A descriptive study on public transport user behaviour from Live Bus Arrivals

In order to offer public transport that meet citizens’ needs for transport and further increase the use of bus services, Public Authorities need to analyse and understand travellers behaviour. Automatic Vehicle Location (AVL) data provide information on the observed time of arrival and departure of a bus at each stop. These data are fed into an algorithm to provide information to users on the expected time of arrival at the bus stop by an on-line service. In the city of London this service is called Live Bus Arrivals. This work describes the general behaviour of Live Bus Arrivals users by analysing the type of requests, localising them and compare them in different days of the week and time ranges. The objective is to identify some of the main passengers’ origin, destination and interchanges behaviour that could be of value to decision-makers and planners

Classification algorithms for Intelligent Transport Systems

Intelligent Transport Systems (ITS) consists in the application of ICT to transport to offer new and improved services to the mobility of people and freights. While using ITS, travellers produce large quantities of data that can be collected and analysed to study their behaviour and to provide information to decision makers and planners. The thesis proposes innovative deployments of classification algorithms for Intelligent Transport System with the aim to support the decisions on traffic rerouting, bus transport demand and behaviour of two wheelers vehicles. The first part of this work provides an overview and a classification of a selection of clustering algorithms that can be implemented for the analysis of ITS data. The first contribution of this thesis is an innovative use of the agglomerative hierarchical clustering algorithm to classify similar travels in terms of their origin and destination, together with the proposal for a methodology to analyse drivers’ route choice behaviour using GPS coordinates and optimal alternatives. The clusters of repetitive travels made by a sample of drivers are then analysed to compare observed route choices to the modelled alternatives. The results of the analysis show that drivers select routes that are more reliable but that are more expensive in terms of travel time. Successively, different types of users of a service that provides information on the real time arrivals of bus at stop are classified using Support Vector Machines. The results shows that the results of the classification of different types of bus transport users can be used to update or complement the census on bus transport flows. Finally, the problem of the classification of accidents made by two wheelers vehicles is presented together with possible future application of clustering methodologies aimed at identifying and classifying the different types of accidents

Developing a meta-model for early-stage overheating risk assessment for new apartments in London

The study presents a proposed approach towards developing the core engine for a simplified Rapid Overheating ASSessment Tool (ROASST), which is intended to help assist early-stage analysis of the risks of indoor overheating for apartments located in Greater London. Using a discrete number of plan forms selected from case studies, a virtual risk database was populated with the results of a large number of parametric dynamic thermal simulations based on the EnergyPlus calculation engine and including aspects such as location within Greater London, orientation, fenestration size and natural ventilation, which are associated with known overheating risk factors. Alternative statistical meta-models were developed with both explanatory and predictive purposes, correlating the simulation input with the overheating risk predictions expressed according to multiple metrics. Results from multiple linear regression analysis show that while all factors considered are relevant towards determining the propensity to overheating, window opening and natural ventilation capacity are by far the strongest predictors among those considered. The implementation of machine learning algorithms is shown to improve the accuracy of the meta-model, producing very high coefficients of determination (R2) and lower prediction errors (RMSE). The development of a meta-model demonstrates the ability of returning accurate predictions with limited input, albeit with significant limitations. Possibilities of further improvements to the tool are briefly outlined, including the coupling with a User Interface for applicability in a design environment for early-stage design advice

Assessing the consistency between observed and modelled route choices through GPS data

In traffic engineering, different assumptions on user behaviour are adopted in order to model the traffic flow propagation on the transport network. This paper deals with the classical hypothesis that drivers use the shortest possible path for their trip, pointing out the error related to using such approximation in practice, in particular in the context of dynamic origin-destination (OD) matrix estimation. If this problem is already well known in the literature, only few works are available, which provide quantitative and empirical analysis of the discrepancy between observed and modelled route sets and choices. This is mainly related to the complexity of collecting suitable data: to analyse route choice in a systematic way, it is necessary to have observations for a large period of time, since observing trajectories for the single user on a specific day could not be enough. Information is required for several days in order to analyse the repetitiveness and understand which elements influence this choice. In this work the use of the real shortest path for a congested network is evaluated, showing the differences between what we model and what users do. Results show that there is a systematic difference between the best possible choice and the actual choice, and that users clearly consider route travel time reliability in their choice process.In traffic engineering, different assumptions on user behaviour are adopted in order to model the traffic flow propagation on the transport network. This paper deals with the classical hypothesis that drivers use the shortest possible path for their trip, pointing out the error related to using such approximation in practice, in particular in the context of dynamic origin-destination (OD) matrix estimation. If this problem is already well known in the literature, only few works are available, which provide quantitative and empirical analysis of the discrepancy between observed and modelled route sets and choices. This is mainly related to the complexity of collecting suitable data: to analyse route choice in a systematic way, it is necessary to have observations for a large period of time, since observing trajectories for the single user on a specific day could not be enough. Information is required for several days in order to analyse the repetitiveness and understand which elements influence this choice. In this work the use of the real shortest path for a congested network is evaluated, showing the differences between what we model and what users do. Results show that there is a systematic difference between the best possible choice and the actual choice, and that users clearly consider route travel time reliability in their choice process

Detection of emotions in Parkinson's disease using higher order spectral features from brain's electrical activity

Non-motor symptoms in Parkinson's disease (PD) involving cognition and emotion have been progressively receiving more attention in recent times. Electroencephalogram (EEG) signals, being an activity of central nervous system, can reflect the underlying true emotional state of a person. This paper presents a computational framework for classifying PD patients compared to healthy controls (HC) using emotional information from the brain's electrical activity

Rare and contemporary dance as cultural mediators within a b-learning mode: the fuzzy logic perspective

The concept of cultural mediation via undergraduate courses in rare and contemporary dance within a blended learning (b-learning) mode is approached here through a fuzzy logic (FL)-based.modelling perspective. Students’ online interaction on the Moodle Learning Management System (LMS).during such b-learning courses was logged over an entire academic year, and the resulting data were.analysed using FL, in order to estimate users’ LMS Quality of Interaction (QoI). Using documental.analysis, the pedagogical design strategies per semester were transformed into concept maps and related.with the dynamically (per week) estimated QoIs. The latter were used by the teachers at the end of the.first semester to reflect upon and update their pedagogical planning, so as to enhance QoI in the second.semester. The results show the beneficial role of QoI in supporting more dynamic design of educational.scenarios, yet considering the inherent tendencies/attitudes of users’ interaction within different cultural.expressions

Inter-hemispheric EEG coherence analysis in Parkinson's disease : Assessing brain activity during emotion processing

Parkinson’s disease (PD) is not only characterized by its prominent motor symptoms but also associated with disturbances in cognitive and emotional functioning. The objective of the present study was to investigate the influence of emotion processing on inter-hemispheric electroencephalography (EEG) coherence in PD. Multimodal emotional stimuli (happiness, sadness, fear, anger, surprise, and disgust) were presented to 20 PD patients and 30 age-, education level-, and gender-matched healthy controls (HC) while EEG was recorded. Inter-hemispheric coherence was computed from seven homologous EEG electrode pairs (AF3–AF4, F7–F8, F3–F4, FC5–FC6, T7–T8, P7–P8, and O1–O2) for delta, theta, alpha, beta, and gamma frequency bands. In addition, subjective ratings were obtained for a representative of emotional stimuli. Interhemispherically, PD patients showed significantly lower coherence in theta, alpha, beta, and gamma frequency bands than HC during emotion processing. No significant changes were found in the delta frequency band coherence. We also found that PD patients were more impaired in recognizing negative emotions (sadness, fear, anger, and disgust) than relatively positive emotions (happiness and surprise). Behaviorally, PD patients did not show impairment in emotion recognition as measured by subjective ratings. These findings suggest that PD patients may have an impairment of inter-hemispheric functional connectivity (i.e., a decline in cortical connectivity) during emotion processing. This study may increase the awareness of EEG emotional response studies in clinical practice to uncover potential neurophysiologic abnormalities