A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

Potential destination discovery for low predictability individuals based on knowledge graph

Travelers may travel to locations they have never visited, which we call potential destinations of them. Especially under a very limited observation, travelers tend to show random movement patterns and usually have a large number of potential destinations, which make them difficult to handle for mobility prediction (e.g., destination prediction). In this paper, we develop a new knowledge graph-based framework (PDPFKG) for potential destination discovery of low predictability travelers by considering trip association relationships between them. We first construct a trip knowledge graph (TKG) to model the trip scenario by entities (e.g., travelers, destinations and time information) and their relationships, in which we introduce the concept of private relationship for complexity reduction. Then a modified knowledge graph embedding algorithm is implemented to optimize the overall graph representation. Based on the trip knowledge graph embedding model (TKGEM), the possible ranking of individuals' unobserved destinations to be chosen in the future can be obtained by calculating triples' distance. Empirically. PDPFKG is tested using an anonymous vehicular dataset from 138 intersections equipped with video-based vehicle detection systems in Xuancheng city, China. The results show that (i) the proposed method significantly outperforms baseline methods, and (ii) the results show strong consistency with traveler behavior in choosing potential destinations. Finally, we provide a comprehensive discussion of the innovative points of the methodology

The Built Environment and Health: Introducing Individual Space-Time Behavior

Many studies have examined the relationship between the built environment and health. Yet, the question of how and why the environment influences health behavior remains largely unexplored. As health promotion interventions work through the individuals in a targeted population, an explicit understanding of individual behavior is required to formulate and evaluate intervention strategies. Bringing in concepts from various fields, this paper proposes the use of an activity-based modeling approach for understanding and predicting, from the bottom up, how individuals interact with their environment and each other in space and time, and how their behaviors aggregate to population-level health outcomes

Recreation, tourism and nature in a changing world : proceedings of the fifth international conference on monitoring and management of visitor flows in recreational and protected areas : Wageningen, the Netherlands, May 30-June 3, 2010

Proceedings of the fifth international conference on monitoring and management of visitor flows in recreational and protected areas : Wageningen, the Netherlands, May 30-June 3, 201

Selective Trajectory Memory Network andits application in Vehicle DestinationPrediction

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

Business intelligence and big data in hospitality and tourism: a systematic literature review

Purpose This paper aims to examine the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying research gaps and future developments and designing an agenda for future research. Design/methodology/approach The study consists of a systematic quantitative literature review of academic articles indexed on the Scopus and Web of Science databases. The articles were reviewed based on the following features: research topic; conceptual and theoretical characterization; sources of data; type of data and size; data collection methods; data analysis techniques; and data reporting and visualization. Findings Findings indicate an increase in hospitality and tourism management literature applying analytical techniques to large quantities of data. However, this research field is fairly fragmented in scope and limited in methodologies and displays several gaps. A conceptual framework that helps to identify critical business problems and links the domains of business intelligence and big data to tourism and hospitality management and development is missing. Moreover, epistemological dilemmas and consequences for theory development of big data-driven knowledge are still a terra incognita. Last, despite calls for more integration of management and data science, cross-disciplinary collaborations with computer and data scientists are rather episodic and related to specific types of work and research. Research limitations/implications This work is based on academic articles published before 2017; hence, scientific outputs published after the moment of writing have not been included. A rich research agenda is designed. Originality/value This study contributes to explore in depth and systematically to what extent hospitality and tourism scholars are aware of and working intendedly on business intelligence and big data. To the best of the authors’ knowledge, it is the first systematic literature review within hospitality and tourism research dealing with business intelligence and big data

Key challenges in agent-based modelling for geo-spatial simulation

Agent-based modelling (ABM) is fast becoming the dominant paradigm in social simulation due primarily to a worldview that suggests that complex systems emerge from the bottom-up, are highly decentralised, and are composed of a multitude of heterogeneous objects called agents. These agents act with some purpose and their interaction, usually through time and space, generates emergent order, often at higher levels than those at which such agents operate. ABM however raises as many challenges as it seeks to resolve. It is the purpose of this paper to catalogue these challenges and to illustrate them using three somewhat different agent-based models applied to city systems. The seven challenges we pose involve: the purpose for which the model is built, the extent to which the model is rooted in independent theory, the extent to which the model can be replicated, the ways the model might be verified, calibrated and validated, the way model dynamics are represented in terms of agent interactions, the extent to which the model is operational, and the way the model can be communicated and shared with others. Once catalogued, we then illustrate these challenges with a pedestrian model for emergency evacuation in central London, a hypothetical model of residential segregation tuned to London data which elaborates the standard Schelling (1971) model, and an agent-based residential location built according to spatial interactions principles, calibrated to trip data for Greater London. The ambiguities posed by this new style of modelling are drawn out as conclusions

Trip Planner: A Big Data Analytics Based Recommendation System for Tourism Planning

Foreign tourism has gained immense popularity in the recent past. To make a rational decision about the destination to be visited one has to go through variety of social media sources with very large number of reviews, which is a tedious task. Automated analysis of these reviews is quite complex as it involves non structured text data having slang terms also. Moreover, these reviews are pouring in continuously. To overcome this problem, this paper provides a Big Data analytics-based framework to make appropriate selection of the destination on the basis of automated analysis of social media contents based upon the adaptation and augmentation of various tools and technologies. The framework has been implemented using Apache Spark and Bidirectional Encoder Representation Transformers (BERT) deep learning models through which raw text review are analysed and a final score based on five metrics is obtained to recommend destination for visit