Distributed Acoustic Sensor System for Modelling Active Travel

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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.