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Selective Trajectory Memory Network andits application in Vehicle DestinationPrediction
Authors
레제
Publication date
1 February 2019
Publisher
서울대학교 대학원
Abstract
학위논문 (석사)-- 서울대학교 대학원 : 공과대학 산업공학과, 2019. 2. Cho, Sungzoon.Predicting efficiently the final destinations of moving vehicles can be of significant usefulness for several applications. Many probabilistic methods have been developed to address it but often include heavy feature engineering and do not generalize well to new datasets. To face these limitations, Deep-Learning models present the advantage of automating processing steps and can therefore be easily adapted to new input data. De Brébisson et al. proposed clustering based deep-learning approaches to solve it in the specific case of the prediction of Taxis destinations with remarkable performances, alongside with a proposition of a novel architecture inspired by Memory-Networks used in Natural Language Processing, and requiring no preliminary clustering. A large room for improvement was however left for the latter approach : the necessity of a relevant selection function retrieving historical trajectories similar to partial trips to predict was indeed outlined by the authors. In this work we propose to use the Segment-Path distance, introduced by Besse et al. in former works on trajectory clustering, to come up with an improved architecture of this memory model. A review of several Memory Networks architecture and their applications in time-series prediction is provided to give an overview of the different structural alternatives existing for the design of our model architecture. Finally, our model is confronted to individual car data and we propose a personalized user-by-user prediction of destinations. We discuss the suitability and limits of the type of model in this specific problem and conclude that the promising obtained results are penalized by infrequent destinations cases inducing noise whose effect could be reduced by turning our approach into a classification problem.Abstract i Contents List of Tables vi List of Figures viii Chapter 1 Introduction 1 1.1 Motivations, background . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem Description : destination forecasting problem . . . . . . . . 2 1.2.1 General context . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2.2 Specific problem tackled . . . . . . . . . . . . . . . . . . . . . 2 1.3 Existing models and methods . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Research Motivation and Contributions . . . . . . . . . . . . . . . . 6 1.5 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . 7 Chapter 2 Related works 8 2.1 Artificial neural network models for trajectory prediction . . . . . . 8 2.1.1 Encoding and clustering approach . . . . . . . . . . . . . . . 8 2.1.2 "Memory network" model for taxi trajectory prediction . . . 11 2.2 Memory networks and applications . . . . . . . . . . . . . . . . . . . 13 2.2.1 MemNN models . . . . . . . . . . . . . . . . . . . . . . . . . 14 2.2.2 End-to-end memory networks (MemN2N) . . . . . . . . . . . 16 2.2.3 Memory networks for multi-dimensional time-series forecasting (MTNnet) . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3 Analogies and comparisons between the memory models introduced . 19 2.4 Distances measures for vehicle trajectories . . . . . . . . . . . . . . . 22 2.4.1 Segment-Path Distance (SPD) . . . . . . . . . . . . . . . . . 23 2.5 Personalized predictions on car manufacturer data . . . . . . . . . . 26 2.5.1 Problem approach and redefinition . . . . . . . . . . . . . . . 26 2.5.2 Method and model . . . . . . . . . . . . . . . . . . . . . . . . 27 Chapter 3 Proposed Model 28 3.1 Overall architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.2 Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3 Memory storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4 Trajectory encoding . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4.1 Encoding architecture . . . . . . . . . . . . . . . . . . . . . . 30 3.4.2 Metadata and embedding . . . . . . . . . . . . . . . . . . . . 31 3.4.3 Distinctions between encoders, weight-sharing . . . . . . . . . 31 3.5 Memory selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.5.1 Attention mechanism . . . . . . . . . . . . . . . . . . . . . . 32 3.5.2 Data used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.6 Query-memory association . . . . . . . . . . . . . . . . . . . . . . . . 33 3.7 Final prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Chapter 4 Experiments 35 4.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2.1 Variability and predictability . . . . . . . . . . . . . . . . . . 36 4.2.2 Considered vehicles . . . . . . . . . . . . . . . . . . . . . . . . 37 4.3 Experimental settings . . . . . . . . . . . . . . . . . . . . . . . . . . 39 4.3.1 Training and testing set . . . . . . . . . . . . . . . . . . . . . 39 4.3.2 Test methodology and parameters . . . . . . . . . . . . . . . 40 4.3.3 Baseline model : simple encoding . . . . . . . . . . . . . . . . 42 4.4 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.4.1 General results . . . . . . . . . . . . . . . . . . . . . . . . . . 42 4.4.2 Factors of influence on models performances . . . . . . . . . . 45 4.4.3 Case studies : 5 example vehicles analysis . . . . . . . . . . . 49 4.4.4 Baseline model . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.5 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Chapter 5 Conclusion 56 5.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 5.2 Future Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Bibliography 58 감사의 글 62Maste
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