This work applies a knowledge modelling approach in the design of a framework for the generation of travel demand for traffic simulation applications. The proposed framework is based on interchangeable modules that integrate the main stages of travel demand modelling supported by an engineered knowledge base. This approach is intended to promote greater behavioural diversity, incorporate more diverse contextual data, facilitate access to online datasets, and support users to undertake and validate investigations across a range of models and implementations. The framework provides the user with direct control to modify the schema, control the selection of data, select alternative modules to execute, and the potential to remotely retrieve data and execute modules. The framework is investigated through a prototype, which generated travel demand across a full day and performed simulation utilising two third-party traffic simulators based on the configuration by the user schema. The problem of travel demand generation was separated into discrete task modules with identification of features for alternative design or further modularisation. The prototype evaluation generated multimode travel demand that was successfully tested on third-party traffic simulators and evidenced the fitness of semantic technologies in building simulator-agnostic interchangeable framework modules that satisfy the need for configurable travel modelling. The findings of the paper also contribute to the understanding of the challenges in utilising Semantic Web technologies for implementing travel demand generation. It is proposed that the framework provides a basis to develop new and existing approaches to travel demand generation to improve modelling outcomes and adoption.

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Mobile devices are ubiquitous for users in everyday life, often with individuals throughout their daily routine. The widespread use of mobile devices allows novel technologies to be designed to improve personal security and safety. The research presented in this paper provides additional functionality through pervasive sensors on mobile devices, making use of acoustic recordings and transcriptions to enable violent language detection in near-real time through a user-centred application. The purpose of the application is to provide a tool to improve the safety of individuals who may experience violent conversations. We have developed a mobile application which actively demonstrates applied theoretical practises across both the Tiny Machine Learning and Human-Computer Interaction literature. The application uses the built-in phone speaker and the device’s on-board transcription service, providing an improved capability to correctly detect violent language in conversations through user proximity.

COVID-19 has shown a relatively low case fatality rate in young healthy individuals, with the majority of this group being asymptomatic or having mild symptoms. However, the severity of the disease among the elderly as well as in individuals with underlying health conditions has caused significant mortality rates worldwide. Understanding this variance amongst different sectors of society and modelling this will enable the different levels of risk to be determined to enable strategies to be applied to different groups. Long established compartmental epidemiological models like SIR and SEIR do not account for the variability encountered in the severity of the SARS-CoV-2 disease across different population groups. To overcome this limitation, it is proposed that a modified model, namely SEIR-v, through which the population is separated into two groups regarding their vulnerability to SARS-CoV2 is applied. This enables the analysis of the spread of the epidemic when different contention measures are applied to different groups in society regarding their vulnerability to the disease. A Monte Carlo simulation adopting the proposed SEIR-v model indicates a large number of deaths could be avoided by slightly decreasing the exposure of vulnerable groups to the disease. From this modelling a number of mechanisms are proposed to limit the exposure of vulnerable individuals to the disease in order to reduce the mortality rate among this group. One option could be the provision of a wristband to vulnerable people and those without a smartphone and contact-tracing app, filling the gap created by systems relying on smartphone apps only.

Pervasive sensing has opened up new opportunities for measuring our feelings and understanding our behavior by monitoring our affective states while mobile. This review paper surveys pervasive affect sensing by examining and considering three major elements of affective pervasive systems, namely “sensing,” “analysis,” and “application.” Sensing investigates the different sensing modalities that are used in existing real-time affective applications, analysis explores different approaches to emotion recognition and visualization based on different types of collected data, and application investigates different leading areas of affective applications. For each of the three aspects, the paper includes an extensive survey of the literature and finally outlines some of challenges and future research opportunities of affective sensing in the context of pervasive computing.

Urban spaces have a great impact on how people feel and behave. There are number of factors that impact our emotional responses to a space. In this paper, we propose an objective way to measure people's emotional reactions in places by monitoring their physiological signals that are related to emotion. By integrating wearable biosensors with mobile phones, we can obtain geo-annotated data relating to emotional states in relation to our spatial surroundings. We are the able to visualize the emotional response data by creating an emotional layer over a geographical map. This can then help us to understand how individuals emotionally perceive urban spaces and help us to illustrate the interdependency between emotions and environmental surroundings.

In this paper we present the design, implementation, evaluation, and user experiences of the NoiseSpy application, our sound sensing system that turns the mobile phone into a low-cost data logger for monitoring environmental noise. It allows users to explore a city area while collaboratively visualizing noise levels in real-time. The software combines the sound levels with GPS data in order to generate a map of sound levels that were encountered during a journey. We report early findings from the trials which have been carried out by cycling couriers who were given Nokia mobile phones equipped with the NoiseSpy software to collect noise data around Cambridge city. Indications are that, not only is the functionality of this personal environmental sensing tool engaging for users, but aspects such as personalization of data, contextual information, and reflection upon both the data and its collection, are important factors in obtaining and retaining their interest.

Learn about projects on participant-environment interaction, the leveraging of information from mobile sensors, user authentication, and urban computing navigation.