A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Characterizing cycling traffic fluency using big mobile activity tracking data
Abstract Mobile activity tracking data, i.e. data collected by mobile applications that enable activity tracking based on the use of the Global Navigation Satellite Systems (GNSS), contains information on cycling in urban areas at an unprecedented spatial and temporal extent and resolution. It can be a valuable source of information about the quality of bicycling in the city. Required is a notion of quality that is derivable from plain GNSS trajectories. In this article, we quantify urban cycling quality by estimating the fluency of cycling traffic using a large set of GNSS trajectories recorded with a mobile tracking application. Earlier studies have shown that cyclists prefer to travel continuously and without halting, i.e. fluently. Our method extracts trajectory properties that describe the stopping behaviour and dynamics of cyclists. It aggregates these properties to segments of a street network and combines them in a descriptive index. The suitability of the data to describe the cyclists' behaviour with street-level detail is evaluated by comparison with various data from independent sources. Our approach to characterizing cycling traffic fluency offers a novel view on the cyclability of a city that could be valuable for urban planners, application providers, and cyclists alike. We find clear indications for the data's ability to estimate characteristics of city cycling quality correctly, despite behaviour patterns of cyclists not caused by external circumstances and the data's inherent bias. The proposed quality measure is adaptable for different applications, e.g. as an infrastructure quality measure or as a routing criterion.
Highlights Cycling traffic fluency describes the smoothness of the traffic flow of bicycles. It corresponds to cyclists' preferences for continuous, uninterrupted travelling. Our approach estimates cycling traffic fluency based on GNSS cycling trajectories. Using big mobile tracking data enables a spatially extensive estimation. The estimation provides insight into the quality of city cycling.
Characterizing cycling traffic fluency using big mobile activity tracking data
Abstract Mobile activity tracking data, i.e. data collected by mobile applications that enable activity tracking based on the use of the Global Navigation Satellite Systems (GNSS), contains information on cycling in urban areas at an unprecedented spatial and temporal extent and resolution. It can be a valuable source of information about the quality of bicycling in the city. Required is a notion of quality that is derivable from plain GNSS trajectories. In this article, we quantify urban cycling quality by estimating the fluency of cycling traffic using a large set of GNSS trajectories recorded with a mobile tracking application. Earlier studies have shown that cyclists prefer to travel continuously and without halting, i.e. fluently. Our method extracts trajectory properties that describe the stopping behaviour and dynamics of cyclists. It aggregates these properties to segments of a street network and combines them in a descriptive index. The suitability of the data to describe the cyclists' behaviour with street-level detail is evaluated by comparison with various data from independent sources. Our approach to characterizing cycling traffic fluency offers a novel view on the cyclability of a city that could be valuable for urban planners, application providers, and cyclists alike. We find clear indications for the data's ability to estimate characteristics of city cycling quality correctly, despite behaviour patterns of cyclists not caused by external circumstances and the data's inherent bias. The proposed quality measure is adaptable for different applications, e.g. as an infrastructure quality measure or as a routing criterion.
Highlights Cycling traffic fluency describes the smoothness of the traffic flow of bicycles. It corresponds to cyclists' preferences for continuous, uninterrupted travelling. Our approach estimates cycling traffic fluency based on GNSS cycling trajectories. Using big mobile tracking data enables a spatially extensive estimation. The estimation provides insight into the quality of city cycling.
Characterizing cycling traffic fluency using big mobile activity tracking data
Brauer, Anna (author) / Mäkinen, Ville (author) / Oksanen, Juha (author)
2020-09-30
Article (Journal)
Electronic Resource
English
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