Semantic Trajectory Data Mining with LLM-Informed POI Classification

• URL: http://arxiv.org/abs/2405.11715v1
• Date: Mon, 20 May 2024 01:29:45 GMT
• Title: Semantic Trajectory Data Mining with LLM-Informed POI Classification
• Authors: Yifan Liu, Chenchen Kuai, Haoxuan Ma, Xishun Liao, Brian Yueshuai He, Jiaqi Ma,
• Abstract summary: We introduce a novel pipeline for human travel trajectory mining using semantic information. Our approach achieves a 93.4% accuracy and a 96.1% F-1 score in POI classification, and a 91.7% accuracy with a 92.3% F-1 score in activity inference.
• Score: 11.90100976089832
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human travel trajectory mining is crucial for transportation systems, enhancing route optimization, traffic management, and the study of human travel patterns. Previous rule-based approaches without the integration of semantic information show a limitation in both efficiency and accuracy. Semantic information, such as activity types inferred from Points of Interest (POI) data, can significantly enhance the quality of trajectory mining. However, integrating these insights is challenging, as many POIs have incomplete feature information, and current learning-based POI algorithms require the integrity of datasets to do the classification. In this paper, we introduce a novel pipeline for human travel trajectory mining. Our approach first leverages the strong inferential and comprehension capabilities of large language models (LLMs) to annotate POI with activity types and then uses a Bayesian-based algorithm to infer activity for each stay point in a trajectory. In our evaluation using the OpenStreetMap (OSM) POI dataset, our approach achieves a 93.4% accuracy and a 96.1% F-1 score in POI classification, and a 91.7% accuracy with a 92.3% F-1 score in activity inference.

Related papers

• Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond [58.63558696061679]
  Trajectory computing is crucial in various practical applications such as location services, urban traffic, and public safety. We present a review of development and recent advances in deep learning for trajectory computing (DL4Traj) Notably, we encapsulate recent advancements in Large Language Models (LLMs) that hold potential to augment trajectory computing.
  arXiv  Detail & Related papers  (2024-03-21T05:57:27Z)
• Optimizing Bus Travel: A Novel Approach to Feature Mining with P-KMEANS and P-LDA Algorithms [12.67101421854941]
  This study presents a bus travel feature extraction method rooted in Point of Interest (POI) data. Our method successfully mines the diverse aspects of bus travel, such as age, occupation, gender, sports, cost, safety, and personality traits.
  arXiv  Detail & Related papers  (2023-12-04T07:21:27Z)
• ALP: Action-Aware Embodied Learning for Perception [60.64801970249279]
  We introduce Action-Aware Embodied Learning for Perception (ALP) ALP incorporates action information into representation learning through a combination of optimizing a reinforcement learning policy and an inverse dynamics prediction objective. We show that ALP outperforms existing baselines in several downstream perception tasks.
  arXiv  Detail & Related papers  (2023-06-16T21:51:04Z)
• Masked Path Modeling for Vision-and-Language Navigation [41.7517631477082]
  Vision-and-language navigation (VLN) agents are trained to navigate in real-world environments by following natural language instructions. Previous approaches have attempted to address this issue by introducing additional supervision during training. We introduce a masked path modeling (MPM) objective, which pretrains an agent using self-collected data for downstream navigation tasks.
  arXiv  Detail & Related papers  (2023-05-23T17:20:20Z)
• Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information [100.03188187735624]
  We introduce a novel approach based on PLMs and pointwise V-information (PVI), a metric that can measure the usefulness of a datapoint for training a model. Our method first fine-tunes a PLM on a small seed of training data and then synthesizes new datapoints - utterances that correspond to given intents. Our method is thus able to leverage the expressive power of large language models to produce diverse training data.
  arXiv  Detail & Related papers  (2023-02-10T07:37:49Z)
• Improving Few-Shot Generalization by Exploring and Exploiting Auxiliary Data [100.33096338195723]
  We focus on Few-shot Learning with Auxiliary Data (FLAD) FLAD assumes access to auxiliary data during few-shot learning in hopes of improving generalization. We propose two algorithms -- EXP3-FLAD and UCB1-FLAD -- and compare them with prior FLAD methods that either explore or exploit.
  arXiv  Detail & Related papers  (2023-02-01T18:59:36Z)
• A Semi-supervised Approach for Activity Recognition from Indoor Trajectory Data [0.822021749810331]
  We consider the task of classifying the activities of moving objects from their noisy indoor trajectory data in a collaborative manufacturing environment. We present a semi-supervised machine learning approach that first applies an information theoretic criterion to partition a long trajectory into a set of segments. The segments are then labelled automatically based on a constrained hierarchical clustering method.
  arXiv  Detail & Related papers  (2023-01-09T01:20:50Z)
• Exploring Map-based Features for Efficient Attention-based Vehicle Motion Prediction [3.222802562733787]
  Motion prediction of multiple agents is a crucial task in arbitrarily complex environments. We show how to achieve competitive performance on the Argoverse 1.0 Benchmark using efficient attention-based models.
  arXiv  Detail & Related papers  (2022-05-25T22:38:11Z)
• Bi-level Alignment for Cross-Domain Crowd Counting [113.78303285148041]
  Current methods rely on external data for training an auxiliary task or apply an expensive coarse-to-fine estimation. We develop a new adversarial learning based method, which is simple and efficient to apply. We evaluate our approach on five real-world crowd counting benchmarks, where we outperform existing approaches by a large margin.
  arXiv  Detail & Related papers  (2022-05-12T02:23:25Z)
• PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map [58.53373202647576]
  We propose PreTraM, a self-supervised pre-training scheme for trajectory forecasting. It consists of two parts: 1) Trajectory-Map Contrastive Learning, where we project trajectories and maps to a shared embedding space with cross-modal contrastive learning, and 2) Map Contrastive Learning, where we enhance map representation with contrastive learning on large quantities of HD-maps. On top of popular baselines such as AgentFormer and Trajectron++, PreTraM boosts their performance by 5.5% and 6.9% relatively in FDE-10 on the challenging nuScenes dataset.
  arXiv  Detail & Related papers  (2022-04-21T23:01:21Z)
• Mobility Inference on Long-Tailed Sparse Trajectory [2.4444287331956898]
  We propose a single trajectory inference algorithm that utilizes a generic long-tailed sparsity pattern in the large-scale trajectory data. The algorithm guarantees a 100% precision in the stay/travel inference with a provable lower-bound in the recall. Evaluations with three trajectory data sets of 40 million urban users validate the performance guarantees of the proposed inference algorithm.
  arXiv  Detail & Related papers  (2020-01-21T16:32:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.