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.

在世界上许多国家，运输部门声称在公共支出中占有重要份额。例如，在2018年，英国运输的公共交通总支出为225亿英镑。可以使用数学模型来评估新运输决策的潜在影响，以预测人们在任何给定情况下在不同地点之间将在不同地点中旅行的方式，何时何地以及如何旅行。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。但是，新形式的移动性（例如自动驾驶汽车，Uber，共享自行车）和新型的用户（例如，年长的旅行者，移民）导致了移动性景观的根本变化。传统的数据和模型未能处理动机Nexus的活动和旅行模式的不断增长。当前主流模型的局限性来自多种因素。首先，他们认为旅行行为仅基于旅行者的年龄，收入，参与者等以及替代方案的属性（例如旅行时间，费用）。他们没有考虑到可能影响个人决定的无数心理因素，例如压力，疲劳或“思考过程”的影响。其次，用于开发模型的数据通常依赖于小规模的调查，要求旅行者根据假设情景的描述报告/记录其过去的行为或陈述其选择，这通常不是对现实世界旅行行为的可靠测量。在平行流上，不断从GP，手机和社交媒体等来源生成大量的移动性数据。先进的技术和机器学习（ML）方法还使通过简单的腕带，离散剪辑和基于智能手机的传感器来测量旅行者的“精神状态”，并从大脑成像中推断他们的思维过程。此外，虚拟现实（VR）技术的进步使在将来的情况下沉浸于旅行者以获得更现实的反应成为可能。将新的数据和方法汇总在一起可以导致旅行行为建模的步骤变化 - 但是统一这些不同研究流的框架尚未制定。 Nexus提议通过开发具有新型数据形式的旅行行为模型来解决这一研究差距。这些将包括：（a）从GPS，手机和其他被动来源生成的现实世界移动性数据；（b）有关使用传感器测量的“最先进”的动态数据；（c）从假设未来方案的VR设置中进行的有关旅行行为的实验数据。利用被动流动性数据和感知心理状态将涉及使用最先进的ML和无处不在的计算技术。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，将涉及将它们集成到传统的旅行行为建模框架中。将这些技术合并在实验室设定之外的第一次将产生更丰富的旅行行为模型，以便将来可以更好地处理完全不同的运输方案和用户组。这些模型将在微观仿真平台中实施，以模拟不同政策方案中的移动性行为，并提高准确性，并帮助计划者和政策制定者做出更明智的投资决策。这项多学科研究将基于和扩展我使用大数据和传感器在行为建模方面的经验。它将支持我在利兹大学（University of Leeds）和全球运输，心理学和计算方面与全球知名学者合作的研究领导角色的过渡。与非学术伙伴的伙伴关系将确保研究快速过渡到实践和现实世界的影响。