MPE: A Mobility Pattern Embedding Model for Predicting Next Locations

• URL: http://arxiv.org/abs/2003.07782v1
• Date: Mon, 16 Mar 2020 05:24:32 GMT
• Title: MPE: A Mobility Pattern Embedding Model for Predicting Next Locations
• Authors: Meng Chen, Xiaohui Yu, Yang Liu
• Abstract summary: We propose a novel mobility pattern embedding model called MPE to shed the light on people's mobility patterns in traffic trajectory data. MPE is capable of casting various types of information (object, location and time) to an integrated low-dimensional latent space. This embedding model opens the door to a wide range of applications such as next location prediction and visualization.
• Score: 16.010576606023417
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The wide spread use of positioning and photographing devices gives rise to a deluge of traffic trajectory data (e.g., vehicle passage records and taxi trajectory data), with each record having at least three attributes: object ID, location ID, and time-stamp. In this paper, we propose a novel mobility pattern embedding model called MPE to shed the light on people's mobility patterns in traffic trajectory data from multiple aspects, including sequential, personal, and temporal factors. MPE has two salient features: (1) it is capable of casting various types of information (object, location and time) to an integrated low-dimensional latent space; (2) it considers the effect of ``phantom transitions'' arising from road networks in traffic trajectory data. This embedding model opens the door to a wide range of applications such as next location prediction and visualization. Experimental results on two real-world datasets show that MPE is effective and outperforms the state-of-the-art methods significantly in a variety of tasks.

Related papers

• FUSED-Net: Enhancing Few-Shot Traffic Sign Detection with Unfrozen Parameters, Pseudo-Support Sets, Embedding Normalization, and Domain Adaptation [2.111102681327218]
  We present 'FUSED-Net', built-upon Faster RCNN for traffic sign detection. Unlike traditional approaches, we keep all parameters unfrozen during training, enabling FUSED-Net to learn from limited samples. We achieve 2.4x, 2.2x, 1.5x, and 1.3x improvements of mAP in 1-shot, 3-shot, 5-shot, and 10-shot scenarios, respectively.
  arXiv  Detail & Related papers  (2024-09-23T09:34:42Z)
• DeTra: A Unified Model for Object Detection and Trajectory Forecasting [68.85128937305697]
  Our approach formulates the union of the two tasks as a trajectory refinement problem. To tackle this unified task, we design a refinement transformer that infers the presence, pose, and multi-modal future behaviors of objects. In our experiments, we observe that ourmodel outperforms the state-of-the-art on Argoverse 2 Sensor and Open dataset.
  arXiv  Detail & Related papers  (2024-06-06T18:12:04Z)
• Digital Twin Mobility Profiling: A Spatio-Temporal Graph Learning Approach [9.56255685195115]
  Mobility profiling can extract potential patterns in urban traffic from mobility data. Digital twin (DT) technology paves the way for cost-effective and performance-optimised management. We propose a digital twin mobility profiling framework to learn node profiles on a mobilitytemporal network DT model.
  arXiv  Detail & Related papers  (2024-02-06T06:37:43Z)
• Layout Sequence Prediction From Noisy Mobile Modality [53.49649231056857]
  Trajectory prediction plays a vital role in understanding pedestrian movement for applications such as autonomous driving and robotics. Current trajectory prediction models depend on long, complete, and accurately observed sequences from visual modalities. We propose LTrajDiff, a novel approach that treats objects obstructed or out of sight as equally important as those with fully visible trajectories.
  arXiv  Detail & Related papers  (2023-10-09T20:32:49Z)
• A Fast and Map-Free Model for Trajectory Prediction in Traffics [2.435517936694533]
  This paper proposes an efficient trajectory prediction model that is not dependent on traffic maps. By comprehensively utilizing attention mechanism, LSTM, graph convolution network and temporal transformer, our model is able to learn rich dynamic and interaction information of all agents. Our model achieves the highest performance when comparing with existing map-free methods and also exceeds most map-based state-of-the-art methods on the Argoverse dataset.
  arXiv  Detail & Related papers  (2023-07-19T08:36:31Z)
• Newell's theory based feature transformations for spatio-temporal traffic prediction [0.0]
  We propose a traffic flow physics-based transformation feature for Deep learning (DL) models for traffic flow forecasting. This transformation incorporates Newell's uncongested and congested filters of traffic flows at the target locations, enabling the models to learn broader dynamics of the system. An important advantage of our framework is its ability to be transferred to new locations where data is unavailable.
  arXiv  Detail & Related papers  (2023-07-12T06:31:43Z)
• MotionTrack: Learning Motion Predictor for Multiple Object Tracking [68.68339102749358]
  We introduce a novel motion-based tracker, MotionTrack, centered around a learnable motion predictor. Our experimental results demonstrate that MotionTrack yields state-of-the-art performance on datasets such as Dancetrack and SportsMOT.
  arXiv  Detail & Related papers  (2023-06-05T04:24:11Z)
• Modeling Continuous Motion for 3D Point Cloud Object Tracking [54.48716096286417]
  This paper presents a novel approach that views each tracklet as a continuous stream. At each timestamp, only the current frame is fed into the network to interact with multi-frame historical features stored in a memory bank. To enhance the utilization of multi-frame features for robust tracking, a contrastive sequence enhancement strategy is proposed.
  arXiv  Detail & Related papers  (2023-03-14T02:58:27Z)
• D2-TPred: Discontinuous Dependency for Trajectory Prediction under Traffic Lights [68.76631399516823]
  We present a trajectory prediction approach with respect to traffic lights, D2-TPred, using a spatial dynamic interaction graph (SDG) and a behavior dependency graph (BDG) Our experimental results show that our model achieves more than 20.45% and 20.78% in terms of ADE and FDE, respectively, on VTP-TL.
  arXiv  Detail & Related papers  (2022-07-21T10:19:07Z)
• Joint Spatial-Temporal and Appearance Modeling with Transformer for Multiple Object Tracking [59.79252390626194]
  We propose a novel solution named TransSTAM, which leverages Transformer to model both the appearance features of each object and the spatial-temporal relationships among objects. The proposed method is evaluated on multiple public benchmarks including MOT16, MOT17, and MOT20, and it achieves a clear performance improvement in both IDF1 and HOTA.
  arXiv  Detail & Related papers  (2022-05-31T01:19:18Z)
• TraLFM: Latent Factor Modeling of Traffic Trajectory Data [16.010576606023417]
  We propose a novel generative model called TraLFM to mine human mobility patterns underlying traffic trajectories. TraLFM is based on three key observations: (1) human mobility patterns are reflected by the sequences of locations in the trajectories; (2) human mobility patterns vary with people; and (3) human mobility patterns tend to be cyclical and change over time.
  arXiv  Detail & Related papers  (2020-03-16T04:41:39Z)

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.