TEACH - Trajectory Embedding Comparator Benchmark

TEACH stands for Trajectory Embedding compArator benCHmark.

This repository provides an interactive benchmark tool for comparing trajectory embedding representations before using them in downstream machine learning models.

The problem of selecting the most appropriate trajectory embedding is challenging because the representation quality can strongly influence the global performance of prediction models. TEACH helps researchers inspect, compare, and evaluate embeddings through both intrinsic and extrinsic evaluation strategies.

📄 Scientific Background and Main Paper

TEACH is motivated by the research presented in:

Cruz, L. A., Coelho da Silva, T. L., Magalhães, R. P., Melo, W. C. D., Cordeiro, M., de Macedo, J. A. F., and Zeitouni, K. (2022).
Modeling Trajectories Obtained from External Sensors for Location Prediction via NLP Approaches.
Sensors, 22(19), 7475. DOI: 10.3390/s22197475

Research Problem

The paper investigates next-location prediction from sparse trajectories collected by fixed external sensors, such as traffic-surveillance cameras equipped with license-plate recognition. Instead of continuously tracking a vehicle through GPS, the system observes an ordered sequence of discrete sensors crossed by the vehicle and attempts to predict the next sensor from a partial trajectory.

This setting is difficult because external-sensor trajectories are naturally sparse, sensors are unevenly distributed across the road network, and observations may be missing when a sensor fails to identify a passing vehicle.

NLP Analogy

The central idea is to reinterpret mobility data using concepts from Natural Language Processing:

Natural Language Processing	External-Sensor Trajectories
Word	Sensor or location token
Sentence	Ordered sensor trajectory
Text document	Complete vehicle trajectory
Corpus	Collection of trajectories
Word embedding	Sensor embedding
Sentence representation	Trajectory embedding
Next-word prediction	Next-sensor prediction

Under this analogy, models such as Word2Vec and BERT Masked Language Modeling (BERT MLM) can learn vector representations from the context and sequential order in which sensors appear in trajectories.

Research Questions

The article investigates three main questions:

Can NLP embeddings represent external-sensor trajectories well enough to improve next-location prediction with recurrent neural networks?
Do learned sensor embeddings preserve spatial proximity and road-network connectivity?
Do trajectory embeddings capture similarity between complete movement patterns?

Dataset and Experimental Design

The experiments used real traffic-monitoring data from Fortaleza, Ceará, Brazil:

Dataset characteristic	Value
External road sensors	489
Monitored vehicles	2,990
Sensor observations	22,530
Collection period	January-June 2019

Each sensor was treated as a location token. Word2Vec with the CBOW architecture and BERT MLM were trained to generate sensor embeddings. These representations were then used as the embedding layer of an LSTM next-sensor predictor, both with frozen weights and with task-specific fine-tuning.

The study combined two complementary evaluation strategies:

Extrinsic evaluation: measures whether an embedding improves the downstream next-sensor prediction task using ACC@1, ACC@2, ACC@3, and road-network closeness error.
Intrinsic evaluation: measures whether embedding-space similarity agrees with geographic distance, road-network distance, Dynamic Time Warping, and edit distance.

Main Experimental Results

Prediction model	ACC@1	ACC@2	ACC@3	Mean closeness error
LSTM trained from scratch	64.10%	74.96%	80.32%	1.00 km
LSTM + BERT embeddings	66.84%	79.70%	85.05%	0.97 km
LSTM + fine-tuned BERT embeddings	73.71%	85.35%	90.01%	0.79 km
LSTM + Word2Vec embeddings	52.14%	63.09%	72.38%	1.44 km
LSTM + fine-tuned Word2Vec embeddings	53.33%	66.90%	72.14%	1.36 km

The fine-tuned BERT representation achieved the strongest predictive performance. It improved ACC@1 by approximately seven percentage points compared with the non-fine-tuned BERT configuration and also reduced the road-network distance between incorrect predictions and the expected sensor.

For intrinsic evaluation, the BERT sensor embeddings obtained an MRR of 0.20 against Euclidean distance and 0.17 against road-network distance. At the trajectory level, the embeddings obtained an MRR of 0.44 using Dynamic Time Warping as the reference and 0.27 using edit distance. These results indicate that the learned vector space captures meaningful spatial, sequential, and connectivity patterns, although the correspondence between embedding similarity and physical distance becomes weaker as the geographic neighborhood expands.

Relationship between the Paper and TEACH

The paper demonstrates that choosing and evaluating a trajectory representation is not merely a preprocessing detail: it can substantially change downstream prediction quality. TEACH turns this evaluation perspective into an interactive and reusable workflow. Instead of comparing only a fixed set of experiments, researchers can upload their own datasets, embeddings, and trained models, inspect trajectory samples, run intrinsic similarity analyses, and compare downstream predictive performance before selecting a representation.

📌 Overview

TEACH is designed to support researchers working with trajectory data, location prediction, and representation learning.

The tool provides a visual and interactive workflow for:

uploading trajectory datasets
uploading trajectory embedding files
uploading trained prediction models
linking datasets, embeddings, and models
visualizing trajectory samples
computing dataset statistics
performing intrinsic embedding evaluation
performing extrinsic model evaluation
comparing embeddings before neural model usage

The main motivation comes from an analogy with Natural Language Processing. In NLP, word embeddings are often evaluated before being used in downstream models. TEACH adapts this idea to trajectory embeddings, where locations, sensors, or trajectory tokens can be represented as vectors.

🧭 Conceptual Map

graph TD
    teach_root["TEACH"] --> data_layer["Trajectory Data"]
    teach_root --> emb_layer["Embedding Representations"]
    teach_root --> model_layer["Prediction Models"]
    teach_root --> eval_layer["Evaluation Strategies"]
    teach_root --> dashboard_layer["Interactive Dashboard"]

    data_layer --> latlon_data["Lat/Lon Trajectories"]
    data_layer --> sequence_data["Sequence Trajectories"]
    data_layer --> road_network["Road Network Context"]

    emb_layer --> token_table["Tokenization Table"]
    emb_layer --> emb_matrix["Embedding Matrix"]
    emb_layer --> trajectory_vectors["Trajectory Vectors"]

    model_layer --> h5_models["Keras .h5 Models"]
    model_layer --> next_location["Next Location Prediction"]

    eval_layer --> intrinsic_eval["Intrinsic Evaluation"]
    eval_layer --> extrinsic_eval["Extrinsic Evaluation"]

    intrinsic_eval --> mrr_location["MRR Location"]
    intrinsic_eval --> similar_locations["Similar Locations"]
    intrinsic_eval --> mrr_trajectory["MRR Trajectory"]
    intrinsic_eval --> similar_trajectories["Similar Trajectories"]

    extrinsic_eval --> accuracy_metrics["Accuracy Metrics"]
    extrinsic_eval --> f1_score["F1-Score"]
    extrinsic_eval --> model_comparison["Model Comparison"]

    dashboard_layer --> upload_tab["Upload Tab"]
    dashboard_layer --> view_tab["View Trajectory Data"]
    dashboard_layer --> intrinsic_tab["Intrinsic Evaluation Tab"]
    dashboard_layer --> extrinsic_tab["Extrinsic Evaluation Tab"]

🧠 NLP Inspiration: From Word Embeddings to Trajectory Embeddings

Word embedding vector-space analogy. TEACH adapts the idea of comparing semantic vector representations to the mobility domain, where locations, sensors, or trajectory tokens are represented as embeddings.

In NLP, an embedding maps a discrete token to a continuous vector.

For example:

word -> vector
location token -> vector
trajectory sequence -> vector representation

TEACH follows the same conceptual direction, but instead of comparing words, it compares representations of trajectory-related elements.

This allows researchers to ask questions such as:

Do nearby locations have similar embedding vectors?
Do trajectories with similar routes become close in embedding space?
Does a better intrinsic representation improve downstream prediction?
Which embedding should be coupled with a neural network model?

🗺️ Trajectory Data Context

Example of GPS track visualization. TEACH focuses on trajectory data represented either as latitude/longitude points or as symbolic sequences of locations.

A trajectory can be represented as an ordered sequence:

$$ T = \langle p_1, p_2, \ldots, p_n \rangle $$

where each point may contain:

time
latitude
longitude
trajectory_id
optional location_label

In a sequence-based representation, the same trajectory can also be modeled as:

$$ T = \langle l_1, l_2, \ldots, l_n \rangle $$

where each $l_i$ is a location token, road segment, sensor identifier, or discretized spatial label.

✅ Main Usage per Script

Script	Main Role	Libraries
`app/dashboards_classes/view_trajectory_data.py`	Dataset statistics and trajectory visualization.	Pandas, NumPy, IPython, IPyWidgets, PyMove, Folium, OS, Pathlib, Random
`app/dashboards_classes/extrinsic_evaluation.py`	Evaluation of trained models on location prediction tasks.	Pandas, NumPy, scikit-learn, Keras, IPython, IPyWidgets, Pathlib
`app/dashboards_classes/intrinsic_evaluation.py`	Embedding quality inspection through distance matrices, MRR, and similarity analysis.	Pandas, NumPy, PyMove, OSMnx, NetworkX, Matplotlib, NLTK, TQDM, Pathlib, OS, Copy
`app/dashboards_classes/teach_main.py`	Main dashboard orchestration class.	Pandas, NumPy, IPython, IPyWidgets, Keras, OS, Pathlib, IO, Copy
`app/utils/distances.py`	Distance functions used by intrinsic comparison routines.	Math, NumPy
`app/utils/geographical.py`	Geographical and sequence utilities.	Keras Preprocessing
`app/utils/metrics.py`	Evaluation metrics.	NumPy
`app/utils/output.py`	Output widgets and display helpers.	IPython, IPyWidgets

📂 Repository Structure

teach/
│
├── app/
│   ├── dashboards_classes/
│   │   ├── extrinsic_evaluation.py
│   │   ├── intrinsic_evaluation.py
│   │   ├── teach_main.py
│   │   └── view_trajectory_data.py
│   │
│   ├── utils/
│   │   ├── distances.py
│   │   ├── geographical.py
│   │   ├── metrics.py
│   │   └── output.py
│   │
│   ├── matrices/
│   ├── trajectories/
│   ├── trejectories/
│   └── teach.ipynb
│
├── Basic_User_Experience_Flow.png
├── TEACH_Class_Diagram.png
├── README.md
└── requirements.txt

Depending on the execution flow, TEACH may also create or update metadata files such as:

Emb.csv
Model.csv
Emb#Data.csv
Model#Data.csv
Model#Emb.csv

🧠 Dependencies & Libraries

TEACH combines scientific Python, geospatial processing, machine learning, visualization, and dashboard tools.

Tool / Library	Purpose
Python 3.8.3	Main execution environment.
Pandas 1.5.3	Tabular data loading, metadata tables, and dataframe operations.
NumPy 1.23.5	Numerical operations and vector calculations.
Matplotlib 3.6.2	Plotting evaluation curves and matrices.
scikit-learn 1.0.2	Machine learning metrics and evaluation utilities.
Keras 2.9.0	Loading and evaluating neural models.
TensorFlow 2.9.1	Backend for Keras models.
TQDM 4.66.1	Progress bars for long computations.
OSMnx 1.2.2	Road network extraction and spatial network analysis.
NetworkX 2.8.5	Graph structures and network algorithms.
IPyWidgets 8.0.6	Interactive dashboard controls.
IPython 8.8.0	Notebook display and output utilities.
PyMove 3.1.2	Trajectory processing and visualization support.
NLTK 3.7	NLP utilities.
Folium 0.14.0	Interactive map visualization.
Keras Preprocessing 1.1.2	Sequence padding utilities.
Voilà 0.5.2	Converts the notebook into an interactive dashboard application.

▶️ Activating TEACH

1. Create a Conda Environment

Create an environment named teach_env:

conda create -n teach_env python=3.8.3

Activate the environment:

conda activate teach_env

2. Install Dependencies

Install dashboard and mobility-related dependencies:

conda install -c conda-forge voila pymove nltk=3.7 ipython=8.8.0

Install TensorFlow and Matplotlib:

conda install tensorflow=2.9.1 matplotlib=3.6.2

Install geospatial and machine learning dependencies:

conda install -c conda-forge osmnx=1.2.2 networkx=2.8.5 scikit-learn=1.0.2

If needed, install remaining packages with pip:

pip install pandas==1.5.3 numpy==1.23.5 folium==0.14.0 ipywidgets==8.0.6 tqdm==4.66.1 keras-preprocessing==1.1.2

3. Clone the Repository

git clone https://github.com/InsightLab/teach.git

Enter the repository and application folder:

cd teach
cd app

4. Run the Dashboard

voila teach.ipynb

Voilà will serve the notebook as an interactive web application.

🧭 Basic User Experience Flow

The TEACH interface is organized as a set of tabs that guide the user through data upload, visualization, intrinsic evaluation, and extrinsic evaluation.

Basic user experience flow of the TEACH dashboard.

Conceptually, the dashboard follows this flow:

flowchart LR
    upload["Upload Data, Embeddings, and Models"] --> link["Create Dataset-Embedding-Model Links"]
    link --> inspect["Inspect Trajectory Data"]
    inspect --> intrinsic["Run Intrinsic Evaluation"]
    intrinsic --> extrinsic["Run Extrinsic Evaluation"]
    extrinsic --> compare["Compare Representations and Models"]

🧩 TEACH Dashboard Tabs

1. Upload Tab

The first tab contains three main upload sections:

Datasets Import
Embeddings Import
Models Import

These sections define the objects that will be compared across the rest of the dashboard.

Datasets Import

This section uploads trajectory datasets in CSV format.

A dataset should contain at least:

time
lat
lon
trajectory_id

Optionally, a dataset may also include:

location_label

The dashboard allows users to:

upload datasets
rename datasets
remove datasets
make datasets available to other tabs

Removing or renaming a dataset may affect links with embeddings and models.

Embeddings Import

This section uploads embedding files in CSV format.

An embedding file should contain:

a tokenization table
an embedding matrix associated with the tokens

Users can:

upload embeddings
rename embeddings
remove embeddings
link embeddings to datasets

The embedding metadata is saved in:

Emb.csv

Dataset-embedding links are saved in:

Emb#Data.csv

Models Import

This section uploads trained models in .h5 format.

Users can:

upload models
rename models
remove models
link models to datasets

Model metadata is saved in:

Model.csv

Dataset-model links are saved in:

Model#Data.csv

Model-embedding links are saved in:

Model#Emb.csv

2. View Trajectory Data Tab

This tab helps users understand the structure of an uploaded dataset.

It includes two main functions:

Show Statistics
Sampling

Show Statistics

The statistics output may include:

number_of_trajectories
maximum_length_of_trajectories
minimum_length_of_trajectories
average_length_of_trajectories
number_of_distinct_locations

The number_of_distinct_locations attribute is available when the dataset contains location_label.

Sampling

The sampling function plots a subset of trajectories.

If the user chooses $k$ trajectories, TEACH samples and visualizes $k$ trajectories from the selected dataset.

This allows quick inspection of:

trajectory distribution
spatial coverage
trajectory length variation
possible outliers
route concentration

3. Intrinsic Evaluation Tab

Intrinsic evaluation studies embedding quality independently of a downstream predictive task.

In TEACH, intrinsic evaluation asks whether embedding-space similarity is consistent with mobility-space similarity.

Intrinsic Evaluation Workflow

flowchart LR
    choose_embedding["Choose Embedding"] --> choose_dataset["Choose Linked Dataset"]
    choose_dataset --> build_matrices["Build or Load Distance Matrices"]
    build_matrices --> location_eval["Location-Level Evaluation"]
    build_matrices --> trajectory_eval["Trajectory-Level Evaluation"]
    location_eval --> mrr_location["MRR Location"]
    location_eval --> similar_locations["Similar Locations"]
    trajectory_eval --> mrr_trajectory["MRR Trajectory"]
    trajectory_eval --> similar_trajectories["Similar Trajectories"]

MRR Location

This evaluation compares distance matrices associated with each location in the trajectory dataset.

TEACH compares:

Cosine Matrix
Euclidean Matrix
Road Matrix

The goal is to determine whether locations that are close in embedding space are also close according to geographic or road-network distance.

Similar Locations

This function allows the user to select a tokenized location and visualize the most similar locations according to embedding similarity.

The comparison is usually based on cosine distance between embedding vectors.

This helps answer:

Does the embedding preserve spatial proximity?
Are similar tokens located in nearby regions?
Are unexpected neighbors appearing in embedding space?

MRR Trajectory

Trajectory-level MRR evaluates similarity between trajectory representations.

A trajectory vector can be defined as the average of the embedding vectors of its locations:

$$ \mathbf{v}_T = \frac{1}{n}\sum_{i=1}^{n}\mathbf{v}_{l_i} $$

TEACH can compare trajectory distance matrices such as:

Cosine Matrix
DTW Matrix
Edit Distance Matrix

Similar Trajectories

This function allows the user to choose a trajectory and visualize the most similar trajectories according to embedding similarity.

This helps evaluate whether the embedding can group trajectories with similar movement patterns.

Mean Reciprocal Rank

Mean Reciprocal Rank, or MRR, evaluates how highly the first relevant item appears in a ranked list.

For a set of queries $Q$:

$$ MRR = \frac{1}{|Q|}\sum_{i=1}^{|Q|}\frac{1}{rank_i} $$

where $rank_i$ is the rank position of the first relevant result for query $i$.

Higher MRR indicates that relevant items tend to appear near the top of the ranking.

4. Extrinsic Evaluation Tab

Extrinsic evaluation compares embeddings through a downstream task.

In TEACH, the downstream task is related to location prediction.

The user selects trained models that were imported and linked to datasets.

For each selected model, TEACH loads the sequence-type trajectory dataset generated from the linked lat/lon data and evaluates predictive performance.

Extrinsic Evaluation Workflow

flowchart LR
    select_models["Select Linked Models"] --> load_sequences["Load Sequence Trajectories"]
    load_sequences --> load_embeddings["Load Associated Embeddings"]
    load_embeddings --> run_prediction["Run Location Prediction"]
    run_prediction --> compute_metrics["Compute Accuracy and F1-Score"]
    compute_metrics --> comparison_table["Generate Comparative DataFrame"]

Metrics

The extrinsic evaluation output includes:

three accuracy variants
F1-Score

The exact interpretation of each accuracy variant depends on the model and evaluation implementation.

The F1-score combines precision and recall:

$$ F_1 = 2 \cdot \frac{precision \cdot recall}{precision + recall} $$

🧱 TEACH Architecture

The Teach_Main class is imported into the Jupyter Notebook and orchestrates the dashboard.

The following classes are used as components:

View_Trajectory_Output
Intrinsic_Evaluation_Output
Extrinsic_Evaluation_Output

These classes are connected because changes in uploaded data, embeddings, or models affect multiple tabs.

TEACH class diagram showing the relationship between the main dashboard class and the output/evaluation components.

Architecture Flow

classDiagram
    class Teach_Main {
        +datasets
        +embeddings
        +models
        +load_metadata()
        +update_tabs()
    }

    class View_Trajectory_Output {
        +show_statistics()
        +sample_trajectories()
    }

    class Intrinsic_Evaluation_Output {
        +mrr_location()
        +similar_locations()
        +mrr_trajectory()
        +similar_trajectories()
    }

    class Extrinsic_Evaluation_Output {
        +evaluate_models()
        +generate_metrics_dataframe()
    }

    Teach_Main --> View_Trajectory_Output
    Teach_Main --> Intrinsic_Evaluation_Output
    Teach_Main --> Extrinsic_Evaluation_Output

📐 Distance and Similarity Measures

TEACH compares embeddings and trajectories using multiple distance concepts.

Cosine Similarity

For two vectors $\mathbf{a}$ and $\mathbf{b}$:

$$ cos(\theta) = \frac{\mathbf{a}\cdot\mathbf{b}}{|\mathbf{a}||\mathbf{b}|} $$

Cosine distance can be defined as:

$$ d_{cos}(\mathbf{a}, \mathbf{b}) = 1 - cos(\theta) $$

Euclidean Distance

$$ d_E(\mathbf{a}, \mathbf{b}) = \sqrt{\sum_{i=1}^{n}(a_i - b_i)^2} $$

Road Network Distance

Road distance measures how far two locations are along the road network rather than through straight-line distance.

This is useful because two locations may be geographically close but far apart by road connectivity.

Dynamic Time Warping

DTW compares sequences that may have different speeds or temporal alignments.

It is useful for comparing trajectories with similar shapes but different sampling rates or movement speeds.

Edit Distance

Edit distance compares sequences according to insertions, deletions, and substitutions.

For trajectory sequences, this can be used to compare symbolic movement patterns.

⏱️ Computational Complexity Notes

Let:

N = number of trajectories
L = average trajectory length
M = number of unique locations or tokens
D = embedding dimension
K = number of selected models

Task	Typical Time Complexity	Typical Space Complexity	Notes
Dataset statistics	$O(N \cdot L)$	$O(1)$ extra	Scans trajectories and lengths.
Sampling trajectories	$O(K_s \cdot L)$	$O(K_s \cdot L)$	$K_s$ is the number of sampled trajectories.
Location cosine matrix	$O(M^2 \cdot D)$	$O(M^2)$	Pairwise vector similarity.
Location Euclidean matrix	$O(M^2 \cdot D)$	$O(M^2)$	Pairwise vector distance.
Road distance matrix	Depends on graph shortest paths	$O(M^2)$	Cost depends on road network size and algorithm.
Trajectory vector averaging	$O(N \cdot L \cdot D)$	$O(N \cdot D)$	Builds trajectory embeddings by averaging token vectors.
Trajectory cosine matrix	$O(N^2 \cdot D)$	$O(N^2)$	Pairwise trajectory vector similarity.
DTW matrix	$O(N^2 \cdot L^2)$	$O(N^2)$	Pairwise sequence alignment.
Edit distance matrix	$O(N^2 \cdot L^2)$	$O(N^2)$	Pairwise symbolic sequence distance.
Extrinsic model evaluation	Depends on model architecture	Depends on model	Evaluates selected neural models.

🧪 Behavior Summary

The TEACH workflow can be summarized as:

1. Start the Voilà dashboard.
2. Upload trajectory datasets.
3. Upload embedding files.
4. Upload trained neural models.
5. Link datasets to embeddings and models.
6. Inspect dataset statistics and sampled trajectories.
7. Run intrinsic evaluation over locations and trajectories.
8. Run extrinsic evaluation over prediction models.
9. Compare representations and model performance.
10. Select the most appropriate trajectory embedding for future experiments.

🧭 Suggested Study Path

A good study order for this repository is:

1. Trajectory data representation
2. Latitude/longitude trajectory format
3. Sequence trajectory format
4. Tokenization of locations
5. Word embeddings and NLP analogy
6. Trajectory embeddings
7. Cosine and Euclidean distance
8. Road-network distance
9. Dynamic Time Warping
10. Edit distance
11. Mean Reciprocal Rank
12. Intrinsic evaluation
13. Neural location prediction
14. Accuracy and F1-score
15. Extrinsic evaluation
16. Dashboard workflow with Voilà

🧰 Technologies and Tools

Tool / Library	Purpose
Python	Main programming language.
Jupyter Notebook	Interactive development environment.
Voilà	Turns `teach.ipynb` into a dashboard.
IPyWidgets	Interactive UI controls.
Pandas	Metadata tables and datasets.
NumPy	Vector and matrix operations.
TensorFlow / Keras	Neural model loading and evaluation.
scikit-learn	Metrics and machine learning utilities.
PyMove	Mobility data processing and visualization.
Folium	Interactive trajectory maps.
OSMnx	Road network extraction and analysis.
NetworkX	Graph algorithms and network structures.
NLTK	NLP support utilities.
Matplotlib	Plots and metric visualizations.

⚠️ Troubleshooting

Voilà does not start

Check whether Voilà is installed inside the active environment:

conda activate teach_env
voila --version

Then run again:

voila teach.ipynb

TensorFlow or Keras model loading fails

Make sure the model was saved in a compatible .h5 format and that the TensorFlow/Keras versions match the expected environment.

OSMnx or road network loading fails

Road network operations may require internet access when downloading OpenStreetMap data.

If the network was previously cached, check whether the cache folder is available and readable.

Distance matrices take too long

Pairwise matrices can be expensive for large datasets.

Consider:

sampling trajectories
reducing the number of locations
using cached matrices
running heavy computations before using the dashboard

🖼️ Image Credits and Licenses

Image	Author / Source	License information	Link
Word Embedding Illustration	Fschwarzentruber / Wikimedia Commons	CC BY-SA 4.0	File page
GPS Track Visualization Example	Wikimedia Commons	See file page for licensing details	File path
Basic User Experience Flow	Local repository image	Repository documentation asset	`Basic_User_Experience_Flow.png`
TEACH Class Diagram	Local repository image	Repository documentation asset	`TEACH_Class_Diagram.png`

📚 References and Further Reading

Main Paper

Cruz, L. A., Coelho da Silva, T. L., Magalhães, R. P., Melo, W. C. D., Cordeiro, M., de Macedo, J. A. F., and Zeitouni, K.
Modeling Trajectories Obtained from External Sensors for Location Prediction via NLP Approaches.
Sensors, 22(19), 7475, 2022.

Read the open-access article
DOI: 10.3390/s22197475
Main topics: trajectory representation learning, sensor embeddings, BERT MLM, Word2Vec, LSTM, next-location prediction, and intrinsic/extrinsic evaluation.

BibTeX

@article{cruz2022modeling,
  title     = {Modeling Trajectories Obtained from External Sensors for Location Prediction via NLP Approaches},
  author    = {Cruz, Livia Almada and Coelho da Silva, Ticiana Linhares and Magalhaes, Regis Pires and Melo, Wilken Charles Dantas and Cordeiro, Matheus and de Macedo, Jose Antonio Fernandes and Zeitouni, Karine},
  journal   = {Sensors},
  volume    = {22},
  number    = {19},
  pages     = {7475},
  year      = {2022},
  publisher = {MDPI},
  doi       = {10.3390/s22197475}
}

Tools and Libraries

Resource	Main Topic	Why it is useful	Link
PyMove	Trajectory processing	Provides tools for trajectory and spatio-temporal data processing and visualization.	GitHub repository
OSMnx	Street networks	Used to download, model, analyze, and visualize OpenStreetMap street networks and geospatial features.	OSMnx docs
NetworkX	Graph algorithms	Supports graph structures, graph algorithms, and network analysis.	NetworkX docs
Folium	Interactive maps	Allows Python data to be visualized with Leaflet maps.	Folium docs
Voilà	Dashboard serving	Converts Jupyter notebooks into standalone interactive dashboards.	Voilà docs
TensorFlow	Neural models	Backend for machine learning models.	TensorFlow
Keras	Neural network API	Model loading and evaluation.	Keras
scikit-learn	ML metrics	Metrics and utilities for model evaluation.	scikit-learn

✅ Summary

TEACH is an interactive benchmark for comparing trajectory embeddings.

It connects:

trajectory data
location tokens
trajectory embeddings
NLP-inspired representation learning
intrinsic evaluation
extrinsic evaluation
location prediction
interactive dashboards
road-network distances
sequence similarity

The main emphasis is:

Upload datasets, embeddings, and models.
Inspect trajectory data.
Compare embeddings intrinsically.
Evaluate models extrinsically.
Select better trajectory representations before downstream learning.

Name		Name	Last commit message	Last commit date
app		app
Basic_User_Experience_Flow.drawio		Basic_User_Experience_Flow.drawio
Basic_User_Experience_Flow.png		Basic_User_Experience_Flow.png
README.md		README.md
TEACH Class Diagram.drawio		TEACH Class Diagram.drawio
TEACH_Class_Diagram.drawio		TEACH_Class_Diagram.drawio
TEACH_Class_Diagram.png		TEACH_Class_Diagram.png

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