Using Object Detection on Social Media Images for Urban Bicycle Infrastructure Planning: A Case Study of Dresden

Martin Knura,Dirk Burghardt,Moris Zahtila,Florian Kluger,Bodo Rosenhahn,Jochen Schiewe

doi:10.3390/ijgi10110733

Abstract

With cities reinforcing greener ways of urban mobility, encouraging urban cycling helps to reduce the number of motorized vehicles on the streets. However, that also leads to a significant increase in the number of bicycles in urban areas, making the question of planning the cycling infrastructure an important topic. In this paper, we introduce a new method for analyzing the demand for bicycle parking facilities in urban areas based on object detection of social media images. We use a subset of the YFCC100m dataset, a collection of posts from the social media platform Flickr, and utilize a state-of-the-art object detection algorithm to detect and classify moving and parked bicycles in the city of Dresden, Germany. We were able to retrieve the vast majority of bicycles while generating few false positives and classify them as either moving or stationary. We then conducted a case study in which we compare areas with a high density of parked bicycles with the number of currently available parking spots in the same areas and identify potential locations where new bicycle parking facilities can be introduced. With the results of the case study, we show that our approach is a useful additional data source for urban bicycle infrastructure planning because it provides information that is otherwise hard to obtain.

Highlights

Today, as cities grow and develop at high-speed rates, many of them actively reinforce greener ways of urban mobility to fight against pollution, traffic jams, noise, etc. [1]
We propose to use this method alongside others established in urban planning in order to enrich the data coverage and provide more comprehensive information for making decisions related to urban infrastructure investments, e.g., bicycle parking
If we overestimate the number of bicycles in certain images, or occasionally detect false positives of other wheeled objects such as motorcycles, we argue that assigning a higher relevance to these cells is an acceptable drawback for the focus of our case study, where we qualify locations as a priority to be further inspected by urban planners

Summary

Introduction

As cities grow and develop at high-speed rates, many of them actively reinforce greener ways of urban mobility to fight against pollution, traffic jams, noise, etc. [1]. As cities grow and develop at high-speed rates, many of them actively reinforce greener ways of urban mobility to fight against pollution, traffic jams, noise, etc. One of the encouraged ways of urban commuting is cycling for its beneficial effect on both the environment and personal health [2]. While this strategy helps to reduce the number of motorized vehicles on streets, it leads to a significant increase in the number of bicycles in urban areas, which raises the importance of bicycle infrastructure planning as a topic. Planning the cycling infrastructure is an important topic for both urban planners and cyclists

Methods

Results

Discussion

Conclusion