Location-based Data Analysis of Visitor Structure for Recreational Area Management

This work presents a location-based data analysis framework for profiling visitors structures. In terms of recreational area management, understanding visitors’ structure and popularity is important. Traditionally, visitors monitoring with automatic counting devices has drawbacks of inaccurate visitors counting. In this work, compared to automatic counting devices, we use Wi-Fi tracking as the main method to count visitors, which provides a fairly precise picture of visitor structures. Moreover, we deliver rich analytic functions in this framework and we present the functionality with visitor data collected from Guanyinshan Visitor Center. This framework not only standardizes visitor counting process but also facilitates a profound analysis of visitor structures. Key Words: Guanyinshan Visitor Center, Wi-Fi trackin

Analysing Crowd Behaviours using Mobile Sensing

PhDResearchers have examined crowd behaviour in the past by employing a variety of methods including ethnographic studies, computer vision techniques and manual annotation-based data analysis. However, because of the resources to collect, process and analyse data, it remains difficult to obtain large data sets for study. Mobile phones offer easier means for data collection that is easy to analyse and can preserve the user’s privacy. The aim of this thesis is to identify and model different qualities of social interactions inside crowds using mobile sensing technology. This Ph.D. research makes three main contributions centred around the mobile sensing and crowd sensing area. Firstly, an open-source licensed mobile sensing framework is developed, named SensingKit, that is capable of collecting mobile sensor data from iOS and Android devices, supporting most sensors available in modern smartphones. The framework has been evaluated in a case study that investigates the pedestrian gait synchronisation phenomenon. Secondly, a novel algorithm based on graph theory is proposed capable of detecting stationary social interactions within crowds. It uses sensor data available in a modern smartphone device, such as the Bluetooth Smart (BLE) sensor, as an indication of user proximity, and accelerometer sensor, as an indication of each user’s motion state. Finally, a machine learning model is introduced that uses multi-modal mobile sensor data extracted from Bluetooth Smart, accelerometer and gyroscope sensors. The validation was performed using a relatively large dataset with 24 participants, where they were asked to socialise with each other for 45 minutes. By using supervised machine learning based on gradient-boosted trees, a performance increase of 26.7% was achieved over a proximity-based approach. Such model can be beneficial to the design and implementation of in-the-wild crowd behavioural analysis, design of influence strategies, and algorithms for crowd reconfiguration.UK Defence Science & Technology Laboratory (DSTL

Entity Recognition via Multimodal Sensor Fusion with Smart Phones

This thesis serves as an exploration that takes the sensors within a cell phone beyond the current state of recognition activities. Current state of the art sensor recognition processes tend to focus on recognizing user activity. Utilizing the same sensors available for user activity classification, this thesis validates the ability to gather data about entities separate from the user carrying the smart phone. With the ability to sense entities, the ability to recognize and classify a multitude of items, situations, and phenomena opens a new realm of possibilities for how devices perceive and react to their environment

Emploi d'un réseau de détecteurs Wi-Fi pour l'étude et la modélisation du comportement de piétons

RÉSUMÉ La densification des villes est la cause d’un besoin grandissant de comprendre les déplacements des gens qui y habitent. La prédiction et la gestion de la circulation automobile sont des domaines bien développés, plus que ces mêmes domaines au sujet des piétons. De plus en plus, les villes réorientent leurs objectifs de développement vers les piétons, ou les cyclistes au détriment des automobilistes. Cela mène à la création de zones réservées aux piétons, qui deviennent de plus en plus des centres commerciaux et culturels importants au développement de la ville. Ce texte porte sur l’analyse d’une nouvelle technologie qui permet de faire la collecte de données de telles zones, et des résultats qui serviront à l’amélioration de zones piétonnes existantes et à la conception de nouvelles.----------ABSTRACT Urban densification is the cause of a growing need to understand the travel behaviours of people living in cities. Prediction and management of automobile traffic are well-developed fields, moreso than those same fields about pedestrians. More and more, cities are reorienting their develpment objectives to gear them towards pedestrians or cyclists at the expense of cars. This has lead to the creation of numerous pedestrian-only zones, that have become important commercial and cultural centers of city development. This document relates the analysis of a new technology that allows data collection of these areas, and the results will benefit the development of existing and new pedestrian-only areas