Distributed Acoustic Sensor System for Modelling Active Travel

在气候变化迫在眉睫的时代，主动旅行（AT）已成为英国（UK）的优先事项，也是实现可持续生活的途径。自动驾驶被定义为通过身体活动的方式进行旅行，例如步行或骑自行车。在英国，交通部门是排放量最大的贡献者，其中61%是由私家车和出租车造成的。用自动驾驶汽车代替汽车出行首先有望减少这些排放。此外，体育锻炼是一种已被证明可以改善身心健康的锻炼形式；因此，减少了对医疗保健的需求，增加了幸福感和生产力。促进自动驾驶的干预措施包括通过自行车/行人专用道、安全的自行车停放、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/学校举措等确保通勤者的安全。当局面临的挑战是，缺乏对不同领域哪种干预更有效的认识。事实上，同样的方案会导致不同的自动运输系统使用率，因为后者取决于每个地区的主要趋势和道路基础设施。由此可见，在每个领域，有些方案可能比其他方案更有效。现在越来越需要根据不同的干预措施来模拟AT趋势的变化。目前研究的AT趋势建模主要依赖于视频片段，用于识别和预测行人的路径。这种方法有几个缺点。首先，视频片段受到恶劣天气条件和光线不足的负面影响。其次，在建筑环境中使用摄像机实现不间断的360度可视性成本较低。第三，视频片段需要高分辨率，因此包含了人们的私人信息。这些信息挑战了通用数据保护条例（GDPR），而不是建模主动移动性所必需的。DASMATE旨在通过利用分布式声学传感器（DAS）系统的初步进展，开发一种新的方法来模拟城市环境中的AT趋势。DAS重新使用地下光缆作为分布式应变传感，其中应变是由地面上移动的物体引起的。由于传感器在地下，DAS不受天气和光线的影响。光纤电缆通常很容易获得，并提供沿电缆长度的连续传感源。此外，DAS系统提供符合gdpr的数据来源，不包括面部颜色、性别或服装等私人信息。DASMATE集中于基于DAS分析的AT建模的两个方面。第一种方法是在监测区域内确定一天中任何时间的运动类型（步行、慢跑、滑板、骑自行车等）。第二个是预测行人的路径，以告知与行驶车辆（可能是无人驾驶车辆）发生碰撞的可能性。这是一个开创性的项目，旨在建立处理DAS数据的第一个框架，以提取代表AT的样本，并建立一个机器学习管道来推断与这两个方面相关的知识。该项目将与来自行业和英国当局（如Fotech和伦敦塔哈姆雷特自治市镇）的合作伙伴共同合作。首席研究员（PI）在信号处理方面保持着良好的记录，具有专业技能，机器学习和优化。行业合作伙伴Fotech正在引领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进行了一年的合作。此外，通过此次合作还收集了一个用于AT建模的独特DAS数据集，该数据集将使该项目成为可能。伦敦塔哈姆雷特区发现了这个项目的价值，并提出在该区试用技术成果，以衡量计划的AT方案的有效性。

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.