Understanding congested travel in urban areas

Rapid urbanization and increasing demand for transportation burdens urban road infrastructures. The interplay of number of vehicles and available road capacity on their routes determines the level of congestion. Although approaches to modify demand and capacity exist, the possible limits of congestion alleviation by only modifying route choices have not been systematically studied. Here we couple the road networks of five diverse cities with the travel demand profiles in the morning peak hour obtained from billions of mobile phone traces to comprehensively analyse urban traffic. We present that a dimensionless ratio of the road supply to the travel demand explains the percentage of time lost in congestion. Finally, we examine congestion relief under a centralized routing scheme with varying levels of awareness of social good and quantify the benefits to show that moderate levels are enough to achieve significant collective travel time savings.

[1]  A. C. Pigou Economics of welfare , 1920 .

[2]  S. Stouffer Intervening opportunities: a theory relating mobility and distance , 1940 .

[3]  J. G. Wardrop,et al.  Some Theoretical Aspects of Road Traffic Research , 1952 .

[4]  J. Wardrop ROAD PAPER. SOME THEORETICAL ASPECTS OF ROAD TRAFFIC RESEARCH. , 1952 .

[5]  T. Koopmans,et al.  Studies in the Economics of Transportation. , 1956 .

[6]  J. Jacobs The Death and Life of Great American Cities , 1962 .

[7]  W. Vickrey Congestion Theory and Transport Investment , 1969 .

[8]  Larry J. LeBlanc,et al.  AN EFFICIENT APPROACH TO SOLVING THE ROAD NETWORK EQUILIBRIUM TRAFFIC ASSIGNMENT PROBLEM. IN: THE AUTOMOBILE , 1975 .

[9]  David Branston,et al.  LINK CAPACITY FUNCTIONS: A REVIEW , 1976 .

[10]  Carlos F. Daganzo,et al.  On Stochastic Models of Traffic Assignment , 1977 .

[11]  Mike J. Smith The marginal cost taxation of a transportation network , 1979 .

[12]  M. Fukushima A modified Frank-Wolfe algorithm for solving the traffic assignment problem , 1984 .

[13]  Heinz Spiess,et al.  Technical Note - Conical Volume-Delay Functions , 1990, Transp. Sci..

[14]  J. Huyck,et al.  Tacit Coordination Games, Strategic Uncertainty, and Coordination Failure , 1990 .

[15]  R Akcelik,et al.  Travel time functions for transport planning purposes: Davidson's function, its time dependent form and alternative travel time function , 1991 .

[16]  Bruce N Janson,et al.  Dynamic traffic assignment for urban road networks , 1991 .

[17]  E. Glaeser,et al.  Growth in Cities , 1991, Journal of Political Economy.

[18]  Ravindra K. Ahuja,et al.  Network Flows: Theory, Algorithms, and Applications , 1993 .

[19]  R. Jayakrishnan,et al.  A FASTER PATH-BASED ALGORITHM FOR TRAFFIC ASSIGNMENT , 1994 .

[20]  L. Shapley,et al.  Potential Games , 1994 .

[21]  L. Shapley,et al.  REGULAR ARTICLEPotential Games , 1996 .

[22]  W Reilly,et al.  HIGHWAY CAPACITY MANUAL 2000 , 1997 .

[23]  Tim Roughgarden,et al.  How bad is selfish routing? , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.

[24]  Tim Roughgarden,et al.  Bounding the inefficiency of equilibria in nonatomic congestion games , 2004, Games Econ. Behav..

[25]  Alain Bertaud The Spatial Organization of Cities: Deliberate Outcome or Unforeseen Consequence? , 2004 .

[26]  A. Nagurney,et al.  A retrospective on Beckmann, McGuire and Winsten's "Studies in the Economics of Transportation" , 2005 .

[27]  Tim Roughgarden,et al.  Selfish routing and the price of anarchy , 2005 .

[28]  Anna Nagurney,et al.  On a Paradox of Traffic Planning , 2005, Transp. Sci..

[29]  K Sneppen,et al.  Networks and cities: an information perspective. , 2005, Physical review letters.

[30]  Robert B. Dial,et al.  A path-based user-equilibrium traffic assignment algorithm that obviates path storage and enumeration , 2006 .

[31]  Dirk Helbing,et al.  Scaling laws in the spatial structure of urban road networks , 2006 .

[32]  T. Geisel,et al.  The scaling laws of human travel , 2006, Nature.

[33]  D. Helbing,et al.  Growth, innovation, scaling, and the pace of life in cities , 2007, Proceedings of the National Academy of Sciences.

[34]  Michael T. Gastner,et al.  Price of anarchy in transportation networks: efficiency and optimality control. , 2007, Physical review letters.

[35]  M. Batty The Size, Scale, and Shape of Cities , 2008, Science.

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

[37]  José R. Correa,et al.  A Geometric Approach to the Price of Anarchy in Nonatomic Congestion Games , 2008, Games Econ. Behav..

[38]  Yu Nie,et al.  A class of bush-based algorithms for the traffic assignment problem , 2009 .

[39]  Carlo G. Prato,et al.  Route choice modeling: past, present and future research directions , 2009 .

[40]  Zhen Qian,et al.  On Centroid Connectors in Static Traffic Assignment: Their Effects on Flow Patterns and How to Optimize , 2010 .

[41]  Chaoming Song,et al.  Modelling the scaling properties of human mobility , 2010, 1010.0436.

[42]  Marc Barthelemy,et al.  Spatial Networks , 2010, Encyclopedia of Social Network Analysis and Mining.

[43]  Albert-László Barabási,et al.  Limits of Predictability in Human Mobility , 2010, Science.

[44]  Xing Xie,et al.  Learning travel recommendations from user-generated GPS traces , 2011, TIST.

[45]  Marta C. González,et al.  A universal model for mobility and migration patterns , 2011, Nature.

[46]  Alexandre M. Bayen,et al.  Understanding Road Usage Patterns in Urban Areas , 2012, Scientific Reports.

[47]  Cecilia Mascolo,et al.  A Tale of Many Cities: Universal Patterns in Human Urban Mobility , 2011, PloS one.

[48]  Zbigniew Smoreda,et al.  Unravelling daily human mobility motifs , 2013, Journal of The Royal Society Interface.

[49]  L. Bettencourt,et al.  Supplementary Materials for The Origins of Scaling in Cities , 2013 .

[50]  César A. Hidalgo,et al.  Unique in the Crowd: The privacy bounds of human mobility , 2013, Scientific Reports.

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

[52]  A. Plastino,et al.  Space–time correlations in urban sprawl , 2013, Journal of The Royal Society Interface.

[53]  M. Barthelemy,et al.  How congestion shapes cities: from mobility patterns to scaling , 2014, Scientific Reports.

[54]  Wen-Xu Wang,et al.  Universal predictability of mobility patterns in cities , 2013, Journal of The Royal Society Interface.

[55]  Zoltán Toroczkai,et al.  Predicting commuter flows in spatial networks using a radiation model based on temporal ranges , 2014, Nature Communications.

[56]  Michael Patriksson,et al.  The Traffic Assignment Problem: Models and Methods , 2015 .

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

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

[59]  M. Batty,et al.  Constructing cities, deconstructing scaling laws , 2013, Journal of The Royal Society Interface.

[60]  Marta C. González,et al.  Analyzing Cell Phone Location Data for Urban Travel , 2015 .

[61]  Enrique Frías-Martínez,et al.  Uncovering the spatial structure of mobility networks , 2015, Nature Communications.

[62]  Vincent D. Blondel,et al.  A survey of results on mobile phone datasets analysis , 2015, EPJ Data Science.

[63]  Marc Barthelemy,et al.  The spatial organization of cities , 2016 .