Hybrid AI Model Predicts City Traffic Three Days Ahead

Traffic is one of the defining headaches of modern urban life, and predicting it has long been one of the hardest problems in artificial intelligence. A new study published in Neural Computing and Applications reports that a hybrid deep learning model combining convolutional neural networks with bidirectional long short-term memory networks can forecast traffic flow up to 72 hours in advance, a horizon at which most conventional models lose their footing. The work, carried out by Mohamed Abd Elkader Hussien and Mohamed AboRizka at the Arab Academy for Science, Technology and Maritime Transport in Cairo, taps into the flood of data generated by Internet of Things sensors across urban road networks and turns it into predictions that remain stable over days rather than minutes.

The appeal of long-term traffic forecasting is easy to understand. Short-term predictions, covering the next few minutes or an hour, help with immediate tasks such as adjusting signal timings or warning drivers of congestion ahead. But the decisions that really shape a city, from planning road maintenance windows to scheduling public transport fleets and managing emergency response capacity, depend on knowing what traffic will look like many hours or even days from now. At those horizons, errors compound quickly. Small mistakes in how a model interprets today’s conditions cascade into large mistakes about tomorrow’s rush hour, which is why most operational systems have stayed firmly in the short-term regime.

The researchers’ answer is an architecture that splits the problem into two complementary parts. The convolutional neural network, or CNN, acts as the spatial analyst. CNNs, which rose to fame in image recognition, excel at detecting local patterns in grid-like data, and multi-sensor traffic readings can be arranged so that the network learns how congestion at one intersection relates to conditions on neighboring streets. The CNN component extracts these spatial features from the streams of data arriving from IoT devices deployed across the transportation network, effectively giving the model a snapshot of the road network’s geometry of congestion at each moment.

The second half of the hybrid is the bidirectional long short-term memory network, or BiLSTM. LSTM networks are a form of recurrent neural network designed to remember information over long sequences, using gated cells that decide what to store, what to discard, and what to pass forward. This makes them well suited to temporal dependencies in traffic, such as the daily rhythm of morning and evening peaks, the weekly cycle that distinguishes weekdays from weekends, and the slow drift of seasonal patterns. The bidirectional twist means the network processes the sequence both forward and backward, so its understanding of any given time step is informed by what came before and what came after, allowing it to capture context that a one-directional reader would miss.

Chained together, the two components form a pipeline that mirrors the structure of the problem itself. Raw sensor readings flow into the CNN, which compresses the spatial state of the network into informative feature representations. Those features then feed into the BiLSTM, which models how they evolve through time and projects them forward across the forecast horizon. This division of labor is the key to the model’s stability: rather than forcing a single network to learn both where traffic happens and how it changes, the hybrid assigns each task to the architecture best built for it.

To test the approach, the team evaluated the model on traffic datasets representing four major locations in Cairo: Misr El-Gedida, Nasr City, Downtown, and New Cairo. Cairo is a demanding proving ground for any forecasting system. Its streets carry enormous and highly variable loads, its traffic patterns are shaped by a dense mix of private cars, informal transport, and commercial vehicles, and disruptions propagate through the network in ways that are difficult to anticipate. A model that performs well on Cairo data has faced some of the messiest conditions urban traffic can offer.

The results, measured using standard error metrics including mean absolute error and root mean square error alongside overall prediction accuracy, showed that the hybrid CNN–BiLSTM model significantly outperformed both traditional machine learning approaches and single deep learning models. That comparison matters because it isolates the value of the hybrid design. Classical methods such as autoregressive statistical models and simpler neural networks have historically struggled to represent the intertwined spatial and temporal structure of traffic, while single deep architectures tend to sacrifice one dimension for the other. The hybrid’s advantage across all three evaluation metrics suggests that the CNN and BiLSTM components are genuinely complementary rather than redundant.

Perhaps the most striking finding is the model’s endurance. It maintained stable performance for prediction horizons up to 72 hours, meaning city operators could in principle run the model on a Monday and receive a usable picture of Thursday’s traffic. The authors highlight this stability as the feature that makes the system practical for real-world smart city traffic management, where forecasts need to be reliable enough to act on, not merely interesting in a laboratory setting. Stable multi-day forecasts could support proactive signal control, dynamic lane management, better-informed logistics routing, and earlier warnings for planned events that will stress the network.

The study also sits within a broader wave of research into deep learning for transportation. Recent years have seen graph neural networks, attention-based architectures, and transformer models all applied to traffic prediction, each trying to capture the spatial-temporal character of urban mobility in different ways. The CNN–BiLSTM approach represents a pragmatic strand of this effort: it builds on two of the most mature and well-understood deep learning building blocks, which makes the resulting system easier to train, deploy, and maintain than more exotic architectures. The authors have made their source code and experimental resources publicly available on GitHub, a step that should help other researchers reproduce the results and adapt the model to their own cities.

For the smart cities movement, the work offers a concrete demonstration of what IoT infrastructure can deliver when paired with the right analytical machinery. Cities have spent the past decade blanketing their streets with sensors, cameras, and connected devices, generating torrents of data that often far exceed what existing systems can exploit. Models like the one developed in Cairo are a step toward converting that raw data deluge into operational foresight. If the hybrid approach generalizes beyond Cairo’s challenging roads, the practical implications could reach far beyond traffic lights and congestion maps, informing everything from urban planning decisions to emissions reduction strategies that depend on anticipating where and when vehicles will cluster. The three-day traffic forecast, once a distant ambition, is now a demonstrated capability, and cities around the world will be watching how far it can be pushed.

Subject of Research: Long-term urban traffic flow forecasting using a hybrid CNN–BiLSTM deep learning model based on IoT sensor data

Article Title: Smart cities long-term traffic flow forecasting using a hybrid CNN–BiLSTM model based on IoT

Article References: Hussien, M. A. E., & AboRizka, M. (2026). Smart cities long-term traffic flow forecasting using a hybrid CNN–BiLSTM model based on IoT. Neural Computing and Applications, 38(19), Article 789. https://doi.org/10.1007/s00521-026-12423-2

Image Credits: AI Generated

DOI: 10.1007/s00521-026-12423-2

Keywords: traffic forecasting, CNN, BiLSTM, deep learning, smart cities, Internet of Things, IoT sensors, long-term prediction, Cairo, intelligent transportation, machine learning, urban mobility