Neuro-Symbolic AI Makes Traffic Forecasts Explainable with Concept Lattices

Traffic congestion has become one of the most stubborn socioeconomic problems of modern urban life, and the tools used to fight it have long suffered from a paradox of their own making. The best forecasting models are deep neural networks whose predictions are accurate but opaque, leaving traffic engineers to trust a number they cannot interrogate. A new study published in Applied Intelligence by researchers at Universiti Malaysia Kelantan, Universiti Teknikal Malaysia Melaka, and Universiti Teknologi Malaysia proposes a way out of that bind: a neuro-symbolic framework that fuses a hybrid CNN-LSTM forecasting engine with Formal Concept Analysis, a branch of mathematics that converts raw data into explicit, human-readable logic rules. The result is a system that not only predicts traffic volumes with state-of-the-art accuracy but can also explain, in deterministic if-then statements, why its forecasts make sense.

The motivation is starkly quantitative. The authors note that residents of Malaysia’s Klang Valley collectively waste more than 250 million hours each year stuck in traffic, an economic drag equivalent to between 1.1 and 2.2 percent of national GDP. Congestion also inflates vehicular emissions, placing Malaysia among the highest CO2 emitters in ASEAN. Traditional remedies such as building more roads have repeatedly run into the Downs-Thomson paradox, in which added capacity simply induces additional demand. The alternative is proactive management through Intelligent Transportation Systems, which depend fundamentally on accurate short-term traffic forecasting to enable dynamic routing, variable toll pricing, and efficient resource allocation.

Deep learning has become the dominant approach to that forecasting problem, and for good reason. Long Short-Term Memory networks, designed to preserve information across long sequences without the vanishing gradient problems that plague ordinary recurrent networks, excel at capturing the daily and weekly rhythms of traffic flow. But the Malaysian team identified a subtle weakness: standalone recurrent architectures struggle to model the instantaneous cross-correlations between heterogeneous vehicle classes, such as the synchronized surges of passenger cars and heavy commercial vehicles within a single time window. Their solution was to prepend a one-dimensional convolutional layer that acts as a trainable spatial filter, extracting localized inter-class dependencies before the temporal memory cells process the sequence.

The hybrid CNN-LSTM engine was validated against an unusually rich dataset: five years of multivariate toll records from PLUS Malaysia Berhad covering 18 plazas in the Klang Valley, totaling 73,891 aggregated observations across five vehicle classes. The data was normalized with min-max scaling and transformed into a supervised learning problem using a 24-hour sliding window, with a strict 70:15:15 chronological split to prevent data leakage. The convolutional head compresses the raw multivariate input into latent spatial features, the LSTM encodes their temporal evolution, and a final ensemble layer built on XGBoost or Random Forest refines the decision boundary using the latent vector produced by the neural backbone.

The benchmarking results were decisive. The CNN-LSTM consistently outperformed standalone LSTM, Bidirectional LSTM, and GRU baselines, achieving the lowest Root Mean Squared Error and Mean Absolute Percentage Error across the heavy commercial and specialty vehicle classes. The contrast was most dramatic for Class 5 machinery, where the CNN-LSTM posted an RMSE of roughly 744 while the GRU’s error ballooned to nearly 2,071, with a MAPE above 52 percent. Standalone recurrent models, the authors argue, simply cannot cope with the sporadic, non-uniform distributions of heavy machinery and buses, whereas the convolutional front end maps localized spikes before they reach the memory cells, keeping forecasts within a tighter band.

Accuracy alone, however, was never the point. The study’s central innovation is its treatment of explainability as a triangulated, multi-perspective exercise rather than a single post-hoc calculation. Most transportation research relies on SHAP, a game-theoretic method that assigns each feature a marginal contribution to a prediction, but the authors argue that using SHAP in isolation leaves the system vulnerable to that method’s inherent biases. Their framework instead cross-validates three mathematically independent explainers: SHAP, Integrated Gradients, which integrates gradients along a path from a baseline input to the actual input, and LIME, which fits a local linear surrogate around each prediction. Only when these methods agree can the attribution be trusted.

The triangulation paid off in a striking way. Both SHAP and Integrated Gradients overwhelmingly identified Class 1 passenger vehicles and total volume as the dominant drivers of the model’s internal state, with normalized importance scores between 0.98 and 1.00, while LIME alone would have suggested a far more distributed picture. That consensus then served as the bridge to the symbolic layer. The team discretized the continuous attribution scores into a binary formal context, using median-based thresholds to avoid both over-saturation and sparsity, and applied Formal Concept Analysis to extract a concept lattice, a hierarchical structure in which each node represents a maximal set of traffic observations sharing a common set of attributes.

The lattice yields deterministic implication rules with remarkable confidence levels. One rule states that when passenger cars, large trucks, buses, and total volume simultaneously exceed their operational thresholds at a given toll plaza, a spike in small lorries follows with absolute certainty. Another provides the reciprocal logic: surges in heavy commercial traffic inherently signal corresponding surges in passenger car volume. Because these symbolic rules align with the numerical consensus from SHAP and Integrated Gradients, the authors can demonstrate what they call neuro-symbolic alignment: the neural network has genuinely learned to base its predictions on the same foundational variables that govern the human-readable logic, rather than exploiting spurious correlations that no one can audit.

The practical implications extend beyond academic curiosity. The full pipeline, from convolutional filtering through ensemble regression to multi-perspective attribution extraction, achieves an average inference latency of 14.2 milliseconds per 24-hour look-back window on an NVIDIA RTX 4090, comfortably within the 15-minute to 1-hour cycles typical of dynamic traffic management. For highway operators, that means forecasts can be paired with explicit rules for proactive intervention, such as anticipating gridlock before it manifests, and that algorithmic actions in automated tolling or lane allocation can always be backed by a symbolic reason. The authors also argue the architecture is network-agnostic: because it relies on multivariate sequences rather than rigid graph topologies, adapting it to a new corridor requires only retraining the feature extractor on local sensor streams.

The study, published as open access in Applied Intelligence, positions itself as a benchmark for deploying explainable AI in high-stakes transportation infrastructure, and it arrives amid a broader shift in the field toward neuro-symbolic systems that pair connectionist learning with formal logic. The authors acknowledge limitations and chart future work in three directions: real-time integration into live traffic control systems, extension to multimodal networks encompassing bus rapid transit and rail flows, and the development of entropy-driven adaptive thresholding so that the concept lattices can continuously evolve with seasonal traffic regimes. For now, the Malaysian dataset demonstrates the core claim: a forecasting system can be simultaneously neural in its handling of noisy real-world data and symbolic in the logic it offers to the humans who must act on it, closing the trust gap that has long separated deep learning from operational deployment.

Subject of Research: Neuro-symbolic explainable AI framework combining CNN-LSTM deep learning and Formal Concept Analysis for interpretable traffic volume forecasting

Article Title: Multi-Perspective explainability in traffic prediction: A neuro-symbolic approach using CNN-LSTMs and concept lattices

Article References: Mohamad Yusof, S. S., Ab. Ghani, H., Md Saleh, N. I., & Khamis, N. (2026). Multi-Perspective explainability in traffic prediction: A neuro-symbolic approach using CNN-LSTMs and concept lattices. Applied Intelligence, 56(16), Article 505. https://doi.org/10.1007/s10489-026-07512-7

Image Credits: AI Generated

DOI: 10.1007/s10489-026-07512-7

Keywords: traffic forecasting, explainable AI, CNN-LSTM, Formal Concept Analysis, neuro-symbolic AI, intelligent transportation systems, SHAP, LIME, Integrated Gradients, deep learning, Malaysia, time-series prediction