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

The research, led by Sharmin Akther of Jahangirnagar University together with colleagues from the Bangladesh Meteorological Department, the University of Melbourne and other institutions, focused on Mymensingh district, located at 24.75 degrees north and 90.40 degrees east. The team assembled a remarkable 38-year record of daily minimum temperatures spanning 1985 to 2022, obtained from the Bangladesh Meteorological Department. Only 30 of the 13,787 daily records, a mere 0.22 percent of the dataset, contained missing values, and these were filled using time-based interpolation that respects the temporal relationship between adjacent observations. Statistical tests confirmed the series was stationary: the Augmented Dickey-Fuller test rejected a unit root with a statistic of minus 11.426, while the KPSS test failed to reject stationarity, giving the researchers a solid foundation for time series modeling.

The core of the study was a head-to-head comparison of forecasting approaches. On the statistical side, the team fitted an autoregressive integrated moving average model, selecting ARIMA(1,1,2) as optimal using the Akaike and Bayesian information criteria, and an exponential smoothing state space model, where the simplest ETS(A,N,N) configuration with additive errors, no trend and no seasonality achieved the lowest AIC. On the machine learning side, they trained support vector regression with a radial basis function kernel, random forest regression, and a long short-term memory neural network, the deep learning architecture specifically designed to capture long-range dependencies in sequential data through its gated memory cells. Each machine learning model was fed 30 days of lagged temperatures as input features, standardized with Z-score normalization, and tuned through five-fold time series cross-validation to prevent any leakage of future information.

The team also built six hybrid models that combined each machine learning predictor with a statistical correction of its residuals, following the classic hybridization strategy in which a neural network captures nonlinear patterns while ARIMA or ETS models any remaining linear structure. The final hybrid prediction was the sum of the machine learning forecast and the statistical model’s forecast of the residuals. This family of models, including LSTM+ARIMA, LSTM+ETS, SVR+ARIMA, SVR+ETS, RF+ARIMA and RF+ETS, was evaluated with the same rigorous out-of-sample protocol applied to the standalone models.

When the models were tested on data they had never seen, covering June 2011 through September 2022, the deep learning model emerged as the clear winner. The LSTM achieved a root mean square error of 1.395 degrees Celsius, a mean absolute error of 1.053 degrees, and a mean absolute scaled error of 0.906, meaning it beat a naive persistence forecast. Support vector regression came in a close second at 1.403 degrees RMSE, and random forest followed at 1.435 degrees. The statistical models, by contrast, performed poorly, with RMSE values around 6.7 to 6.8 degrees and MASE values above 4. The researchers attribute the LSTM’s success to three factors: daily minimum temperature exhibits stronger day-to-day persistence than mean or maximum temperature, the 38-year training record provides ample data for learning seasonal cycles, and the single-station design avoids errors from spatial heterogeneity.

Perhaps the most surprising finding concerned the hybrid models. Despite the theoretical appeal of combining statistical and machine learning methods, none of the six hybrids significantly improved on its standalone counterpart. The best hybrid, LSTM+ARIMA, achieved an RMSE of 1.397 degrees, essentially identical to the standalone LSTM’s 1.395 degrees. Diebold-Mariano tests, which formally compare predictive accuracy between competing forecasters, confirmed that only LSTM+ARIMA differed significantly from its base model, and in that case the standalone LSTM was actually better. The message is that when a deep learning model already captures the complex temporal structure of a temperature series, bolting on a statistical correction adds complexity without adding skill.

Accuracy in predicting temperature, however, is not the same as accuracy in detecting cold waves, and here the study delivers its most important caution. Treating cold wave detection as a binary classification problem with the 10 degree threshold, the LSTM showed excellent discriminative power, with a ROC-AUC of 0.975, meaning it ranks cold days above ordinary days with remarkable consistency. Yet its recall was only 0.215: the model correctly identified just 14 of the 65 actual cold wave days in the test period, missing 51 of them. Precision stood at 0.560, so when the model did flag a cold day it was right 56 percent of the time, and it raised only 11 false alarms. The overall accuracy of 0.985 is misleading because cold days make up only 1.6 percent of observations, a class imbalance that pushes models toward conservative behavior. Monthly analysis revealed the pattern in detail: the model detected 22 of 43 cold days in December, a 51 percent detection rate, but only 2 of 18 in November and 1 of 4 in January, suggesting systematic underestimation of early winter cold events.

Using the trained model, the researchers generated a daily minimum temperature forecast for 2027 with 80 and 95 percent prediction intervals constructed from the test RMSE. The projection captured the expected seasonal cycle, with summer values peaking around 24 to 25 degrees and winter minima between 22 and 23 degrees, and all forecasted temperatures remained above the cold wave threshold. But the authors are emphatic that this absence of forecasted cold waves must not be read as a prediction of no cold wave risk. A model that misses roughly 78 percent of historical cold days would likely miss actual cold waves in 2027 as well. The model was trained on data ending in 2011 and cannot account for climate regime shifts or changing winter patterns since then, and the winter prediction intervals, spanning roughly plus or minus 2 to 3 degrees, are wide enough that a downward fluctuation could still push temperatures below 10 degrees. The researchers stress that disaster management agencies, health authorities and local governments should continue normal winter preparedness measures from November through February regardless of this exploratory projection, and that operational decisions should rely on routine seasonal forecasts from national meteorological services.

The study’s implications reach beyond Bangladesh. The finding that deep learning substantially outperforms linear statistical models for daily temperature echoes results from across South Asia, including ARIMA-based temperature analysis in Karachi, Pakistan, and an STL-ARIMA-LSTM hybrid for heatwave forecasting in Rajshahi that achieved an RMSE of 1.18 degrees. The low recall for rare events is also not unique to this work; studies of heatwave and flood classification have reported similar struggles with imbalanced datasets, where high overall accuracy conceals poor detection of the very events that matter most. The authors recommend that future operational systems adjust the classification threshold to balance precision and recall, incorporate perceived temperature metrics such as the wind chill index, and integrate humidity, wind speed, cloud cover and large-scale climate indices like ENSO, all of which were absent from the current univariate framework.

The researchers outline a clear roadmap for strengthening the approach. Expanding the analysis to multiple stations across Bangladesh would test spatial transferability and reveal regional patterns in cold wave occurrence. Advanced techniques for handling class imbalance, including synthetic minority oversampling, focal loss and cost-sensitive learning, could raise recall at some cost to precision. Linking temperature forecasts to health outcome data such as cold-related mortality and hospitalization rates would allow health-relevant alert thresholds to be defined and would provide direct validation of the model’s usefulness as an early warning tool. Probabilistic methods such as quantile regression forests and Bayesian neural networks could extend forecast horizons while quantifying uncertainty more honestly. For now, the study positions the LSTM model as a powerful temperature forecasting instrument rather than a standalone cold wave alarm, a distinction that could shape how machine learning is deployed to protect vulnerable communities across the region.

Subject of Research: Machine learning forecasting of daily minimum temperatures and cold wave events in Mymensingh district, Bangladesh

Article Title: Forecasting cold wave in Bangladesh: a validated machine learning approach for early warning and vulnerability reduction

Article References: Akther, S., Hussain Khan, M. M., Chowdhury, S., Das, A., Rahman, M., & Rois, R. (2026). Forecasting cold wave in Bangladesh: a validated machine learning approach for early warning and vulnerability reduction. BMC Environmental Science, 3(1), Article 17. https://doi.org/10.1186/s44329-026-00058-6

Image Credits: AI Generated

DOI: 10.1186/s44329-026-00058-6

Keywords: cold wave, Bangladesh, LSTM, machine learning, temperature forecasting, early warning, Mymensingh, ARIMA, hybrid models, time series, climate extremes, public health