The two missing ingredients, according to the team, are time and topology. Most existing approaches treat a trajectory as a sequence of spatial points and compare those sequences geometrically, while handling the temporal dimension only crudely, if at all. The problem is that a trip down the same street at 8 a.m. and the same trip at 8 p.m. can be radically different experiences, shaped by rush-hour congestion, signal timing, and traffic flow patterns that change across the day and the week. Existing methods, the authors note, neither explicitly integrate the temporal information of GPS trajectories with the topology of the road network, nor adapt flexibly to dynamically changing time granularity, because they rely on fixed time slices. A framework locked into rigid hourly buckets, for example, cannot smoothly distinguish a pattern that repeats every fifteen minutes from one that unfolds over a whole day.

The proposed solution is called STTF, short for Spatial-Temporal and Topological Fusion. The framework begins by constructing a knowledge graph that captures relationships among multiple nodes in a road network, effectively aligning raw GPS trajectories with the streets they actually travel on. This is an important shift in perspective. Rather than treating a trajectory as a free-floating cloud of latitude-longitude points, STTF grounds every observation in the physical structure of the city, where intersections connect to road segments and segments connect to one another in a rich web of spatial relationships. Map-matching techniques of the kind long used in navigation systems make this alignment possible, but STTF goes further by encoding the network itself as a graph that the model can learn from.

Once the knowledge graph is built, the framework turns to Knowledge Graph Embedding, or KGE, techniques borrowed from the field of representation learning. These methods, popularized in artificial intelligence research for encoding entities and relations in large knowledge bases, learn compact numerical vectors, or embeddings, for each node and each relation in the graph. In STTF, the result is a fused graph in which the geometry of the road network and the movement patterns of trajectories are expressed in a shared mathematical space. Road segments that play similar roles in the network, or that carry similar traffic, end up with similar embeddings, giving the model a topological vocabulary it can use when comparing trips that may never visit exactly the same points.

The second major innovation addresses the temporal dimension through multi-granularity encoding. Instead of committing to a single fixed time slice, STTF applies both a day encoding matrix and a week encoding matrix, allowing the model to represent time at multiple scales simultaneously. A day encoding captures where a trip falls within the daily rhythm of the city, distinguishing the morning commute from the midnight lull, while a week encoding distinguishes weekday patterns from weekend ones. Because these encodings operate at multiple granularities, the framework can adapt to dynamically changing temporal patterns rather than forcing all phenomena into one arbitrary resolution. This flexibility matters for real applications, where the meaningful timescale of a pattern, whether it is a fifteen-minute delivery window or a weekly shopping rhythm, varies from one dataset to the next.

At the heart of the framework sits a spatial-temporal topology fusion module, the component that explicitly marries temporal information with road network topology. The module uses an attention mechanism, the same class of technique that powers modern language models, to learn topological embeddings from the fused graph, deciding which parts of the network context deserve emphasis for a given trajectory. It then jointly models four kinds of information: segment embeddings describing the road segments traversed, temporal embeddings capturing when the movement occurred, positional embeddings encoding where each observation sits within the sequence, and road type information describing the character of the streets involved, such as highways versus local roads. The output is a single fused representation of the entire trajectory, a vector that condenses space, time, and network structure into one comparable form.

Training the model requires no manually labeled examples of similar and dissimilar trajectories, a significant practical advantage given how expensive such labels are to produce. Instead, STTF learns through self-supervised learning driven by two loss functions. The first is a contrastive loss, an approach inspired by recent breakthroughs in self-supervised image representation learning, which teaches the model to pull representations of related trajectories closer together while pushing unrelated ones apart. The second is a masked language model loss, adapted from the pre-training recipe behind models like BERT, in which parts of the input are hidden and the model must reconstruct them, forcing it to internalize the statistical structure of movement through the road network. Together, these objectives let the framework learn meaningful trajectory representations directly from raw data.

The researchers evaluated STTF on two real-world datasets, pitting it against six mainstream baseline methods for trajectory similarity computation. The framework consistently surpassed all of them, achieving state-of-the-art performance. The baselines it outperformed represent the main lineages of prior work: classical geometric measures that compare point sequences directly, graph-based neural approaches that model road networks with architectures such as graph attention networks and residual LSTMs, and earlier self-supervised frameworks that fused spatial and temporal signals but, in the authors’ assessment, failed to integrate them with network topology explicitly or to handle time granularity adaptively. The consistent margin across datasets suggests that the fusion of topology and multi-granular time is not a marginal refinement but a substantive source of modeling power.

The implications reach well beyond the benchmark tables. Trajectory similarity is a foundational primitive for downstream applications including trajectory clustering, data compression, and intelligent route planning, so improvements at this level propagate outward. Better similarity measures could sharpen anomaly detection, helping cities spot unusual traffic events or suspicious movement patterns; they could improve ride-sharing matching by identifying trips that genuinely overlap in space and time; and they could make route recommendation systems more sensitive to when a journey happens, not just where. The self-supervised design is particularly consequential for deployment, because it means the framework can be trained on whatever trajectory data a city or logistics operator already possesses, without the bottleneck of human annotation.

The work also illustrates a broader trend in machine learning research: the migration of techniques between domains. Knowledge graph embeddings arrived from semantic web and question-answering research, attention mechanisms and masked pre-training from natural language processing, and contrastive learning from computer vision. STTF assembles these ingredients around a problem with distinctly geographic structure, the topology of a road network, and in doing so demonstrates how multimodal fusion, the deliberate combination of heterogeneous information sources, can outperform models that consider space and time in isolation. The authors report no competing interests and received no external funding for the study. As urban datasets continue to swell, frameworks that respect both the geometry of streets and the rhythms of the clock are likely to become the standard against which future trajectory models are measured.

Subject of Research: Trajectory similarity computation using spatial-temporal and topological fusion with knowledge graph embeddings and self-supervised learning

Article Title: A spatial-temporal and topological fusion framework for trajectory similarity computation

Article References: A spatial-temporal and topological fusion framework for trajectory similarity computation. (n.d.). https://doi.org/10.1007/s10489-026-07472-y

Image Credits: AI Generated

DOI: 10.1007/s10489-026-07472-y

Keywords: trajectory similarity, knowledge graph embedding, self-supervised learning, road network topology, spatial-temporal fusion, attention mechanism, contrastive learning, masked language model, GPS trajectories, urban data analysis, route planning, trajectory clustering

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