Online Co-movement Pattern Prediction in Mobility Data
- URL: http://arxiv.org/abs/2102.08870v1
- Date: Wed, 17 Feb 2021 17:01:32 GMT
- Title: Online Co-movement Pattern Prediction in Mobility Data
- Authors: Andreas Tritsarolis, Eva Chondrodima, Panagiotis Tampakis and Aggelos
Pikrakis
- Abstract summary: We provide an accurate solution to the problem of Online Prediction of Co-movement patterns.
In order to calculate the accuracy of our solution, we propose a co-movement pattern similarity measure.
The accuracy of our solution is demonstrated experimentally over a real dataset from the maritime domain.
- Score: 1.5790464310310084
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Predictive analytics over mobility data are of great importance since they
can assist an analyst to predict events, such as collisions, encounters,
traffic jams, etc. A typical example of such analytics is future location
prediction, where the goal is to predict the future location of a moving
object,given a look-ahead time. What is even more challenging is being able to
accurately predict collective behavioural patterns of movement, such as
co-movement patterns. In this paper, we provide an accurate solution to the
problem of Online Prediction of Co-movement Patterns. In more detail, we split
the original problem into two sub-problems, namely Future Location Prediction
and Evolving Cluster Detection. Furthermore, in order to be able to calculate
the accuracy of our solution, we propose a co-movement pattern similarity
measure, which facilitates us to match the predicted clusters with the actual
ones. Finally, the accuracy of our solution is demonstrated experimentally over
a real dataset from the maritime domain.
Related papers
- Physics-guided Active Sample Reweighting for Urban Flow Prediction [75.24539704456791]
Urban flow prediction is a nuanced-temporal modeling that estimates the throughput of transportation services like buses, taxis and ride-driven models.
Some recent prediction solutions bring remedies with the notion of physics-guided machine learning (PGML)
We develop a atized physics-guided network (PN), and propose a data-aware framework Physics-guided Active Sample Reweighting (P-GASR)
arXiv Detail & Related papers (2024-07-18T15:44:23Z) - JRDB-Traj: A Dataset and Benchmark for Trajectory Forecasting in Crowds [79.00975648564483]
Trajectory forecasting models, employed in fields such as robotics, autonomous vehicles, and navigation, face challenges in real-world scenarios.
This dataset provides comprehensive data, including the locations of all agents, scene images, and point clouds, all from the robot's perspective.
The objective is to predict the future positions of agents relative to the robot using raw sensory input data.
arXiv Detail & Related papers (2023-11-05T18:59:31Z) - Implicit Occupancy Flow Fields for Perception and Prediction in
Self-Driving [68.95178518732965]
A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants.
Existing works either perform object detection followed by trajectory of the detected objects, or predict dense occupancy and flow grids for the whole scene.
This motivates our unified approach to perception and future prediction that implicitly represents occupancy and flow over time with a single neural network.
arXiv Detail & Related papers (2023-08-02T23:39:24Z) - Context-aware multi-head self-attentional neural network model for next
location prediction [19.640761373993417]
We utilize a multi-head self-attentional (A) neural network that learns location patterns from historical location visits.
We demonstrate that proposed the model outperforms other state-of-the-art prediction models.
We believe that the proposed model is vital for context-aware mobility prediction.
arXiv Detail & Related papers (2022-12-04T23:40:14Z) - How do you go where? Improving next location prediction by learning
travel mode information using transformers [6.003006906852134]
We propose a transformer decoder-based neural network to predict the next location an individual will visit based on historical locations, time, and travel modes.
In particular, the prediction of the next travel mode is designed as an auxiliary task to help guide the network's learning.
Our experiments show that the proposed method significantly outperforms other state-of-the-art next location prediction methods.
arXiv Detail & Related papers (2022-10-08T19:36:58Z) - Predicting Future Occupancy Grids in Dynamic Environment with
Spatio-Temporal Learning [63.25627328308978]
We propose a-temporal prediction network pipeline to generate future occupancy predictions.
Compared to current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds.
We publicly release our grid occupancy dataset based on nulis to support further research.
arXiv Detail & Related papers (2022-05-06T13:45:32Z) - Forecasting from LiDAR via Future Object Detection [47.11167997187244]
We propose an end-to-end approach for detection and motion forecasting based on raw sensor measurement.
By linking future and current locations in a many-to-one manner, our approach is able to reason about multiple futures.
arXiv Detail & Related papers (2022-03-30T13:40:28Z) - You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory
Prediction [52.442129609979794]
Recent deep learning approaches for trajectory prediction show promising performance.
It remains unclear which features such black-box models actually learn to use for making predictions.
This paper proposes a procedure that quantifies the contributions of different cues to model performance.
arXiv Detail & Related papers (2021-10-11T14:24:15Z) - AMENet: Attentive Maps Encoder Network for Trajectory Prediction [35.22312783822563]
Trajectory prediction is critical for applications of planning safe future movements.
We propose an end-to-end generative model named Attentive Maps Network (AMENet)
AMENet encodes the agent's motion and interaction information for accurate and realistic multi-path trajectory prediction.
arXiv Detail & Related papers (2020-06-15T10:00:07Z) - Physically constrained short-term vehicle trajectory forecasting with
naive semantic maps [6.85316573653194]
We propose a model that learns to extract relevant road features from semantic maps as well as general motion of agents.
We show that our model is not only capable of anticipating future motion whilst taking into consideration road boundaries, but can also effectively and precisely predict trajectories for a longer time horizon than initially trained for.
arXiv Detail & Related papers (2020-06-09T09:52:44Z) - Spatiotemporal Relationship Reasoning for Pedestrian Intent Prediction [57.56466850377598]
Reasoning over visual data is a desirable capability for robotics and vision-based applications.
In this paper, we present a framework on graph to uncover relationships in different objects in the scene for reasoning about pedestrian intent.
Pedestrian intent, defined as the future action of crossing or not-crossing the street, is a very crucial piece of information for autonomous vehicles.
arXiv Detail & Related papers (2020-02-20T18:50:44Z)
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.