By 2030, Active Travel England - an executive agency sponsored by the Department for Transport - wants to see cars left at home and 50 per cent of all trips in towns and cities undertaken by active travel (AT).
To this end, the government has made £3.2bn available to ensure the provision of AT schemes that deliver to new national standards, but local authorities can be challenged by the lack of insights into suitable AT interventions in a given area.
Now, Dr Mona Jaber, an IoT lecturer at Queen Mary University of London, is leading DASMATE (Distributed Acoustic Sensor System for Modelling Active Travel), an EPSRC-funded project being undertaken with industry partner Fotech and the London Borough of Tower Hamlets.
“It is anticipated that the preliminary outcomes of this pilot study would be used to inform how trials in urban areas, such as Tower Hamlets, should be planned,” said Dr Jaber. “Tower Hamlets are interested in measuring the uptake of active travel before and after a planned intervention.”
DAS reuses underground fibre optic cables as distributed strain sensing where the strain is caused by moving objects above ground. DAS is not affected by weather or light and the fibre optic cables are often readily available, offering a continuous source for sensing along the length of the cable. Unlike video cameras, DAS systems also offer a GDPR-compliant source of data.
DASMATE will concentrate two aspects of AT modelling based on DAS analysis, the first to identify the type of AT at any time of the day in a monitored area, the second with predicting the path of active travellers to inform on the possibility of collision with moving vehicles.
The project will be informed by two datasets - Cellarhead and Fleet – acquired in collaboration with Fotech. Dr Jaber told The Engineer that Cellarhead data was collected at a heavy traffic intersection in Stoke-on-Trent where AT included walking, jogging, cycling, skateboarding, and riding a non-electric scooter.
“This dataset is considered noisy as the active travel DAS signals would be overshadowed by those generated by heavy vehicles,” she said.
The second dataset was collected in a very low traffic road in Fleet but close to a construction site with heavy equipment and related activities.
“This dataset is considered ‘cleaner’ since we tried to avoid data collection during noisy activities from the heavy machinery at the construction site,” said Dr Jaber, adding that the fibre lines attached to the DAS interrogators used to collect both datasets were located underground along the edge of the test roads.
“In principle, a single DAS interrogator can be used to monitor a road stretch of up to 40km. It is not certain if it would be possible to detect and classify active travel over such distances since the signal generated is likely to be weaker than normal DAS signals. This is one of the questions that would be answered in this study.”
Dr Jaber continued: “Based on our initial investigation, it is possible to isolate an active travel DAS signal in the presence of heavy traffic as these are significantly different in time and space. The data collected from Cellarhead would help us confirm this finding and identify other challenges and possible technologies to mitigate these.”
By predicting the path of active travellers, the researchers believe DAS can help inform on the possibility of collision with moving vehicles, which at some point in the future may be connected or driverless and fitted with LiDAR, cameras, sonars, and radars
“This project does not mean to replace this type of vehicle-centric intelligence but to complement it with infrastructure-centric intelligence,” said Dr Jaber. “The latter is informed by the analysis of the DAS signal and may be used to control traffic signals and/or relay the information to connected vehicles through V2X technologies. This project is not concerned with the communication technology [V2X]; instead, it aims to create the infrastructure-centric knowledge that would be useful to controlling or informing traffic signals and connected vehicles.”
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