arXiv:2404.10528v1 Announce Type: new
Abstract: Active travel is an essential component in intelligent transportation systems. Cycling, as a form of active travel, shares the road space with motorised traffic which often affects the cyclists’ safety and comfort and therefore peoples’ propensity to uptake cycling instead of driving. This paper presents a unique dataset, collected by cyclists across London, that includes video footage, accelerometer, GPS, and gyroscope data. The dataset is then labelled by an independent group of London cyclists to rank the safety level of each frame and to identify objects in the cyclist’s field of vision that might affect their experience. Furthermore, in this dataset, the quality of the road is measured by the international roughness index of the surface, which indicates the comfort of cycling on the road. The dataset will be made available for open access in the hope of motivating more research in this area to underpin the requirements for cyclists’ safety and comfort and encourage more people to replace vehicle travel with cycling.
Analyzing the Importance of Active Travel Data in Intelligent Transportation Systems
Active travel, which involves modes of transportation such as cycling and walking, plays a critical role in intelligent transportation systems. In order to promote active travel and encourage more individuals to choose sustainable modes of transportation over driving, it is crucial to understand the factors that impact the safety and comfort of cyclists. A recent study has provided a unique dataset that offers valuable insights into these factors, with the potential to revolutionize research in this field.
The dataset, collected by cyclists across London, includes a wide range of data such as video footage, accelerometer, GPS, and gyroscope data. By combining these different types of data, researchers are able to gain a comprehensive understanding of the cyclist’s experience on the road.
One of the key aspects of this dataset is the inclusion of safety ranking for each frame by an independent group of London cyclists. This allows researchers to assess the safety level of specific situations, identify potential hazards, and develop strategies to mitigate risks. By understanding the objects in the cyclist’s field of vision that might affect their experience, policymakers and urban planners can take proactive measures to improve cycling infrastructure and ensure the safety of cyclists.
In addition to safety rankings, the dataset also measures the quality of the road using the international roughness index of the surface. This information provides insights into the comfort of cycling on different roads, which is another crucial factor in people’s propensity to choose cycling over driving. With this data, researchers can identify areas where road conditions need improvement and suggest interventions to enhance the overall cycling experience.
The multi-disciplinary nature of this dataset is noteworthy. It combines video footage, sensor data, and human perception to offer a holistic view of the cyclist’s environment. This interdisciplinary approach brings together elements of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, as it leverages various technologies to create an immersive understanding of active travel.
Furthermore, the dataset’s availability as an open-access resource is paramount in encouraging further research in this area. By providing access to this data, more researchers can analyze and build upon the findings, ultimately contributing to a better understanding of the requirements for cyclists’ safety and comfort. This, in turn, can help inform policy decisions and interventions aimed at promoting active travel.
In conclusion,
this unique dataset has immense potential in advancing our understanding of active travel in intelligent transportation systems. By combining various data sources and incorporating safety rankings and road quality measurements, researchers can gain valuable insights into the factors that influence cycling behavior. The interdisciplinary nature of this dataset makes it relevant to the wider field of multimedia information systems, as it integrates technologies from different domains to create a comprehensive understanding of active travel. With the dataset being made openly available, it is expected to inspire further research and drive innovation in the design of safer and more comfortable cycling infrastructure.