Assessing pedestrian level barriers using mobile robot to achieve 15-minute city concept

To mitigate the increasing greenhouse gas (GHG) emissions in cities, various urban mobility models have been developed. One that is gaining increasing popularity is that of the “15-Minute City”. This human-centric concept stipulates that important facilities in the neighborhood must be accessible within 15 minutes on foot or by non-motorized vehicles. Theoretical and experimental studies were carried out to test the applicability of the 15-minute city concept. Research shows that the applicability of the concept mainly depends on the residential and working population, the city size and density, the service proximity and the walkability of the neighborhood.

In this context, many studies aim to investigate the current state of the urban accessibility based on open street maps or remote sensing devices that can monitor the static situation of larger urban areas. However, these methods fail to capture the dynamic and random events on the pedestrian level that often have a direct impact on walking duration and an indirect impact on the accessibility of services and residents’ willingness to walk. Until now, only limited studies focus on the ease of mobility and comfort on the pedestrian level, as well as the potential barriers to walking that can increase the walking time. One of the reasons for the lack of study is that experiments focusing on these aspects can potentially cost time and resources due to the uncertain and random nature of dynamic events in complex urban contexts.

In this study, we propose a novel methodology to monitor random events in urban situations, aiming to non-intrusively capture dynamic obstacles that may affect the ease of pedestrian mobility and pedestrian comfort with low cost and less manpower. A mobile robot (Diabolo Robot, Direct Drive Technology Limited) is utilized to mimic the walking behavior of pedestrians by exhibiting different walking speeds. Equipped with sensors and a camera, the mobile robot can detect and record the dynamic obstacles along the route by monitoring the acoustic situation, visual images, air quality index, wind speed, etc.

The collected results are transmitted via the Rasperry Pi board and stored locally for further analysis. By analyzing the collected data, we can identify the most common events on the pedestrian level that can affect walking duration. The outcome serves as valuable support in the development and application of the 15-minute city concept from a more pedestrian-centered perspective.