scholarly journals Temporal-aware Location Prediction Model Using Similarity Approach

2013 ◽  
Author(s):  
Ghulam Sarwar ◽  
Farman Ullah ◽  
Sungchang Lee
Keyword(s):  
Prediction Model ◽  
Location Prediction

scholarly journals Predicting Future Locations of Moving Objects by Recurrent Mixture Density Network

2020 ◽  
Vol 9 (2) ◽  
pp. 116
Author(s):  
Rui Chen ◽  
Mingjian Chen ◽  
Wanli Li ◽  
Naikun Guo
Keyword(s):  
Prediction Model ◽  
Intelligent Transportation Systems ◽  
Moving Objects ◽  
Short Term Memory ◽  
Transportation Systems ◽  
Performance Comparison ◽  
Parameter Analysis ◽  
Location Prediction ◽  
Time Step ◽  
Mixture Density

Accurate and timely location prediction of moving objects is crucial for intelligent transportation systems and traffic management. In recent years, ubiquitous location acquisition technologies have provided the opportunity for mining knowledge from trajectories, making location prediction and real-time decisions more feasible. Previous location prediction methods have mostly developed on the basis of shallow models whereas shallow models are not competent for some tricky challenges such as multi-time-step location coordinates prediction. Motivated by the current study status, we are dedicated to a deep-learning-based approach to predict the coordinates of several future locations of moving objects based on recent trajectory records. The method of this work consists of three successive parts: trajectory preprocessing, prediction model construction, and post-processing. In this work, a prediction model named the bidirectional recurrent mixture density network (BiRMDN) was constructed by integrating the long short-term memory (LSTM) and mixture density network (MDN) together. This model has the ability to learn long-term contextual information from recent trajectory and model real-valued location coordinates. We employed a vessel trajectory dataset for the implementation of this approach and determined the optimal model configuration after several parameter analysis experiments. Experimental results involving a performance comparison with other widely used methods demonstrate the superiority of the BiRMDN model.

scholarly journals Neural Network Based Vehicular Location Prediction Model for Cooperative Active Safety Systems

2018 ◽  
Vol 30 (2) ◽  
pp. 205-215
Author(s):  
Murat Dörterler ◽  
Ömer Faruk Bay
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Geographical Location ◽  
Computation Time ◽  
Active Safety ◽  
Location Prediction ◽  
Moving Vehicle ◽  
Proposed Model ◽  
Active Safety Systems ◽  
Safety Systems

Safety systems detect unsafe conditions and provide warnings for travellers to take action and avoid crashes. Estimation of the geographical location of a moving vehicle as to where it will be positioned next with high precision and short computation time is crucial for identifying dangers. To this end, navigational and dynamic data of a vehicle are processed in connection with the data received from neighbouring vehicles and infrastructure in the same vicinity. In this study, a vehicular location prediction model was developed using an artificial neural network for cooperative active safety systems. The model is intended to have a constant, shorter computation time as well as higher accuracy features. The performance of the proposed model was measured with a real-time testbed developed in this study. The results are compared with the performance of similar studies and the proposed model is shown to deliver a better performance than other models.

scholarly journals A Markov Chain Position Prediction Model Based on Multidimensional Correction

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Sijia Chen ◽  
Jian Zhang ◽  
Fanwei Meng ◽  
Dini Wang
Keyword(s):  
Markov Chain ◽  
Prediction Model ◽  
Location Prediction ◽  
Low Carbon ◽  
Model Based ◽  
Order Markov Chain ◽  
Position Sequence ◽  
Position Prediction ◽  
Location Based Social Networks ◽  
Green System

User location prediction in location-based social networks can predict the density of people flow well in terms of intelligent transportation, which can make corresponding adjustments in time to make traffic smooth, reduce fuel consumption, reduce greenhouse gas emissions, and help build a green cycle low-carbon transportation green system. This paper proposes a Markov chain position prediction model based on multidimensional correction (MDC-MCM). Firstly, extract corresponding information from the user’s historical check-in position sequence as a position-position conversion map. Secondly, the influence of check-in period, space distance, and other factors on the position prediction is linearly weighted and merged with the position prediction of the n-order Markov chain to construct MDC-MCM. Finally, we conduct a comprehensive performance evaluation of MDC-MCM using the dataset collected from Brightkite. Experimental results show that compared with other advanced location prediction technologies, MDC-MCM achieves better location prediction results.

scholarly journals Location Prediction Model Based on the Internet of Vehicles for Assistance to Medical Vehicles

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 10754-10767
Author(s):  
Jiujun Cheng ◽  
Huaichen Yan ◽  
Aiguo Zhou ◽  
Chunmei Liu ◽  
Ding Cheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
The Internet ◽  
Location Prediction ◽  
Internet Of Vehicles ◽  
Model Based

Wear mechanism and notch wear location prediction model in ball nose end milling of Inconel 718

Wear ◽  
2013 ◽  
Vol 302 (1-2) ◽  
pp. 1171-1179 ◽  
Author(s):  
M.S. Kasim ◽  
C.H. Che Haron ◽  
J.A. Ghani ◽  
M.A. Sulaiman ◽  
M.Z.A. Yazid
Keyword(s):  
Prediction Model ◽  
Wear Mechanism ◽  
Inconel 718 ◽  
End Milling ◽  
Location Prediction ◽  
Notch Wear

scholarly journals Location Extraction and Prediction Method Based on Floating Car Spatial-Temporal Trajectory

2020 ◽  
Vol 9 (5) ◽  
pp. 302
Author(s):  
Shaoming Pan ◽  
Ziying Li ◽  
Yanwen Chong
Keyword(s):  
Prediction Model ◽  
Dynamic Environment ◽  
Prediction Method ◽  
Dynamic Parameter ◽  
Location Prediction ◽  
Estimation Of Parameters ◽  
Extraction Algorithm ◽  
Dynamic Parameter Estimation ◽  
And Behavior ◽  
Temporal Trajectory

Predicting the next important location by mining the user’s historical spatial-temporal trajectory can be done for behavioral analysis and path planning. Since extracting the important location precisely is the premise of next location prediction, an enhanced location extraction algorithm is proposed to meet the requirements of dynamic trajectory via dynamic parameter estimation. To realize the estimation of parameters dynamically, the differences of floating car velocity in terms of spatial distribution and behavior in time distribution are considered in the location extraction algorithm. Then, an improved recurrent neural network (RNN) model is designed to mine the variation law of floating car trajectories to improve the accuracy of important location prediction under different conditions. Different from the traditional prediction model considering only the constraint of distance, the attention mechanism and semantic information are considered simultaneously by the proposed prediction model. Finally, the floating car trajectory of Beijing is selected for our experiments, and the results show that the proposed location extraction algorithm can meet the requirements of a dynamic environment and our model achieves high prediction accuracy.

An adaptive location prediction model based on fuzzy control

2011 ◽  
Vol 34 (7) ◽  
pp. 816-834 ◽  
Author(s):  
Theodoros Anagnostopoulos ◽  
Christos Anagnostopoulos ◽  
Stathes Hadjiefthymiades
Keyword(s):  
Fuzzy Control ◽  
Prediction Model ◽  
Location Prediction ◽  
Model Based
Sign in / Sign up

Export Citation Format

Share Document