Generation of Synthetic Trajectory Microdata from Language Models

Blanco-Justicia, Alberto; Jebreel, Najeeb Moharram; Manjón, Jesús A.; Domingo-Ferrer, Josep

doi:10.1007/978-3-031-13945-1_13

Cited by 4 publications

(5 citation statements)

References 22 publications

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“…A conceptually similar model was proposed by Sakuma et al (2021), where the variational encoder was replaced with principal component analysis for dimension reduction and a Laplace mechanism [16]. Blanco-Justicia et al (2022) applied the bidirectional LSTM to synthesize mobility data [17]. Their results were validated using several distance/visitation metrics at the aggregate level.…”

Section: Machine-learning Based Methodsmentioning

confidence: 99%

Learning Daily Human Mobility with a Transformer-Based Model

Wang,

Osaragi

2024

IJGI

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The generation and prediction of daily human mobility patterns have raised significant interest in many scientific disciplines. Using various data sources, previous studies have examined several deep learning frameworks, such as the RNN and GAN, to synthesize human movements. Transformer models have been used frequently for image analysis and language processing, while the applications of these models on human mobility are limited. In this study, we construct a transformer model, including a self-attention-based embedding component and a Generative Pre-trained Transformer component, to learn daily movements. The embedding component takes regional attributes as input and learns regional relationships to output vector representations for locations, enabling the second component to generate different mobility patterns for various scenarios. The proposed model shows satisfactory performance for generating and predicting human mobilities, superior to a Long Short-Term Memory model in terms of several aggregated statistics and sequential characteristics. Further examination indicates that the proposed model learned the spatial structure and the temporal relationship of human mobility, which generally agrees with our empirical analysis. This observation suggests that the transformer framework can be a promising model for learning and understanding human movements.

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Section: Machine-learning Based Methodsmentioning

confidence: 99%

Learning Daily Human Mobility with a Transformer-Based Model

Wang,

Osaragi

2024

IJGI

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“…Our selection process took into consideration various factors, especially the clarity of reasoning behind their approaches, promising results demonstrated in respective evaluations, and the availability of source code, either as open source or obtainable upon request. The selection process resulted in the following models: AdaTrace [9], PrivTrace [22], BiLSTM [4], DP-Loc [14], and TrajGAIL [6].…”

Section: Synthesis Algorithmsmentioning

confidence: 99%

“…Blanco-Justicia et al [4] propose the utilization of a BiLSTM, a bidirectional long short-term memory network, a recurrent architecture that has proven superior performance in modeling sequences such as time series or natural language. Considering a visited location as a word and a trip as a sentence, the principles of autoregressive text generation using RNNs can be adopted to generate trips.…”

Section: Synthesis Algorithmsmentioning

confidence: 99%

“…The BiLSTM uses a similar neural network architecture as [4]. To speed up training and prevent overfitting, we implement an early stopping criterion causing the training to stop if the validation loss increases or stagnates for more than one epoch.…”

Section: Configurations For Synthesis Modelsmentioning

confidence: 99%

“…Thus, for synthetic data to be usable for map matching we require the distance between consecutive points for the majority of trips to be within the magnitude of the spatial resolution used for data generation. 4 Table 1 shows the distribution of distances between two consecutive points for each synthetic dataset (excluding repetition of identical locations). While AdaTrace (DP), PrivTrace (DP), and BiLSTM yield distances in the required order of magnitude, DP-Loc proves not suitable for map matching with a median distance of 1.4𝑘𝑚 (1.5𝑘𝑚 for the DP version).…”

Section: Matching Abilitymentioning

confidence: 99%

See 2 more Smart Citations

Reconsidering utility: unveiling the limitations of synthetic mobility data generation algorithms in real-life scenarios

Kapp,

Mihaljevic

2023

Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems

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In recent years, there has been a surge in the development of models for the generation of synthetic mobility data. These models aim to facilitate the sharing of data while safeguarding privacy, all while ensuring high utility and flexibility regarding potential applications. However, current utility evaluation methods fail to fully account for real-life requirements.We evaluate the utility of five state-of-the-art synthesis approaches, each with and without the incorporation of differential privacy (DP) guarantees, in terms of real-world applicability. Specifically, we focus on so-called trip data that encode fine granular urban movements such as GPS-tracked taxi rides. Such data prove particularly valuable for downstream tasks at the road network level. Thus, our initial step involves appropriately map matching the synthetic data and subsequently comparing the resulting trips with those generated by the routing algorithm implemented in OpenStreetMap, which serves as an efficient and privacy-friendly baseline.Out of the five evaluated models, one fails to produce data within reasonable computation time and another generates too many jumps to meet the requirements for map matching. The remaining three models succeed to a certain degree in maintaining spatial distribution, one even with DP guarantees. However, all models struggle to produce meaningful sequences of geo-locations with reasonable trip lengths and to model traffic flow at intersections accurately. It is important to note that trip data encompasses various relevant characteristics beyond spatial distribution, such as temporal information, all of which are discarded by these models. Consequently, our results imply that current synthesis models fall short in their promise of high utility and flexibility. CCS CONCEPTS• Security and privacy → Usability in security and privacy; Data anonymization and sanitization; • Applied computing → Transportation.

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Generating Spatiotemporal Trajectories with GANs and Conditional GANs

Zhao,

Wang

2023

Communications in Computer and Information Science

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Generation of Synthetic Trajectory Microdata from Language Models

Cited by 4 publications

References 22 publications

Learning Daily Human Mobility with a Transformer-Based Model

Learning Daily Human Mobility with a Transformer-Based Model

Reconsidering utility: unveiling the limitations of synthetic mobility data generation algorithms in real-life scenarios

Generating Spatiotemporal Trajectories with GANs and Conditional GANs

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