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.

在气候变化是迫在眉睫的威胁的时代，主动旅行（AT）已成为英国（英国）的优先事项，并且是通往可持续生活的途径。 AT定义为通过体育活动（例如步行或骑自行车）进行旅程。在英国，运输部门是排放的最高贡献者，其中61％由私人汽车和出租车造成。首先承诺要替换开车旅行，以减少这些排放。此外，AT是一种锻炼形式，已被证明可以改善身心健康。因此，减少了对医疗服务的需求，并提高了幸福和生产力。促进的干预措施包括通过自行车/行人车道确保通勤者的安全，安全自行车停车，自行车共享，骑自行车培训，自行车贷款计划，电动辅助自行车，社区/学校计划等。当局面临的挑战是缺乏有关哪种干预类型在不同领域更有效的见解。实际上，由于后者取决于每个地区的主要趋势和道路基础设施，因此相同的方案会导致吸收时不同。因此，在每个领域，某些方案可能比其他方案更有效。在与不同干预措施相关的趋势上的更改需要建模。用于建模趋势的最先进的研究主要取决于用于识别和预测行人路径的视频录像。这种方法有几个缺点。首先，视频镜头受到不利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度可见度是具有成本抑制的。第三，录像带必须是高分辨率，因此包含有关人员的私人信息。这种信息挑战了一般数据保护调节（GDPR），而对主动移动性进行建模并不需要。Drastrate旨在通过利用分布式声学传感器（DAS）系统的初期进步来开发一种新的城市环境趋势建模方法。 DAS REUSE地下光纤电缆作为分布的应变感应，在地面上移动物体引起的应变。鉴于传感器在地下，DAS不受天气和光线的影响。纤维电缆通常很容易获得，并提供沿电缆长度传感的连续源。此外，DAS系统提供了符合GDPR的数据来源，其中不包括私人信息，例如面部颜色，性别或服装。基于DAS分析的AT建模的两个方面的dasmate集中。第一个是在一天中的任何时间在受监视区域中的任何时间识别AT（步行，慢跑，滑板，骑自行车等）的类型。第二个关注的是预测活跃旅客的道路，以告知与移动车辆碰撞的可能性（这可能是无驾驶员）。这个开创性的项目旨在建立处理DAS数据的第一个框架，以提取代表机器学习管道的样本，以推断与这两个方面相关的知识。该项目将与行业和英国当局（例如Fotech and London Borough of Tower Hamlet）合作。首席研究员（PI）通过专业技能机器学习和优化保持了信号处理方面的良好记录。行业合作伙伴Fotech正在领导DAS的智能城市应用程序，并与PI合作了一年，用于基于DAS的车辆分类和占用检测。此外，一个独特的DAS数据集可用于建模，该数据集将通过此协作收集该项目。伦敦塔村庄的伦敦自治市镇在该项目中发现了价值，并提出试用该行政区的技术成果，以衡量计划中计划的效力。

项目成果

IoT and machine learning for enabling sustainable development goals

• DOI: 10.3389/frcmn.2023.1219047
• 发表时间: 2023-07-03
• 期刊: FRONTIERS IN COMMUNICATIONS AND NETWORKS
• 作者: Jaber，Mona