Distributed Acoustic Sensor System for Modelling Active Travel

用于建模主动行程的分布式声学传感器系统

• 批准号: EP/X01262X/1
• 负责人: Mona Jaber
• 金额: $ 52.44万
• 依托单位: Queen Mary University of London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX01262X%2F1
• 关键词: Distributed; Acoustic; Sensor; System; Modelling

In a time where climate change is an imminent threat, Active Travel (AT) has become a priority in the United Kingdom (UK) and a pathway towards sustainable living. AT is defined as making a journey by physically active means, e.g., walking or cycling. In the UK, the transport sector is the highest contributor of emissions with 61% of this contribution caused by private cars and taxis. Replacing motored journeys with AT firstly promises to reduce these emissions. Moreover, AT is a form of exercise that has been shown to improve physical and mental health; hence, reduces the need of medical care and increases happiness and productivity. Interventions to promote AT include ensuring safety of commuters through cycle/pedestrian lanes, safe cycle parking, bike-sharing, cycling training, bike loan schemes, electrically assisted bikes, community/school initiatives, among others. The challenge that authorities face is the lack of insights on which type of intervention would be more effective in different areas. Indeed, the same scheme would result in different AT uptake since the latter depends on predominant trends and road infrastructure in each area. It follows that, in each area, some schemes are likely to be more effective than others.There is a rising need to model changes in AT trends in relation to different interventions. State-of-the-art research for modelling AT trend mostly relies on video footage which is used to identify and predict the path of pedestrians. There are several drawbacks to such approaches. Firstly, video footage is negatively impacted from adverse weather conditions and lack of light. Secondly, it is cost-inhibitive to realise uninterrupted 360 degrees visibility using video cameras in a built environment. Thirdly, the video footage needs to be high resolution, hence contains private information about people. Such information challenges General Data Protection Regulation (GDPR) whilst is not required for modelling active mobility.DASMATE aims to develop a new approach for modelling AT trends in an urban environment by leveraging the incipient advances in Distributed Acoustic Sensor (DAS) systems. DAS reuses underground fibre optic cables as distributed strain sensing where the strain is caused by moving objects above ground. Given that the sensors are underground, DAS is not affected by weather nor light. Fibre cables are often readily available and offer a continuous source for sensing along the length of the cable. Moreover, DAS systems offer a GDPR-compliant source of data that does not include private information such as face colour, gender, or clothing. DASMATE in centred on two aspects of AT modelling based on DAS analysis. The first consists of identifying the type of AT (walking, jogging, skateboarding, cycling, etc.) at any time of the day in a monitored area. The second is concerned with predicting the path of active travellers to inform on the possibility of collision with moving vehicles (which may be driver-less). This a pioneering project that aims to establish the first framework for processing DAS data to extract samples representing AT and build a machine learning pipeline to infer knowledge related to both aspects.This project will be worked together with partners both from the industry and UK authorities such as Fotech and London Borough of Tower Hamlet. The principal investigator (PI) maintains a strong track record in signal processing with professional skills machine learning, and optimization. The industry partner Fotech is leading the smart city application of DAS and has been collaborating with PI for a year on DAS-based vehicle classification and occupancy detection. Moreover, a unique DAS dataset for AT modelling that will enable this project has been collected jointed through this collaboration. The London Borough of Tower Hamlet finds value in this project and has offered to trial the technology outcomes in the borough to measure the efficacy of planned AT schemes.

在气候变化成为迫在眉睫的威胁的时候，主动旅行（AT）已经成为英国的优先事项，也是实现可持续生活的途径。AT被定义为通过身体活动方式进行旅行，例如，步行或骑车。在英国，交通部门是排放量最大的部门，其中61%的排放量来自私人汽车和出租车。用自动变速器取代汽车旅行首先承诺减少这些排放。此外，AT是一种锻炼形式，已被证明可以改善身心健康;因此，减少了对医疗保健的需求，增加了幸福感和生产力。促进AT的干预措施包括通过自行车/人行道确保通勤者的安全，安全的自行车停车场，自行车共享，自行车培训，自行车贷款计划，电动辅助自行车，社区/学校倡议等。当局面临的挑战是缺乏对不同领域哪种类型的干预更有效的见解。事实上，同样的计划将导致不同的AT吸收，因为后者取决于每个地区的主要趋势和道路基础设施。因此，在每个领域，有些计划可能比其他计划更有效，越来越需要模拟与不同干预措施有关的AT趋势变化。最先进的AT趋势建模研究主要依赖于用于识别和预测行人路径的视频片段。这种方法有几个缺点。首先，视频镜头受到恶劣天气条件和缺乏光线的负面影响。其次，在建筑环境中使用摄像机实现不间断的360度可见性是成本高昂的。第三，视频片段需要高分辨率，因此包含有关人的私人信息。这些信息挑战了通用数据保护条例（GDPR），而对主动移动性建模并不需要这些信息。DASMATE旨在通过利用分布式声学传感器（DAS）系统的初期进展，开发一种新的方法来建模城市环境中的AT趋势。DAS重复使用地下光纤电缆作为分布式应变传感，其中应变由地面上的移动物体引起。由于传感器位于地下，DAS不受天气或光线的影响。光纤电缆通常是容易获得的，并且提供用于沿电缆的长度沿着感测的连续源。此外，DAS系统提供符合GDPR的数据源，不包括面部颜色、性别或服装等私人信息。DASMATE集中在基于DAS分析的AT建模的两个方面。第一个包括识别AT的类型（步行，慢跑，滑板，骑自行车等）。一天中的任何时候都可以在监控区域内进行。第二个是预测主动旅行者的路径，以告知与移动车辆（可能是无人驾驶的）碰撞的可能性。这是一个开创性的项目，旨在建立第一个处理DAS数据的框架，以提取代表AT的样本，并建立机器学习管道来推断与这两个方面相关的知识。该项目将与Fotech和伦敦陶尔哈姆雷特等行业和英国当局的合作伙伴共同努力。首席研究员（PI）在信号处理方面保持着良好的记录，拥有专业的机器学习和优化技能。行业合作伙伴Fotech正在领导DAS的智慧城市应用，并与PI在基于DAS的车辆分类和占用检测方面合作了一年。此外，一个独特的DAS数据集的AT建模，将使这个项目已经收集通过这种合作联合。塔哈姆雷特的伦敦自治市镇发现了这个项目的价值，并提出在自治市镇试用技术成果，以衡量计划中的AT方案的有效性。