NEXt generation activity and travel behavioUr modelS: Bringing together choice modelling, ubiquitous computing and data science

下一代活动和出行行为模型：将选择建模、普适计算和数据科学结合在一起

基本信息

批准号：
MR/T020423/1
负责人：
Charisma Farheen Choudhury
金额：
$ 166.97万
依托单位：
University of Leeds
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FT020423%2F1
关键词：
NEXt generation activity travel behavioUr

项目摘要

In many countries around the world, the transport sector claims a major share of the public spending. For example, the total public spending on transport in the UK was £22.5 billion in 2018. The potential impacts of new transport decisions can be evaluated using mathematical models to predict what people will do, when and where, and how they will travel in-between different locations in any given scenario. These travel behaviour models are typically based on theories of economics and psychology and developed using survey data. However, new forms of mobility (e.g. self-driving cars, Uber, shared-bikes) and new types of users (e.g. older travellers, migrants) are leading to radical changes in the mobility landscape. The traditional data and models are failing to deal with the rising complexities of activity and travel patterns which motivates NEXUS. The limitations of the current mainstream models arise from multiple factors. Firstly, they assume travel behaviour is solely based on the age, income, attitudes, etc. of the traveller and the attributes of the alternatives (e.g. travel times, costs). They do not account for the myriad of psychological factors that could influence an individual's decision, for example, the effect of stress, fatigue or the 'thinking process' more generally. Secondly, the data used for developing the models typically rely on small-scale surveys where travellers are asked to report/log their past behaviour or to state their choices based on descriptions of hypothetical scenarios, which very often are not reliable measures of the real-world travel behaviour. On a parallel stream, large amounts of mobility data are constantly generated from sources like GPS, mobile phones and social media. Advanced technologies and machine learning (ML) methods have also made it possible to measure the 'mental state' of the travellers by simple wristbands, discrete clip-ons and smartphone-based sensors and infer their thinking processes from brain imaging. Further, advances in virtual reality (VR) technology has made it possible to immerse travellers in future scenarios to obtain more realistic responses. Bringing together new data and methodologies can lead to a step change in travel behaviour modelling - but the framework to unify these different streams of research is yet to be formulated. NEXUS proposes to address this research gap by developing methodologies to augment travel behaviour models with novel forms of data. These will include: (a) real-world mobility data generated from GPS, mobile phones and other passive sources; (b) dynamic data about the 'state-of-the-mind' measured using sensors; and (c) experimental data on travel behaviour from VR settings of hypothetical future scenarios. Utilizing passive mobility data and sensing mental states will involve utilizing state-of-the-art ML and ubiquitous computing techniques. Combining the different types of real-world and experimental data sources for predicting behaviour in new scenarios will involve integrating these in traditional travel behaviour modelling framework. Merging these techniques, for the very first time outside the lab-setting, will produce a richer set of travel behaviour models that can better deal with radically different transport scenarios and user-groups in the future. The models will be implemented in a microsimulation platform to simulate the mobility behaviour in different policy scenarios with increased accuracy and aid the planners and policy-makers in making more informed investment decisions. This multi-disciplinary research will build on and extend my past experience in behavioural modelling using big data and sensors. It will support my transition to a research leadership role at the University of Leeds and collaboration with globally renowned academics in transport, psychology and computing. Partnership with non-academic partners will ensure the quick transition of the research to practice and real-world impact.

在世界许多国家，交通运输部门占据了公共支出的主要份额，例如，2018 年英国的交通公共支出总额为 225 亿英镑。新交通决策的潜在影响可以使用数学进行评估。这些旅行行为模型通常基于经济学和心理学理论，并使用调查数据开发而成。流动性（例如自动驾驶汽车、优步、共享自行车）和新型用户（例如老年旅行者、移民）正在导致出行格局发生根本性变化，传统数据和模型无法应对日益复杂的活动和旅行。当前主流模型的局限性来自于多种因素，首先，他们假设旅行行为完全基于旅行者的年龄、收入、态度等以及替代方案的属性（例如旅行时间、它们没有考虑到可能影响个人决策的无数心理因素，例如压力、疲劳或更普遍的“思维过程”的影响。其次，用于开发模型的数据通常依赖于。小规模调查，要求旅行者报告/记录他们过去的行为或根据假设场景的描述陈述他们的选择，这通常不是现实世界旅行行为的可靠衡量标准。移动数据不断从 GPS 等来源生成，先进的技术和机器学习（ML）方法还使得通过简单的腕带、分立的夹子和基于智能手机的传感器来测量旅行者的“精神状态”并从大脑推断他们的思维过程成为可能。此外，虚拟现实（VR）技术的进步使旅行者能够沉浸在未来的场景中，以获得更真实的反应，从而可以使旅行行为建模发生重大变化——但统一的框架。这些不同的研究流NEXUS 提议通过开发利用新型数据形式增强旅行行为模型的方法来解决这一研究空白，这些方法包括： (a) 从 GPS、移动电话和其他被动来源生成的现实世界移动数据； (b) 使用传感器测量的有关“精神状态”的动态数据；以及 (c) 来自假设的未来场景的 VR 设置的旅行行为的实验数据。最先进的机器学习和结合不同类型的现实世界和实验数据源来预测新场景中的行为将涉及将这些技术集成到传统的旅行行为建模框架中，这将是首次在实验室环境之外产生。一套更丰富的出行行为模型，可以更好地处理未来截然不同的交通场景和用户群体。这些模型将在微观模拟平台中实施，以更高的精度模拟不同政策场景下的出行行为，并为规划者和决策者提供帮助。政策制定者这项多学科研究将建立并扩展我过去使用大数据和传感器进行行为建模的经验，它将支持我过渡到利兹大学的研究领导角色以及与全球知名学者的合作。与非学术合作伙伴的合作将确保研究快速转化为实践和现实世界的影响。