Optimising the Loading Diversity of Rail Passenger Crowding using On-Board Occupancy Data

Crowded conditions on trains can lead to lower passenger satisfaction, discourage rail travel, result in negative economic impacts and are a factor in a number of health and safety hazards. In the UK there is an annual survey of rail passenger crowding, although the measures used do not reflect coach-by-coach variations, nor do they reflect variations across the peak period. In this MPhil thesis I investigated the application of weight-based automatic passenger counting data to deliver more even loadings on trains through the provision of new real-time and static solutions. In addition I investigated the potential benefits of such solutions in terms of reduced dwell times and reduced crowding. The overall concept proposed was to make the most of the existing available capacity; for example, so that no-one is standing when seats are available. Through analysing a large sample of air suspension data, I identified station-specific trends where some coaches were over capacity while others had spare capacity. I also conducted a critical review of academic research into on-train crowding and solutions that seek to optimise ‘loading diversity’. This study contributes to this emerging subject area in several ways: I propose two new metrics to describe inter-coach loading diversity that, unlike existing metrics, contain information relative to the capacity; I have revealed a link between the inter-coach loading diversity metrics and estimated boarding times, with trains classified as ‘very uneven’ on departure typically having dwell times of approximately five to ten seconds greater than services that were classified as being ‘even’ with a similar total number of passengers on board; and finally I have applied classification supervised learning techniques to predict the load factor for a given service and these predictors were an improvement over taking the historical average

Emotive computing may have a role in telecare

This brief paper sets out arguments for the introduction of new technologies into telecare and lifestyle monitoring that can detect and monitor the emotive state of patients. The significantly increased use of computers by older people will enable the elements of emotive computing to be integrated with features such as keyboards and webcams, to provide additional information on emotional state. When this is combined with other data, there will be significant opportunities for system enhancement and the identification of changes in user status, and hence of need. The ubiquity of home computing makes the keyboard a very attractive, economic and non-intrusive means of data collection and analysis

eHealth and the Internet of Things

To respond to an ageing population, eHealth strategies offer significant opportunities in achieving a balanced and sustainable healthcare infrastructure. Advances in technology both at the sensor and device levels and in respect of information technology have opened up other possibilities and options. Of significance among these is what is increasingly referred to as the Internet of Things, the interconnection of physical devices to an information infrastructure. The paper therefore sets out to position the Internet of Things at the core of future developments in eHealt

Linking recorded data with emotive and adaptive computing in an eHealth environment

Telecare, and particularly lifestyle monitoring, currently relies on the ability to detect and respond to changes in individual behaviour using data derived from sensors around the home. This means that a significant aspect of behaviour, that of an individuals emotional state, is not accounted for in reaching a conclusion as to the form of response required. The linked concepts of emotive and adaptive computing offer an opportunity to include information about emotional state and the paper considers how current developments in this area have the potential to be integrated within telecare and other areas of eHealth. In doing so, it looks at the development of and current state of the art of both emotive and adaptive computing, including its conceptual background, and places them into an overall eHealth context for application and development

DATA VALIDATION CONSTRAINT LANGUAGES

Application programming interfaces (APIs) are becoming increasingly prevalent across the industry. At its heart, an API is a means for transferring data between two systems in an interoperable way. While the data that is passed across an API is generally well structured, that structure can be arbitrarily complex. Determining whether or not a set of data is valid is often not straightforward, as there may be complex dependencies between different data items in a set. Writing custom code to perform such a validation is time consuming and prone to error. To address challenges of these types, techniques are presented herein that support a language for expressing complex constraints on YANG (e.g., see the Internet Engineering Task Force (IETF) Request for Comments (RFC) 7950) data that is closely tied to the underlying YANG data model such that the evaluation of the constraints is context-aware and has knowledge of the data model. Aspects of the presented techniques offer a number of benefits including, for example, making the writing of constraints much easier, reducing development costs by enabling checking for many more errors at compile time, increasing quality and security (e.g., by automating input validation and thus avoiding the need to write complex and bug-prone manual validation code), etc