Towards More Worthwhile Rail Journeys: Exploring the Role of Personal Data in Augmenting Rail Travellers' Use of Travel Time

信息通信技术的最新进展为个人提供了创造性地应对日常挑战的能力和工具。智能手机和无处不在的互联网接入已经成为我们日常生活的一部分，这一点尤其正确。这导致了个人每天在社会各个方面产生的数据爆炸式增长。交通运输业也不例外，因为旅行者在移动中越来越多地联系在一起。就交通部门而言，为了应对公共交通需求的增长，他们多年来一直在增加支出。2014/15年度，英国公共交通支出为206亿英镑，比10年前（2004/05年度）增长29%，其中约一半的支出用于铁路（37%）和当地公共交通（14%）。面对日益无处不在的设备和技术，旅行者有能力利用的一个领域是利用他们的旅行时间，尽管通常缺乏支持。智能手机的普及率达到72%，54%的人认为智能手机是他们旅行体验的必需品，难怪超过一半的英国旅行者总是在寻找优化他们旅行的方法。不幸的是，直到最近，交通研究和政策都将这段时间定义为“浪费”在“真正”活动之间的时间，并试图将其最小化。然而，有定性证据表明旅行时间具有积极的效用，超过四分之三的铁路乘客不认为使用他们的旅行时间是浪费，而且绝大多数人通过移动技术，工作或休闲阅读材料以及基础设施设计或设施等组合来装备自己利用这段时间。研究声明：本博士将尝试挑战当前关于旅行时间使用的负效用的正统观点，并倡导其对政策制定者和乘客的潜在好处。特别是，它将探索运营商如何提高乘客的旅行时间行为。这可以采取将个人或集体个人数据纳入框架的形式，将支持旅行时间利用的技术基础设施商业化或优化，也可以通过基于个人数据见解的干预措施设计，帮助交通工作人员支持旅行者的活动。该研究将与泰利斯英国公司合作进行。研究问题：该项目的总体研究目标是了解个人数据如何融入铁路乘客旅行时间使用的论述，特别是如何利用它来支持他们的旅行时间需求。目的深入分析乘客在旅途中产生的个人数据，以及这些数据可以揭示的乘客旅行时间使用情况。评估可用于获取乘客旅行时间使用数据的技术和基础设施分析可用于利用乘客个人数据优化服务提供的框架和基础设施。了解现有的非流动性数据集是否可以与乘客个人数据相结合，以提供有关旅行时间行为的更有用的见解。

Recent advances in ICTs have given individuals the power and tools to creatively tackle everyday challenges they face. This is particularly true with the case smartphones and ubiquitous Internet access, which have become a part of our daily lives. This has led to an explosion in the data generated by individuals each day across various facets of society. The transport industry is no exception, as travellers are increasingly connected on the move. For their part, transport authorities have responded to an increase in demand for public transport with increased spending over the years. In 2014/15, £20.6 billion was spent on public transport in the UK, representing a 29% increase compared to 10 years ago (2004/05) - about half of this spending going towards railways (37%) and local public transport (14%).In the face of increasing ubiquity of devices and technologies, one area that travellers are equipped to take advantage of, though usually poorly supported, is in the use of their travel time. With a smartphone penetration at 72% of the population - 54% deeming it necessary to their travel experience - it is no wonder that over half of UK travellers always look for ways to optimize their journey. Unfortunately, transport studies and policies have until recently framed this time as 'wasted' time in between 'real' activities and sought to minimise it. However, there is qualitative proof that travel time has a positive utility, with over three quarters of rail passengers not considering the use of their travel time as a waste and a large majority equipping themselves to utilise this time through a combination of objects such as mobile technologies, work-related or leisure reading material and infrastructural design or facilities.Research StatementThis PhD will attempt to contribute to challenging the current orthodoxy on the disutility of travel time use, and advocate its potential benefits to both policy makers and passengers. In particular, it will explore ways in which operators can augment the travel time behaviour of passengers. This could be in the form of frameworks that incorporate individual or collective personal data to commercialise or optimize the technology infrastructure that underpins the use of travel time or through the design of interventions based on personal data insights that can aid transport staff in supporting traveller activity. The research will be undertaken in collaboration with Thales UK.Research QuestionsAimThe overarching research aim of the project will be to understand how personal data fits into the discourse on rail passenger's travel time use, particularly how it can be harnessed to support their travel time needs.ObjectivesIn-depth analysis of personal data generated by passengers during journeys and what that can reveal about passenger travel time use.Evaluation of technologies and infrastructure available to capture passenger data on travel time useAnalysis of frameworks and infrastructure that can be employed to optimize service provision using personal data of passengers.Understand whether existing non-mobility datasets can be combined with passenger personal data to provide more useful insights on travel time behaviour.