A data science framework for planning the growth of bicycle infrastructures

Abstract Cities around the world are turning to non-motorized transport alternatives to help solve congestion and pollution issues. This paradigm shift demands on new infrastructure that serves and boosts local cycling rates. This creates the need for novel data sources, tools, and methods that allow us to identify and prioritize locations where to intervene via properly planned cycling infrastructure. Here, we define potential demand as the total trips of the population that could be supported by bicycle paths. To that end, we use information from a phone-based travel demand and the trip distance distribution from bike apps. Next, we use percolation theory to prioritize paths with high potential demand that benefit overall connectivity if a bike path would be added. We use Bogota as a case study to demonstrate our methods. The result is a data science framework that informs interventions and improvements to an urban cycling infrastructure.

[1]  A. El-geneidy,et al.  Build It. But Where? The Use of Geographic Information Systems in Identifying Locations for New Cycling Infrastructure , 2013 .

[2]  Roger Mackett,et al.  Changes in mode of travel to work: a natural experimental study of new transport infrastructure , 2015, International Journal of Behavioral Nutrition and Physical Activity.

[3]  Mathieu Bastian,et al.  Gephi: An Open Source Software for Exploring and Manipulating Networks , 2009, ICWSM.

[4]  Ziyou Gao,et al.  Switch between critical percolation modes in city traffic dynamics , 2017, Proceedings of the National Academy of Sciences.

[5]  Ali Abbas,et al.  The Propensity to Cycle Tool: An open source online system for sustainable transport planning , 2015, ArXiv.

[6]  Emilio Frazzoli,et al.  A review of urban computing for mobile phone traces: current methods, challenges and opportunities , 2013, UrbComp '13.

[7]  Dennis Luxen,et al.  Real-time routing with OpenStreetMap data , 2011, GIS.

[8]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[9]  Mohsen Ramezani,et al.  Revealing latent characteristics of mobility networks with coarse-graining , 2019, Scientific Reports.

[10]  Yunpeng Wang,et al.  Percolation transition in dynamical traffic network with evolving critical bottlenecks , 2014, Proceedings of the National Academy of Sciences.

[11]  Consuelo Uribe Mallarino.  Estratificación social en Bogotá: de la política pública a la dinámica de la segregación social , 2008 .

[12]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Kentaro Toyama,et al.  Project Lachesis: Parsing and Modeling Location Histories , 2004, GIScience.

[14]  Jennifer Dill,et al.  Bikeway Networks: A Review of Effects on Cycling , 2016 .

[15]  Angela Hull,et al.  Bicycle infrastructure: can good design encourage cycling? , 2014 .

[16]  Jiwon Kim,et al.  Identification of communities in urban mobility networks using multi-layer graphs of network traffic , 2017 .

[17]  N. Geroliminis,et al.  Existence of urban-scale macroscopic fundamental diagrams: Some experimental findings - eScholarship , 2007 .

[18]  Hoon Kim,et al.  Monte Carlo Statistical Methods , 2000, Technometrics.

[19]  Marta C. González,et al.  Understanding congested travel in urban areas , 2016, Nature Communications.

[20]  Xiaokun Wang,et al.  Prioritizing bicycle paths in Belo Horizonte City, Brazil: Analysis based on user preferences and willingness considering individual heterogeneity , 2014 .

[21]  Meead Saberi,et al.  Macroscopic dynamics and the collapse of urban traffic , 2018, Proceedings of the National Academy of Sciences.

[22]  Marta C. González,et al.  The path most traveled: Travel demand estimation using big data resources , 2015, Transportation Research Part C: Emerging Technologies.

[23]  Marta C. González,et al.  Measuring the Impacts of Economic Well Being in Commuting Networks - A Case Study of Bogota, Colombia , 2017 .

[24]  Yanhua Li,et al.  Planning Bike Lanes based on Sharing-Bikes' Trajectories , 2017, KDD.

[25]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[26]  Christian P. Robert,et al.  Monte Carlo Statistical Methods , 2005, Springer Texts in Statistics.

[27]  Igor Linkov,et al.  Resilience and efficiency in transportation networks , 2017, Science Advances.

[28]  I. Thomas,et al.  Regions and borders of mobile telephony in Belgium and in the Brussels metropolitan zone , 2010 .

[29]  Marta C. González,et al.  Origin-destination trips by purpose and time of day inferred from mobile phone data , 2015 .