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New perspectives on daily urban mobility: Harnessing the potential of Smart Card Travel data

日常城市出行的新视角：利用智能卡旅行数据的潜力

基本信息

批准号：
2092249
负责人：
金额：
--
依托单位：
University College London
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2018
资助国家：
英国
起止时间：
2018 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2092249
关键词：
New perspectives daily urban mobility

项目摘要

Smart Travel Cards (STCs) are introduced in many cities in the UK and elsewhere. The data form a novel resource routinely collected by transport authorities, complementing the wide range of travel data already held by those organisations. STCs record all electronic ticket transactions in public transport and in this way trace daily mobility in unprecedented detail for a large proportion of both local residents and visitors. In combination with other data held in the public and private domains, the data offer potential to understand, in new ways, the dynamics of daily, urban mobility for the benefit of transport organisations as well as important issues of wider concern in social science and policy. In particular, the data complement traditional, often costly data sources (Travel-To-Work component of the UK Census, travel diaries in household travel surveys) by offering a nearly real-time temporal resolution, geographical detail and wide population coverage. In assembling the data provided by TfWM (based on an existing formal data sharing agreement), the research will contribute to pressing social science and policy questions of achieving sustainable and inclusive mobility in the context of social inequalities and lifestyle choices (Kandt et al 2015). The research is also envisioned to deliver a framework to harness STC data for social science research more generally and thereby develop a blueprint for other cities that have introduced or will introduce STCs. The research also contributes directly to the objectives of TfWM of using their vast databases to support their mandate of delivering sustainable and inclusive mobility. While the STC data are particularly interesting because they deliver key variables of travel demand (e.g. trip generation, frequency, distance, time of day, dwelling time), a number of heuristics will have to be developed in order to derive useful indicators of travel, ascertain the coverage of the data, understand the extent and operation of bias and link the indicators to wider social domains, such as exclusion or health and well-being. Travel demand is a derived demand and may be understood as traces of all sorts of daily activities, which aggregated across population groups, neighbourhoods or regions may well add up to a detailed, multi-level understanding of urban dynamics. Current work on using STC data is still in its infancy, especially in relation to efforts to extend analytics beyond operational questions of the transport sector. Within the research (and in accordance to existing interests at TfWM), it will need to be established, how many residents are represented in routinely collected datasets. This question is related to bias in Big Data and hence relates to a common concern in Big Data Analytics. Specific methods of turning the data into information include strategies and techniques to (1) generate daily mobility profiles from travel transactions, (2) link trips to daily activities, e.g. by inferring trip destinations and purposes and (3) infer demographics from mobility profiles. As part of these undertakings, the student will need to make use of the implicit geographic information available in the data through data linkage of STC-recorded boardings and GPS vehicle tracking data. The research thus links to the long-established geodemographics with geo-spatial research undertaken by Paul Longley's team (e.g. Longley et al 2016, Longley et al 2015). The resulting expertise from these steps will enable the student to both develop research frameworks of Big Data Analytics in social science and address challenges in the transport sector.

智能旅行卡（STC）在英国和其他地方的许多城市推出。这些数据构成了交通管理机构定期收集的一种新资源，补充了这些组织已经掌握的广泛的旅行数据。STCs记录公共交通的所有电子车票交易，并以前所未有的详细程度跟踪大部分当地居民和游客的日常流动情况。与公共和私人领域的其他数据相结合，这些数据有可能以新的方式了解日常城市交通的动态，以使交通组织受益，以及社会科学和政策中更广泛关注的重要问题。特别是，这些数据补充了传统的，往往是昂贵的数据来源（英国人口普查的上班旅行部分，家庭旅行调查中的旅行日记），提供了近乎实时的时间分辨率，地理细节和广泛的人口覆盖面。在收集TfWM提供的数据（基于现有的正式数据共享协议）时，该研究将有助于在社会不平等和生活方式选择的背景下实现可持续和包容性流动的紧迫社会科学和政策问题（Kandt等人，2015）。该研究还设想提供一个框架，以更普遍地利用STC数据进行社会科学研究，从而为已经引入或将要引入STC的其他城市制定蓝图。该研究还直接有助于TfWM利用其庞大的数据库来支持其提供可持续和包容性流动性的任务的目标。STC的数据特别有趣，因为它们提供了旅行需求的关键变量（如旅行的产生、频率、距离、一天中的时间、停留时间），必须制定一些统计学，以得出有用的旅行指标，确定数据的覆盖范围，了解偏见的程度和运作，并将指标与更广泛的社会领域联系起来，例如排斥或健康和福祉。出行需求是一种衍生需求，可被理解为各种日常活动的痕迹，将不同人口群体、社区或区域的出行需求汇总起来，就可以对城市动态有一个详细的、多层次的了解。目前使用STC数据的工作仍处于起步阶段，特别是在努力将分析扩展到运输部门的业务问题之外。在研究中（根据TfWM的现有兴趣），需要确定有多少居民在常规收集的数据集中被代表。这个问题与大数据中的偏见有关，因此与大数据分析中的一个共同关注点有关。将数据转化为信息的具体方法包括以下策略和技术：（1）从旅行交易中生成日常移动性配置文件，（2）将旅行与日常活动联系起来，例如通过推断旅行目的地和目的，以及（3）从移动性配置文件中推断人口统计数据。作为这些工作的一部分，学生将需要通过STC记录的登机和GPS车辆跟踪数据的数据链接，利用数据中隐含的地理信息。因此，该研究与Paul Longley团队进行的地理空间研究（例如Longley et al 2016，Longley et al 2015）长期建立的地理人口统计学联系起来。这些步骤所产生的专业知识将使学生能够在社会科学中开发大数据分析的研究框架，并解决交通运输领域的挑战。