Crowdsourced Quantification and Visualization of Urban Mobility Space Inequality

Most cities are car-centric, allocating a privileged amount of urban space to cars at the expense of sustainable mobility like cycling. Simultaneously, privately owned vehicles are vastly underused, wasting valuable opportunities for accommodating more people in a livable urban environment by occupying spacious parking areas. Since a data-driven quantification and visualization of such urban mobility space inequality is lacking, here we explore how crowdsourced data can help to advance its understanding. In particular, we describe how the open-source online platform What the Street!? uses massive user-generated data from OpenStreetMap for the interactive exploration of city-wide mobility spaces. Using polygon packing and graph algorithms, the platform rearranges all parking and mobility spaces of cars, rails, and bicycles of a city to be directly comparable, making mobility space inequality accessible to a broad public. This crowdsourced method confirms a prevalent imbalance between modal share and space allocation in 23 cities worldwide, typically discriminating bicycles. Analyzing the guesses of the platform’s visitors about mobility space distributions, we find that this discrimination is consistently underestimated in the public opinion. Finally, we discuss a visualized scenario in which extensive parking areas are regained through fleets of shared, autonomous vehicles. We outline how such accessible visualization platforms can facilitate urban planners and policy makers to reclaim road and parking space for pushing forward sustainable transport solutions.

[1]  Tamitza Toroyan,et al.  Global status report on road safety , 2009, Injury Prevention.

[2]  D. Sperling,et al.  China's electric car surge , 2017 .

[3]  D. Shoup The High Cost of Free Parking , 1997 .

[4]  Henriette Cramer,et al.  Aesthetic capital: what makes london look beautiful, quiet, and happy? , 2014, CSCW.

[5]  Jacek Malczewski,et al.  Quality Evaluation of Volunteered Geographic Information: The Case of OpenStreetMap , 2017 .

[6]  M. Harris,et al.  Safe cycling: how do risk perceptions compare with observed risk? , 2012, Canadian journal of public health = Revue canadienne de sante publique.

[7]  Susan Shaheen,et al.  Growth in Worldwide Carsharing , 2007 .

[8]  Damon Honnery,et al.  Greening passenger transport: a review , 2013 .

[9]  M. Haklay How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets , 2010 .

[10]  Roger Tourangeau,et al.  Evaluating the Effectiveness of Visual Analog Scales : A Web Experiment , 2006 .

[11]  Carlo Ratti,et al.  From Parking Lot to Paradise. , 2017, Scientific American.

[12]  Peter D. Norton,et al.  Street Rivals: Jaywalking and the Invention of the Motor Age Street , 2007 .

[13]  Organización Mundial de la Salud World health statistics 2017: monitoring health for the SDGs, Sustainable Development Goals , 2018 .

[14]  M. Barthelemy,et al.  Modeling the polycentric transition of cities. , 2013, Physical review letters.

[15]  Andy S. Choi,et al.  Transport transitions in Copenhagen: Comparing the cost of cars and bicycles , 2015 .

[16]  Ralph Buehler,et al.  Safer Cycling Through Improved Infrastructure. , 2016, American journal of public health.

[17]  Vyron Antoniou,et al.  How Many Volunteers Does it Take to Map an Area Well? The Validity of Linus’ Law to Volunteered Geographic Information , 2010 .

[18]  Stefan Gössling,et al.  Urban transport justice , 2016 .

[19]  Matthew E. Kahn,et al.  Sprawl and Urban Growth , 2003 .

[20]  M. Batty The New Science of Cities , 2013 .

[21]  Peter A Cripton,et al.  Route infrastructure and the risk of injuries to bicyclists: a case-crossover study. , 2012, American journal of public health.

[22]  Gabriel E. Kreindler,et al.  Citywide Effects of High-Occupancy Vehicle Restrictions: Evidence from the Elimination of ‘3-in-1’ in Jakarta , 2016 .

[23]  P. Sturm,et al.  Calculating spatial urban sprawl indices using open data , 2017 .

[24]  Stephen R. Carpenter,et al.  Scenario Planning: a Tool for Conservation in an Uncertain World , 2003, Conservation Biology.

[25]  Marco Minghini,et al.  Using OpenStreetMap to Create Land Use and Land Cover Maps , 2017 .

[26]  Paolo Santi,et al.  Supporting Information for Quantifying the Benefits of Vehicle Pooling with Shareability Networks Data Set and Pre-processing , 2022 .

[27]  T. Litman Socially Optimal Transport Prices and Markets , 2015 .

[28]  P. Moriarty,et al.  The prospects for global green car mobility , 2008 .

[29]  Geoff Boeing,et al.  OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks , 2016, Comput. Environ. Urban Syst..

[30]  Dominique Meroux,et al.  Three Revolutions in Urban Transportation: How To Achieve the Full Potential of Vehicle Electrification, Automation, and Shared Mobility in Urban Transportation Systems Around the World by 2050 , 2017 .

[31]  J. Bates,et al.  Spaced Out: Perspectives on parking policy , 2012 .

[32]  Todd Litman,et al.  Autonomous Vehicle Implementation Predictions: Implications for Transport Planning , 2015 .

[33]  Global motorization, social ecology and China , 2007 .

[34]  Paolo Santi,et al.  Estimating Savings in Parking Demand Using Shared Vehicles for Home–Work Commuting , 2017, IEEE Transactions on Intelligent Transportation Systems.

[35]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[36]  M. Harris,et al.  Impact of Transportation Infrastructure on Bicycling Injuries and Crashes : a Review of the Literature , 2009 .

[37]  Kristian Skrede Gleditsch,et al.  Graphic Discovery: A Trout in the Milk and Other Visual Adventures , 2006 .

[38]  Emilio Frazzoli,et al.  Toward a Systematic Approach to the Design and Evaluation of Automated Mobility-on-Demand Systems: A Case Study in Singapore , 2014 .

[39]  A. Bauman,et al.  Health benefits of cycling: a systematic review , 2011, Scandinavian journal of medicine & science in sports.

[40]  Jana Reinhard Walkable City How Downtown Can Save America One Step At A Time , 2016 .

[41]  Hansi Senaratne,et al.  A review of volunteered geographic information quality assessment methods , 2017, Int. J. Geogr. Inf. Sci..

[42]  Edmund K. Burke,et al.  An effective heuristic for the two-dimensional irregular bin packing problem , 2013, Annals of Operations Research.

[43]  Rachel Botsman,et al.  What's Mine Is Yours: The Rise of Collaborative Consumption , 2010 .

[44]  Henner Gimpel,et al.  Understanding the Sharing Economy -- Drivers and Impediments for Participation in Peer-to-Peer Rental , 2016, 2016 49th Hawaii International Conference on System Sciences (HICSS).

[45]  M. Molina,et al.  Megacities and Atmospheric Pollution , 2004, Journal of the Air & Waste Management Association.

[46]  Ramesh Raskar,et al.  Computer vision uncovers predictors of physical urban change , 2017, Proceedings of the National Academy of Sciences.

[47]  Bernd Fusshoeller,et al.  Strategies Towards Meeting Future Particulate Matter Emission Requirements in Homogeneous Gasoline Direct Injection Engines , 2011 .

[48]  M. Strube,et al.  Citizen-Centric Urban Planning through Extracting Emotion Information from Twitter in an Interdisciplinary Space-Time-Linguistics Algorithm , 2016 .

[49]  Itf Urban Mobility System Upgrade: How shared self-driving cars could change city traffic , 2015 .

[50]  Mattias Höjer,et al.  How much transport can the climate stand?--Sweden on a sustainable path in 2050 , 2006 .

[51]  T. Goddard,et al.  Drivers' Attitudes and Behaviors Toward Bicyclists: Intermodal Interactions and Implications for Road Safety , 2017 .

[52]  D. Banister Unsustainable Transport: City Transport in the New Century , 2005 .

[53]  César A. Hidalgo,et al.  The Collaborative Image of The City: Mapping the Inequality of Urban Perception , 2013, PloS one.

[54]  Stefan Gössling,et al.  Urban Space Distribution and Sustainable Transport , 2016 .

[55]  Barry Hutton Planning Sustainable Transport , 2013 .

[56]  Tan Yigitcanlar,et al.  Measures of transport-related social exclusion: A critical review of the literature , 2016 .

[57]  Martin A. Andresen,et al.  Obesity relationships with community design, physical activity, and time spent in cars. , 2004, American journal of preventive medicine.

[58]  Adam Millard-Ball,et al.  Getting Around a License-Plate Ban: Behavioral Responses to Mexico City’s Driving Restriction , 2017 .

[59]  Emilio Frazzoli,et al.  Revisiting Street Intersections Using Slot-Based Systems , 2016, PloS one.

[60]  Ian A. Waitz,et al.  Air pollution and early deaths in the United States. Part I: Quantifying the impact of major sectors in 2005 , 2013 .

[61]  Marta C. González,et al.  Using Convolutional Networks and Satellite Imagery to Identify Patterns in Urban Environments at a Large Scale , 2017, KDD.

[62]  Mikhail Chester,et al.  Parking Infrastructure: A Constraint on or Opportunity for Urban Redevelopment? , 2016 .

[63]  Raúl Rojas,et al.  Adapting to the Traffic Swarm: Swarm Behaviour for Autonomous Cars , 2016 .

[64]  Yann Blumer,et al.  Anticipating transitions beyond the current mobility regimes: How acceptability matters , 2014 .