Game Theory Meets GPS: New AI Framework Gives Every Travel Mode Its Own Feature Fingerprint

Every journey leaves a statistical fingerprint. A cyclist’s acceleration pulses with each pedal stroke, a bus lurches through stop-and-go rhythms, and a metro train hums along its rails with a vibration signature all its own. For years, machine learning models have tried to read these fingerprints from GPS data to automatically detect how people travel, a task known as transportation mode detection. Now, researchers at the University of Porto have introduced a framework that fundamentally rethinks how such models treat the evidence, arguing that the features that reveal a walker are not the same features that reveal a metro rider, and that every travel mode deserves its own tailored mathematical lens.

The new study, published in the International Journal of Data Science and Analytics by Akilu Rilwan Muhammad, Ana Aguiar, and João Mendes-Moreira, presents a class-aware classification framework called Class-subspace. Its central innovation lies in borrowing an idea from cooperative game theory: the Shapley value. Originally developed to fairly divide a payout among players in a coalition, Shapley values have become one of the most trusted tools for explaining machine learning predictions through the SHAP method, short for SHapley Additive exPlanations. Rather than using SHAP merely to explain a model after the fact, the Porto team flips the workflow, using SHAP attributions to actively construct the model itself.

The reasoning is deceptively simple. Conventional transportation mode detection pipelines apply a single, globally selected feature set across every travel mode, optimising one subset of variables for the entire multi-class problem. But the researchers found that the features most relevant for distinguishing a bus journey were often sub-optimal for identifying a car trip. A single global feature set, they argue, necessarily compromises performance for certain classes. Class-subspace instead trains an initial classifier, computes mean absolute SHAP values for each feature and each class, ranks the features by their contribution to each mode’s prediction, and retains the minimal set of features that explains a cumulative threshold of 60 percent of each class’s relevance. Each transportation mode thus acquires its own compact feature subspace, and a specialised base learner is trained for every mode before their votes are aggregated into a final decision.

The technical pipeline behind the framework is substantial. The team worked with two real-world GPS trajectory datasets: SenseMyFEUP, collected from 227 participants in Porto, Portugal, using an Android application that sampled location roughly once per second, and the well-known GeoLife dataset from Microsoft Research Asia, gathered from 182 participants in Beijing over nearly five years. Rigorous preprocessing was essential. The researchers chained GPS points into trips using a 30-minute gap threshold, filtered out anomalous sessions that continued recording without movement, applied a spatial filter to keep only city-scale journeys, and capped implausible walking trips at eight kilometres. Segments shorter than 50 metres were discarded, removing nearly 39 percent of segments in the Porto data and about 14 percent in GeoLife.

Feature engineering drew on both the time and frequency domains. For each 100-step instance, the team computed statistics of speed, acceleration, jerk, and bearing rate, capturing the kinematic character of motion. They then applied the Fast Fourier Transform to convert these signals into the frequency domain, hypothesising that cyclic human movements such as walking or pedalling would produce distinctive high-frequency components, while smooth mechanical travel by car or bus would concentrate energy at low frequencies. In total, 80 features per instance were generated, with frequency-domain information encoded through the indices of the top ten spectral components rather than their magnitudes, capturing which frequency bands are active for each mode rather than how much energy they carry.

Evaluation was deliberately conservative. Instead of the random data splits common in the field, which can leak future patterns into training and inflate accuracy, the team used temporal splits, training on earlier data and testing on later periods. Against baselines including Sequential Forward Floating Selection, Mutual Information filtering, and the Boruta algorithm, Class-subspace achieved weighted ROC-AUC scores of up to 78.8 percent on the Porto dataset and 89.5 percent on GeoLife using the XGBoost classifier, performing comparably to established global feature selection methods. A formal statistical comparison using the Friedman test and Bonferroni-Dunn post hoc analysis confirmed that no baseline differed significantly from the proposed method.

What the framework sacrifices in raw accuracy gains, it returns in interpretability. An analysis of feature overlap across class-specific subspaces, measured with the Jaccard similarity index, revealed moderate overlap and a small universal core of speed-based statistics, including maximum, mean, standard deviation, and 85th-percentile speed, that appeared in every configuration. Beyond that shared core, each mode carried its own signature. Walking was captured almost entirely by speed statistics, consistent with its narrow and stable range. Cycling added bearing rate features reflecting frequent directional changes. Bus detection drew on jerk and bearing rate, echoing stop-and-go dynamics. Metro uniquely selected bearing rate distributions and acceleration frequency components, matching the confined, scheduled vibration patterns of rail transit. On the Porto data with random forest, only 7 of 32 distinct features were common to all classes, while 9 served exclusively as metro signatures.

The authors are candid about limitations. Bus and metro classes remained difficult on the Porto dataset, a challenge they attribute to the joint effect of class imbalance and class overlap rather than to feature selection itself, pointing to their earlier work characterising these issues. The computational cost of Shapley values is a known constraint, and the sensitivity of performance to the contribution threshold varied between datasets, with GeoLife remarkably stable across thresholds while the Porto data peaked sharply at 0.60. Future work, they suggest, could explore adaptive threshold selection through cross-validated grid search, Bayesian optimisation, or information-theoretic criteria, and extend the class-subspace concept to other multi-class domains.

The broader implications reach well beyond transportation research. As cities worldwide lean on crowdsensed mobility data for planning, emissions modelling, and infrastructure investment, the trustworthiness of the underlying classification models matters enormously. Class-subspace demonstrates that per-class interpretability need not come at the price of predictive power: a model can match the performance of globally optimised baselines while explicitly revealing which evidence supports each decision. In a field where reported accuracies above 90 percent sometimes rest on leaky evaluation protocols, the Porto team’s insistence on temporal splits and statistical rigor sets a standard. Their message to the machine learning community is clear: when classes are fundamentally different, stop forcing them to share the same lens, and let game theory hand each one its own.

Subject of Research: SHAP-driven class-aware machine learning for transportation mode detection from GPS trajectory data

Article Title: Class-subspace learning: a SHAP-driven framework for class-aware transportation mode detection

Article References: Muhammad, A. R., Aguiar, A., & Mendes-Moreira, J. (2026). Class-subspace learning: a SHAP-driven framework for class-aware transportation mode detection. International Journal of Data Science and Analytics, 22(1), Article 302. https://doi.org/10.1007/s41060-026-01263-x

Image Credits: AI Generated

DOI: 10.1007/s41060-026-01263-x

Keywords: transportation mode detection, SHAP values, machine learning, GPS trajectories, feature selection, Shapley values, urban mobility, smart mobility, interpretability, XGBoost, Fast Fourier Transform, class-aware classification