Analysis of human mobility patterns from GPS trajectories and contextual information

This work was supported by the EU FP7 Marie Curie ITN GEOCROWD grant (FP7- PEOPLE-2010-ITN-264994).Human mobility is important for understanding the evolution of size and structure of urban areas, the spatial distribution of facilities, and the provision of transportation services. Until recently, exploring human mobility in detail was challenging because data collection methods consisted of cumbersome manual travel surveys, space-time diaries or interviews. The development of location-aware sensors has significantly altered the possibilities for acquiring detailed data on human movements. While this has spurred many methodological developments in identifying human movement patterns, many of these methods operate solely from the analytical perspective and ignore the environmental context within which the movement takes place. In this paper we attempt to widen this view and present an integrated approach to the analysis of human mobility using a combination of volunteered GPS trajectories and contextual spatial information. We propose a new framework for the identification of dynamic (travel modes) and static (significant places) behaviour using trajectory segmentation, data mining and spatio-temporal analysis. We are interested in examining if and how travel modes depend on the residential location, age or gender of the tracked individuals. Further, we explore theorised “third places”, which are spaces beyond main locations (home/work) where individuals spend time to socialise. Can these places be identified from GPS traces? We evaluate our framework using a collection of trajectories from 205 volunteers linked to contextual spatial information on the types of places visited and the transport routes they use. The result of this study is a contextually enriched data set that supports new possibilities for modelling human movement behaviour.PostprintPeer reviewe

Robust and Hierarchical Stop Discovery in Sparse and Diverse Trajectories

The advance of GPS tracking technique brings a large amount of trajectory data. To better understand such mobility data, semantic models like “stop/move” (or inferring “activity”, “transportation mode”) recently become a hot topic for trajectory data analysis. Stops are important parts of tra- jectories, such as “working at office”, “shopping in a mall”, “waiting for the bus”. There are several methods such as velocity, clustering, density algorithms being designed to discover stops. However, existing works focus on well-defined trajectories like movement of vehicle and taxi, not working well for heterogeneous cases like diverse and sparse trajectories. On the contrary, our paper addresses three main challenges: (1) provide a robust clustering-based method to discover stops; (2) discover both shared stops and personalized stops, where shared stops are the common places where many trajectories pass and stay for a while (e.g. shopping mall), whilst personalized stops are individual places where user stays for his/her own purpose (e.g. home, office); (3) further build stop hierarchy (e.g. a big stop like EPFL campus and a small stop like an office building). We evaluate our approach with several diverse and spare real-life GPS data, compare it with other methods, and show its better data abstraction on trajectory

Frequency Modulated Continuous Waveform Radar for Collision Prevention in Large Vehicles

The drivers of large vehicles can have very limited visibility, which contributes to poor situation awareness and an increased risk of collision with other agents. This thesis is focused on the development of reliable sensing for this close proximity problem in large vehicles operating in harsh environmental conditions. It emphasises the use of in-depth knowledge of a sensor’s physics and performance characteristics to develop effective mathematical models for use in different mapping algorithms. An analysis of the close proximity problem and the demands it poses on sensing technologies is presented. This guides the design and modelling process for a frequency modulated continuous waveform (FMCW) radar sensor for use in solving the close proximity problem. Radar offers better all-weather performance than other sensing modalities, but its measurement structure is more complex and often degraded by noise and clutter. The commonly used constant false alarm rate (CFAR) threshold approach performs poorly in applications with frequent extended targets and a short measurement vector, as is the case here. Therefore, a static detection threshold is calculated using measurements of clutter made using the radar, allowing clutter measurements to be filtered out in known environments. The detection threshold is used to develop a heuristic sensor model for occupancy grid mapping. This results in a more reliable representation of the environment than is achieved using the detection threshold alone. A Gaussian mixture extended Kalman probability hypothesis density filter (GM-EK-PHD) is implemented to allow mapping in dynamic environments using the FMCW radar. These methods are used to produce maps of the environment that can be displayed to the driver of a large vehicle to better avoid collisions. The concepts developed in this thesis are validated using simulated and real data from a low-cost 24GHz FMCW radar developed at the Australian Centre for Field Robotics at the University of Sydney

Cooperative Vehicle Tracking in Large Environments

Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can be easily achieved by providing vehicles with a constant communication link to a control centre and having the vehicles broadcast their position. The problem changes dramatically when vehicles operate within a large environment of potentially hundreds of square kilometres and in difficult terrain. This thesis presents algorithms for cooperative tracking of vehicles based on a vehicle motion model that incorporates the properties of the working area, and information collected by infrastructure collection points and other mobile agents. The probabilistic motion prediction approach provides long-term estimates of vehicle positions using motion profiles built for the particular environment and considering the vehicle stopping probability. A limited number of data collection points distributed around the field are used to update the position estimates, with negative information also used to improve the estimation. The thesis introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates and inter-vehicle measurements to be relayed among vehicles and finally conveyed to the collection points for an improved position estimate. It uses a store-and-synchronise concept to deal with intermittent communication and aims to disseminate data in an opportunistic manner. A nonparametric filtering algorithm for cooperative tracking is proposed to incorporate the information harvested, including the negative, relative, and time delayed observations. An important contribution of this thesis is to enable the optimisation of fleet scheduling when full coverage networks are not available or feasible. The proposed approaches were validated with comprehensive experimental results using data collected from a large-scale mining operation

Cooperative Vehicle Tracking in Large Environments

Vehicle position tracking and prediction over large areas is of significant importance in many industrial applications, such as mining operations. In a small area, this can be easily achieved by providing vehicles with a constant communication link to a control centre and having the vehicles broadcast their position. The problem changes dramatically when vehicles operate within a large environment of potentially hundreds of square kilometres and in difficult terrain. This thesis presents algorithms for cooperative tracking of vehicles based on a vehicle motion model that incorporates the properties of the working area, and information collected by infrastructure collection points and other mobile agents. The probabilistic motion prediction approach provides long-term estimates of vehicle positions using motion profiles built for the particular environment and considering the vehicle stopping probability. A limited number of data collection points distributed around the field are used to update the position estimates, with negative information also used to improve the estimation. The thesis introduces the concept of observation harvesting, a process in which peer-to-peer communication between vehicles allows egocentric position updates and inter-vehicle measurements to be relayed among vehicles and finally conveyed to the collection points for an improved position estimate. It uses a store-and-synchronise concept to deal with intermittent communication and aims to disseminate data in an opportunistic manner. A nonparametric filtering algorithm for cooperative tracking is proposed to incorporate the information harvested, including the negative, relative, and time delayed observations. An important contribution of this thesis is to enable the optimisation of fleet scheduling when full coverage networks are not available or feasible. The proposed approaches were validated with comprehensive experimental results using data collected from a large-scale mining operation

Mobile psychiatry: Personalised Ambient Monitoring for the mentally ill

Mental health has long been a neglected problem in global healthcare. The social and economic impacts of conditions affecting the mind are still underestimated. However, in recent years it is becoming more apparent that mental disorders are a growing global concern that is not to be trivialised. Considering the rising burden of psychiatric illnesses, there is a necessity of developing novel services and researching effective means of providing interventions to sufferers. Such novel services could include technology-based solutions already used in other healthcare applications but are yet to make their way into standard psychiatric practice. This thesis presents a study on how pervasive technology can be utilised to devise an “early warning” system for patients with bipolar disorder. The system, containing wearable and environmental sensors, would collect behavioural data and use it to inform the user about subtle changes that might indicate an upcoming episode. To test the feasibility of the concept a prototype system was devised, which was followed by trials including four healthy volunteers as well as a bipolar patient. The system included a number of sensory inputs including: accelerometer, light sensors, microphones, GPS tracking and motion detectors. The experiences from the trials led to a conclusion that a large number of sensors may result in incompliance from the users. Therefore, a separate investigation was launched into developing a methodology for detecting behavioural patterns in inputs possible to collect from a mobile phone alone. The premise being that a phone is an everyday use appliance and is likely to be carried and accepted by the patient. The trial revealed that monitoring GPS tracks and Bluetooth encounters has the potential of gaining an insight into a person’s social and behavioural patterns, which usually are strongly influenced by the course of bipolar disorder. Lessons learned during these proceedings amounted to a clearer concept of how a future personalised ambient monitoring system could improve the outcome of treatment of bipolar disorder as well as other psychiatric conditions

Mobile psychiatry: Personalised Ambient Monitoring for the mentally ill

Mental health has long been a neglected problem in global healthcare. The social and economic impacts of conditions affecting the mind are still underestimated. However, in recent years it is becoming more apparent that mental disorders are a growing global concern that is not to be trivialised. Considering the rising burden of psychiatric illnesses, there is a necessity of developing novel services and researching effective means of providing interventions to sufferers. Such novel services could include technology-based solutions already used in other healthcare applications but are yet to make their way into standard psychiatric practice. This thesis presents a study on how pervasive technology can be utilised to devise an “early warning” system for patients with bipolar disorder. The system, containing wearable and environmental sensors, would collect behavioural data and use it to inform the user about subtle changes that might indicate an upcoming episode. To test the feasibility of the concept a prototype system was devised, which was followed by trials including four healthy volunteers as well as a bipolar patient. The system included a number of sensory inputs including: accelerometer, light sensors, microphones, GPS tracking and motion detectors. The experiences from the trials led to a conclusion that a large number of sensors may result in incompliance from the users. Therefore, a separate investigation was launched into developing a methodology for detecting behavioural patterns in inputs possible to collect from a mobile phone alone. The premise being that a phone is an everyday use appliance and is likely to be carried and accepted by the patient. The trial revealed that monitoring GPS tracks and Bluetooth encounters has the potential of gaining an insight into a person’s social and behavioural patterns, which usually are strongly influenced by the course of bipolar disorder. Lessons learned during these proceedings amounted to a clearer concept of how a future personalised ambient monitoring system could improve the outcome of treatment of bipolar disorder as well as other psychiatric conditions