A Personal Location Prediction Method to Solve the Problem of Sparse Trajectory Data

2019 ◽  
Author(s):  
Fan Li ◽  
Qingquan Li ◽  
Zhen Li ◽  
Zhao Huang ◽  
Xiaomeng Chang ◽  
...  
Keyword(s):  
Prediction Method ◽  
Location Prediction ◽  
Trajectory Data

A location prediction method for work-in-process based on frequent trajectory patterns

2017 ◽  
Vol 233 (1) ◽  
pp. 306-320 ◽  
Author(s):  
Haoshu Cai ◽  
Yu Guo ◽  
Kun Lu
Keyword(s):  
Data Model ◽  
Data Analytics ◽  
Moving Objects ◽  
Prediction Method ◽  
Shop Floor ◽  
Location Prediction ◽  
Trajectory Data ◽  
Logical Space ◽  
Work In Process ◽  
Series Of Experiments

In the data-rich manufacturing environment, the production process of work-in-process is described and presented by trajectories with manufacturing significance. However, advanced approaches for work-in-process trajectory data analytics and prediction are comparatively inadequate. However, the location prediction of moving objects has drawn great attention in the manufacturing field. Yet most approaches for predicting future locations of objects are originally applied in geography domain. When applied to manufacturing shop floor, the prediction results lack manufacturing significance. This article focuses on predicting the next locations of work-in-process in the workshop. First, a data model is introduced to map the geographic trajectories into the logical space, in order to convert the manufacturing information into logical features. Based on the data model, a prediction method is proposed to predict the next locations using frequent trajectory patterns. A series of experiments are performed to examine the prediction method. The experiment results illustrate the impacts of the user-defined factors and prove that the proposed method is effective and efficient.

Origin-Aware Location Prediction Based on Historical Vehicle Trajectories

2022 ◽  
Vol 13 (1) ◽  
pp. 1-18
Author(s):  
Meng Chen ◽  
Qingjie Liu ◽  
Weiming Huang ◽  
Teng Zhang ◽  
Yixuan Zuo ◽  
...  
Keyword(s):  
Travel Time ◽  
Markov Models ◽  
Prediction Method ◽  
Time Difference ◽  
Location Prediction ◽  
Trajectory Data ◽  
Difference Model ◽  
The Difference ◽  
Real World Datasets ◽  
Joint Prediction

Next location prediction is of great importance for many location-based applications and provides essential intelligence to various businesses. In previous studies, a common approach to next location prediction is to learn the sequential transitions with massive historical trajectories based on conditional probability. Nevertheless, due to the time and space complexity, these methods (e.g., Markov models) only utilize the just passed locations to predict next locations, neglecting earlier passed locations in the trajectory. In this work, we seek to enhance the prediction performance by incorporating the travel time from all the passed locations in the query trajectory to each candidate next location. To this end, we propose a novel prediction method, namely the Travel Time Difference Model, which exploits the difference between the shortest travel time and the actual travel time to predict next locations. Moreover, we integrate the Travel Time Difference Model with a Sequential and Temporal Predictor to yield a joint model. The joint prediction model integrates local sequential transitions, temporal regularity, and global travel time information in the trajectory for the next location prediction problem. We have conducted extensive experiments on two real-world datasets: the vehicle passage record data and the taxi trajectory data. The experimental results demonstrate significant improvements in prediction accuracy over baseline methods.

scholarly journals A geographical location prediction method based on continuous time series Markov model

PLoS ONE ◽  
2018 ◽  
Vol 13 (11) ◽  
pp. e0207063 ◽  
Author(s):  
Yongping Du ◽  
Chencheng Wang ◽  
Yanlei Qiao ◽  
Dongyue Zhao ◽  
Wenyang Guo
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Continuous Time ◽  
Geographical Location ◽  
Prediction Method ◽  
Location Prediction

scholarly journals HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction

2018 ◽  
Author(s):  
Dejiang Kong ◽  
Fei Wu
Keyword(s):  
Real Time ◽  
Short Term Memory ◽  
Location Based Services ◽  
Location Prediction ◽  
Trajectory Data ◽  
Short Term ◽  
Data Set ◽  
Term Memory ◽  
Long Time ◽  
Long Short Term Memory

The widely use of positioning technology has made mining the movements of people feasible and plenty of trajectory data have been accumulated. How to efficiently leverage these data for location prediction has become an increasingly popular research topic as it is fundamental to location-based services (LBS). The existing methods often focus either on long time (days or months) visit prediction (i.e., the recommendation of point of interest) or on real time location prediction (i.e., trajectory prediction). In this paper, we are interested in the location prediction problem in a weak real time condition and aim to predict users' movement in next minutes or hours. We propose a Spatial-Temporal Long-Short Term Memory (ST-LSTM) model which naturally combines spatial-temporal influence into LSTM to mitigate the problem of data sparsity. Further, we employ a hierarchical extension of the proposed ST-LSTM (HST-LSTM) in an encoder-decoder manner which models the contextual historic visit information in order to boost the prediction performance. The proposed HST-LSTM is evaluated on a real world trajectory data set and the experimental results demonstrate the effectiveness of the proposed model.

scholarly journals Location Prediction with Personalized Federated Learning

2021 ◽  
Author(s):  
shuang wang ◽  
Bowei Wang ◽  
Shuai Yao ◽  
Jiangqin Qu ◽  
Yuezheng Pan
Keyword(s):  
Human Mobility ◽  
Prediction Method ◽  
Human Movement ◽  
Movement Behavior ◽  
Location Prediction ◽  
Learning Framework ◽  
User Behaviors ◽  
Learning Techniques ◽  
Location Based Social Networks ◽  
Privacy Issues

Abstract Location prediction has attracted wide attention in human mobility prediction because of the popularity of location-based social networks. Existing location prediction methods have achieved remarkable development in centrally stored datasets. However, these datasets contain privacy data about user behaviors and may cause privacy issues. A location prediction method is proposed in our work to predict human movement behavior using federated learning techniques in which the data is stored in different clients and different clients cooperate to train to extract useful users’ behavior information and prevent the disclosure of privacy. Firstly, we put forward an innovative spatial-temporal location prediction framework(STLPF) for location prediction by integrating spatial-temporal information in local and global views on each client, and propose a new loss function to optimize the model. Secondly, we design a new personalized federated learning framework in which clients can cooperatively train their personalized models in the absence of a global model. Finally, the numerous experimental results on check-in datasets further show that our privacy-protected method is superior and more effective than various baseline approaches.

scholarly journals A Real-Time Trajectory Prediction Method of Small-Scale Quadrotors Based on GPS Data and Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7061
Author(s):  
Zhao Yang ◽  
Rong Tang ◽  
Jie Bao ◽  
Jiahuan Lu ◽  
Zhijie Zhang
Keyword(s):  
Real Time ◽  
Stable State ◽  
Prediction Method ◽  
Small Scale ◽  
Trajectory Data ◽  
Trajectory Prediction ◽  
Unit Model ◽  
Proposed Model ◽  
Historical Trajectory ◽  
Gated Recurrent Unit

This paper proposes a real-time trajectory prediction method for quadrotors based on a bidirectional gated recurrent unit model. Historical trajectory data of ten types of quadrotors were obtained. The bidirectional gated recurrent units were constructed and utilized to learn the historic data. The prediction results were compared with the traditional gated recurrent unit method to test its prediction performance. The efficiency of the proposed algorithm was investigated by comparing the training loss and training time. The results over the testing datasets showed that the proposed model produced better prediction results than the baseline models for all scenarios of the testing datasets. It was also found that the proposed model can converge to a stable state faster than the traditional gated recurrent unit model. Moreover, various types of training samples were applied and compared. With the same randomly selected test datasets, the performance of the prediction model can be improved by selecting the historical trajectory samples of the quadrotors close to the weight or volume of the target quadrotor for training. In addition, the performance of stable trajectory samples is significantly better than that with unstable trajectory segments with a frequent change of speed and direction with large angles.

Disaggregate Short-Term Location Prediction Based on Recurrent Neural Network and an Agent-Based Platform

2019 ◽  
Vol 2673 (8) ◽  
pp. 657-668
Author(s):  
Yanjie Dong ◽  
John Polak ◽  
Aruna Sivakumar ◽  
Fangce Guo
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Simulated Data ◽  
Location Prediction ◽  
Trajectory Data ◽  
Short Term ◽  
Agent Based ◽  
Individual Level ◽  
Data Limitations

With the growing popularity of mobile and sensory devices, there has been a strong research interest in short-term disaggregate-level location prediction. Such predictive models have huge application potential in several sectors to change and improve people’s daily life and experience. Existing methods in this research stream have mainly focused on the prediction of sequence of location, with valuable temporal information overlooked. In addition, data limitations have constrained the development and understanding from different algorithms. In this paper, the authors propose a recurrent neural network-based method (RNN and LSTM, long short-term memory) for the next and future location prediction. This model predicts the sequence in time, thus it can predict both when and where an individual will be in the future and the duration of the stay at each location. The predictive model is developed based on an agent-based simulation platform that can produce realistic spatial-temporal trajectory data at the individual level. Analysis of the simulated data has shown that RNN and LSTM are capable of predicting future locations with better results than other comparative methods, especially for agents with high location variability. Online prediction with true location information fed into the model later in the day would greatly improve the predicted results. However, significant variations can be observed at the zonal level, with all methods performing much better on frequently visited locations than less visited locations or irregular visits.

scholarly journals A Spatial-Temporal Self-Attention Network (STSAN) for Location Prediction

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Shuang Wang ◽  
AnLiang Li ◽  
Shuai Xie ◽  
WenZhu Li ◽  
BoWei Wang ◽  
...  
Keyword(s):  
New York ◽  
Geographic Location ◽  
Temporal Information ◽  
Location Prediction ◽  
Trajectory Data ◽  
Attention Network ◽  
Trajectory Representation ◽  
Activity Preference ◽  
Location Based Social Networks ◽  
Significant Attention

With the popularity of location-based social networks, location prediction has become an important task and has gained significant attention in recent years. However, how to use massive trajectory data and spatial-temporal context information effectively to mine the user’s mobility pattern and predict the users’ next location is still unresolved. In this paper, we propose a novel network named STSAN (spatial-temporal self-attention network), which can integrate spatial-temporal information with the self-attention for location prediction. In STSAN, we design a trajectory attention module to learn users’ dynamic trajectory representation, which includes three modules: location attention, which captures the location sequential transitions with self-attention; spatial attention, which captures user’s preference for geographic location; and temporal attention, which captures the user temporal activity preference. Finally, extensive experiments on four real-world check-ins datasets are designed to verify the effectiveness of our proposed method. Experimental results show that spatial-temporal information can effectively improve the performance of the model. Our method STSAN gains about 39.8% Acc@1 and 4.4% APR improvements against the strongest baseline on New York City dataset.

Sign in / Sign up

Export Citation Format

Share Document