The pulse of the cycling city: visualising Madrid bike share system GPS routes and cycling flow (original) (raw)
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Bicycling has grown significantly in the past ten years. In some regions, the implementation of large-scale bike-sharing systems and improved cycling infrastructure are two of the factors enabling this growth. An increase in non-motorized modes of transportation makes our cities more human, decreases pollution, traffic, and improves quality of life. In many cities around the world, urban planners and policymakers are looking at cycling as a sustainable way of improving urban mobility. Although bike-sharing systems generate abundant data about their users' travel habits, most cities still rely on traditional tools and methods for planning and policy-making. Recent technological advances enable the collection and analysis of large amounts of data about urban mobility, which can serve as a solid basis for evidence-based policy-making. In this paper, we introduce a novel analytical method that can be used to process millions of bike-sharing trips and analyze bikesharing mobility, abstracting relevant mobility flows across specific urban areas. Backed by a visualization platform, this method provides a comprehensive set of analytical tools to support public authorities in making data-driven policy and planning decisions. This paper illustrates the use of the method with a case study of the Greater Boston bike-sharing system and, as a result, presents new findings about that particular system. Finally, an assessment with expert users showed that this method and tool were considered very useful, relatively easy to use and that they intend to adopt the tool in the near future.
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Bicycle sharing systems are increasingly being deployed in urban areas around the world, alongside online maps that disclose the state (i.e., location, number of bicycles/number of free parking slots) of stations in each city. Recent work has demonstrated how regularly monitoring these online maps allows for a granular analysis of a city’s cycling trends; further, the literature indicates that different cities have unique spatio-temporal patterns, reducing the generalisability of any insights or models derived from a single system. In this work, we analyse 4.5 months of online bike-sharing map data from ten cities which, combined, have 996 stations. While an aggregate comparison supports the view of cities having unique usage patterns, results of applying unsupervised learning to the temporal data shows that, instead, only the larger systems display heterogeneous behaviour, indicating that many of these systems share intrinsic similarities. We further show how these similarities are reflected in the predictability of stations’ occupancy data via a cross-city comparison of the error that a variety of approaches achieve when forecasting the number of bicycles that a station will have in the near future. We close by discussing the impact of uncovering these similarities on how future bicycle sharing systems can be designed, built, and managed.
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Cycling in British cities is increasing but at a slow rate nationally. The ultimate realizations of cycling benefits in urban areas, such as cities in North East England, are hampered by lack of appropriate data to aid in our understanding of cycling behaviors to inform policy strategies and improve cycling uptake as well as data processing methodologies. Several efforts are being made to enhance data availability to understand cycling behaviors to inform policy strategies for which this research aims to contribute by providing evidence on the use of the area's cycling infrastructure by utility cyclists. A proposed corridor space analytical approach was used to analyze the newly collected 7-day GPS data from 79 utility cyclists to estimate the extent to which respondents used the area's cycling infrastructure. The data was used together with the area cycling infrastructure data from Newcastle City Council. Findings from the corridor space analysis suggest that 57.4 % of cyclists from sample prefer cycling on the cycle network, while 33.8 % cycle outside the cycle network with 8.8 % near the cycle network. Also, for all cycle trips, men tend to dominate in cycling on and near the cycle network. Both the males and females tend to use the cycle network more than off the network for utility trips. With 42.6 % of cyclists still cycling outside the designated cycle network, it is imperative that policy initiatives are aimed towards investing in cycling research and infrastructure to further deepen our understanding to encourage cycling around the study area. It was also suggested that the captured detailed actual route choice preferences could serve as input to the development of agent-based models towards understanding cycling behaviors around the study area.
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Lecture Notes in Geoinformation and Cartography, 2015
The neglect of non-motorized transportation options in transport planning and demand modelling is gradually being addressed in the United Kingdom. In route choice research there has been, in recent years, a trend away from modelling hypothetical situations towards field testing. This is partly due to the effective use of emerging GPS technologies for gathering travel behavior data in "wild" urban spaces, making it possible to observe realistic situations. Such data on detailed travel behaviors offer possibilities for further research, especially in the non-motorized transportation arena. Globally, there has been progress in the development of cyclists' route choice models using revealed preference GPS data from various geographical and local contexts. However, we have little evidence on detailed cyclists' route choices in the UK in a national and local context. This is particularly the case with low cycling participation cities in North England, where there have been various attempts to increase cycling uptake in recent years. This chapter fills this knowledge gap by undertaking a route choice analysis using the cycling-friendly version of OpenStreetMap (OSM) as the transportation network for analysis, alongside GPS tracks (7 days) and travel diary data for 79 Utility Cyclists around Newcastle upon Tyne in North East England. We examined specific variables as proposed in the relevant cycling literature and used these to develop a model testing the null hypothesis that network restrictions (i.e. one way, turn restrictions and access) do not have any significant influence on the movement of commuter cyclists. The findings suggest that OSM can provide a robust transportation network for cycling research, in particular when combined with GPS track data. The observed routes were significantly longer than their shortest path alternatives, the only exception being the straight-line distance between the observed bike routes and the unrestricted network routes, where the difference was not statistically significant.