Predicting traffic volumes and estimating the effects of shocks in massive transportation systems

Proceedings of the National Academy of Sciences of the United States of America

Volumn 112, Issue 18, 2015, Pages 5643-5648

  Silva, Ricardo ^a   Kang, Soong Moon ^a   Airoldi, Edoardo M ^b  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b HARVARD UNIVERSITY   (United States)

Author keywords

Complex systems; Regime change; Smart cities; Transportation

Indexed keywords

ARTICLE; COMPARATIVE STUDY; HUMAN; METHODOLOGY; OUTCOME VARIABLE; PREDICTION; PRIORITY JOURNAL; SHOCK; SMART CARD; STATISTICAL ANALYSIS; TRAFFIC; UNITED KINGDOM; ALGORITHM; AUDIOVISUAL EQUIPMENT; CITY; CITY PLANNING; ENVIRONMENTAL PLANNING; FORECASTING; MOTOR VEHICLE; PREVENTION AND CONTROL; PROCEDURES; STATISTICS AND NUMERICAL DATA; THEORETICAL MODEL; TRAFFIC ACCIDENT; TRAFFIC AND TRANSPORT; TRENDS;

ACCIDENTS, TRAFFIC; ALGORITHMS; CITIES; CITY PLANNING; ENVIRONMENT DESIGN; FORECASTING; HUMANS; LONDON; MAPS AS TOPIC; MODELS, THEORETICAL; MOTOR VEHICLES; TRANSPORTATION;

EID: 84928902060     PISSN: 00278424     EISSN: 10916490     Source Type: Journal    
DOI: 10.1073/pnas.1412908112     Document Type: Article

References (28)

1. Banavar JR, Maritan A, Rinaldo A (1999) Size and form in efficient transportation networks. Nature 399:130-132.
2. Boelter LMK, Branch MC (1960) Urban planning, transportation and system analysis. Proc Natl Acad Sci USA 46(6):824-831.
3. Carey M, Kwieciński A (1994) Stochastic approximation to the effects of headways on knock-on delays of trains. Transportation Resarch B 28B(4):251-267.
4. Eagle N, Pentland A, Lazer D (2009) Inferring friendship network structure by using mobile phone data. Proc Natl Acad Sci USA 106(36):15274-15278.
5. Guimerà R, Mossa S, Turtschi A, Amaral LAN (2004) The worldwide air transportation network: Anomalous centrality, community structure, and cities' global roles. Proc Natl Acad Sci USA 102(22):7794-7799.
6. Colizza V, Barrat A, Bartheĺemy M, Vespignani A (2005) The role of the airline transportation network in the prediction and predictability of global epidemics. Proc Natl Acad Sci USA 103(7):2015-2020.
7. Newman M, Barabási A-L, Watts DJ, eds (2006) The Structure and Dynamics of Networks (Princeton Univ Press, Princeton).
8. Christakis NA, Fowler JH (2013) Social contagion theory: Examining dynamic social networks and human behavior. Stat Med 32(4):556-577.
9. Onnela J-P, et al. (2007) Structure and tie strengths in mobile communication networks. Proc Natl Acad Sci USA 104(18):7332-7336.
10. Dodds PS, Muhamad R, Watts DJ (2003) An experimental study of search in global social networks. Science 301(5634):827-829.
11. Brockmann D, Hufnagel L, Geisel T (2006) The scaling laws of human travel. Nature 439(7075):462-465.
12. Rand DG, Arbesman S, Christakis NA (2011) Dynamic social networks promote cooperation in experiments with humans. Proc Natl Acad Sci USA 108(48):19193-19198.
13. González MC, Hidalgo CA, Barabási A-L (2008) Understanding individual human mobility patterns. Nature 453(5):779-782.
14. Wang P, Gonzalez MC, Hidalgo CA, Barabási A-L (2009) Understanding the spreading patterns of mobile phone viruses. Science 324(5930):1071-1076.
15. Simini F, Gonzalez MC, Maritan A, Barabási A-L (2012) A universal model for mobility and migration patterns. Nature 484(7392):96-100.
16. Shirado H, Fu F, Fowler JH, Christakis NA (2013) Quality versus quantity of social ties in experimental cooperative networks. Nat Commun 4:2814.
17. Roth C, Kang SM, Batty M, Barthélemy M (2011) Structure of urban movements: Polycentric activity and entangled hierarchical flows. PLoS ONE 6(1):e15923.
18. Transport for London (2012) TfL Factsheet. Available at https://www.tfl.gov.uk/cdn/static/cms/documents/tfl-factsheet.pdf. Accessed June 16, 2014.
19. Vardi Y (1996) Network tomography:Estimating source-destination traffic intensities from link data. J Am Stat Assoc 91:365-377.
20. Transport for London (2014) Rolling Origin and Destination Survey: The Complete Guide, 2003. Revised October 2010, March 2012, and January 2014 (London Underground Limited, UK).
21. Tebaldi C, West M (1998) Bayesian inference on network traffic using link count data. J Am Stat Assoc 93(442):557-573.
22. Cao J, Davis D, Van Der Viel S, Yu B (2000) Time-varying network tomography: router link data. J Am Stat Assoc 95:1063-1075.
23. Airoldi EM, Faloutsos C (2004) Recovering latent time-series from their observed sums: Network tomography with particle filters. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, New York), pp 30-39.
24. Airoldi EM, Blocker AW (2013) Estimating latent processes on a network from indirect measurements. J Am Stat Assoc 108(501):149-164.
25. Pearl J (2000) Causality: Models, Reasoning and Inference (Cambridge Univ Press, Cambridge, UK).
26. Imbens GW, Rubin DB (2015) Causal Inference in Statistics, Social, and Biomedical Sciences: An Introduction (Cambridge Univ Press, Cambridge, UK).
27. Dunning T (2012) Natural Experiments in the Social Sciences (Cambridge Univ Press, Cambridge, UK).
28. Morgan SL, Winship C (2014) Counterfactuals and Causal Inference: Methods and Principles for Social Research (Cambridge Univ Press, Cambridge, UK).