reluctance at play: transportation data can be sensitive, revealing patterns about infrastructure, commerce and movement that agencies may not wish to hand over to commercial models and chatbots. That combination of aspiration and caution shaped the project from the start. The team set out to build a platform that could be hosted entirely on local infrastructure, secure enough for sensitive data, intuitive enough for planners to use comfortably in day-to-day work, and powerful enough to justify the effort of adoption.

Traffic proved an ideal test case, and New York an ideal proving ground. The researchers analyzed traffic observations spanning 2009 through 2024 and benchmarked two forecasting methods against each other. The first was ARIMA, or AutoRegressive Integrated Moving Average, a conventional statistical forecasting technique that has anchored time-series analysis for decades. ARIMA models excel at capturing seasonality, the regular weekly and annual rhythms that make rush hours predictable. The second was a Long Short-Term Memory network, or LSTM, a recurrent neural architecture designed to learn patterns that extend across long sequences. Unlike simpler feedforward networks, LSTMs use gating mechanisms that let them retain information over many time steps, making them well suited to traffic data, where the useful signal is rarely a clean repetition of past cycles.

The benchmark results, reported by Motarwar, who trained the forecasting models, were decisive. On previously unseen data from 2021 through 2024, the LSTM outperformed ARIMA, achieving an average prediction error of roughly 343 vehicles per day compared with about 418 for ARIMA, an improvement of approximately 18 percent. The gap, Motarwar notes, reflects a conceptual difference between the two methods. ARIMA gives you seasonality, which is true but not the whole story; the LSTM picks up the parts of the pattern that do not repeat cleanly, and that is where most of the improvement came from. This distinction matters practically because the non-repeating component of traffic, driven by construction, shifting land use, weather and economic change, is precisely the component that most affects planning decisions.

With the forecasting engine validated, the team turned its projections toward the future. The model projects average daily traffic rising from 12,540 vehicles in 2025 to 19,680 in 2029, a substantial increase that the researchers are careful to frame as a forecast rather than a certainty. Those figures come with prediction intervals wide enough to demand humility, a caveat the authors emphasize deliberately. Forecasting in urban systems is an exercise in bounded uncertainty, not prophecy, and the framework is designed to present projections alongside the ranges in which they might plausibly fall, keeping the limits of the evidence visible rather than burying them beneath a single confident number.

Forecasting alone, however, was only one piece of the puzzle, and arguably not the most novel one. Patil led the spatial component, mapping traffic onto Uber’s H3 system, an open-source hexagonal hierarchical geospatial indexing scheme that has become popular for spatial analytics. Unlike irregular administrative boundaries, H3 divides geographic space into nested hexagonal cells that can be aggregated or subdivided consistently, allowing planners to examine congestion at multiple scales, from borough-wide patterns down to localized hotspots, without the distortions that arise when data is forced into political or census boundaries. Clustering algorithms were then applied to identify areas where heavy traffic repeatedly accumulated over time. The results were instructive: Manhattan emerged as the highest-congestion borough in the analysis, followed by Brooklyn and Queens, but the more useful finding was the temporal consistency of the problem corridors.

That consistency is exactly what makes the spatial layer valuable to practitioners. A citywide average, Patil points out, does not help city planners. What they need to know is which corridors are badly impacted, and those corridors turn out to be consistent year to year. The hexagonal mapping and clustering were designed precisely to expose that structure, converting thousands of individual traffic observations into a spatial picture of where intervention would matter most. When the same hotspots persist across years, they become targets for policy: bus lane additions, signal retiming, freight management or congestion pricing can be evaluated against the specific places where repeated accumulation of traffic is demonstrable rather than anecdotal.

The final component turns those analytical results into something closer to a conversation. Building on the forecast and the congestion maps, the researchers constructed a customized portal using Meta’s LLaMA model connected to a project-specific knowledge base containing forecasts, congestion locations and other traffic information. A planner could ask, for example, how a highway expansion near LaGuardia Airport might affect traffic in Queens, or what could happen if one-way streets in Lower Manhattan were converted to two-way. Because the language model is bound to the project’s own knowledge base, its answers are anchored in the city’s data rather than in generic statistical narratives. A general chatbot, Motarwar observes, gives you a reasonable-sounding generic paragraph about congestion; planners need an answer that comes from their own data. The team frames this as much a data problem as a model problem, a distinction that goes to the heart of how public institutions should think about generative AI.

Crucially, the entire system is designed to run locally. Agencies could keep sensitive transportation information behind their own firewall rather than sending it to an outside AI service, even if commercial models sometimes offer stronger capabilities. That trade-off, capability for control, is one the researchers consider worth making, and it explains why the paper appeared in a special issue devoted to ethical and explainable GeoAI. The team is not claiming the platform can solve congestion on its own, nor has it yet undergone a large-scale trial with working planners. Rozhkov hopes to test future versions with smaller municipalities and other planning datasets. Rather than asking AI to make urban decisions, the researchers envision it as an interface between planners and increasingly complicated datasets, a way of keeping the underlying data, assumptions and decisions under local control, which is ultimately what will benefit local residents.

Subject of Research: A geospatial artificial intelligence framework combining traffic forecasting, H3 hexagonal spatial mapping and a locally hosted large language model to support urban traffic congestion planning.

Article Title: A new AI framework could help cities plan for future traffic

Article References: A new AI framework could help cities plan for future traffic. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: GeoAI, traffic forecasting, LSTM neural network, ARIMA, urban planning, New York City traffic, Uber H3 hexagonal grid, large language model, LLaMA, congestion mapping, Transactions in GIS, explainable AI