1. Class 16: Individual Mobility and Transportation Networks Prof. Albert-László Barabási Prof. Marta Gonzalez Network Science: Nobility 2015 Prof. Boleslaw Szymanski

2. 600 million passenger cars worldwide (roughly one car per eleven people). 250 million passenger registered cars in the US. 806 million cars and light trucks on the road in 2007. Worldwide Flights 4.8 billion passengers 2014 Average trip length per passenger 1,393 km Worldwide Ground Trips ~588 billion trips per year ~Average trip distance 25km Image: D. Brockmann (NWU)

4. (c) Nonhub connectors (green), provincial hubs (yellow), and connector hubs (brown) in the worldwide air transportation network

5. What can we learn from roads?

6. 6 Different Centrality Measures of Streets in Venice 1.Closeness, 2.Betweenness centrality, 3.Straightness, 4.Information Closeness. Closeness measures to what extent a certain node is near all the other nodes in a system along the shortest path, more formally the inverse of cumulative distance required to reach from that node to all other nodes. Betweenness centrality Betweenness centrality is equal to the number of shortest paths from all vertices to all others that pass through that node Straightness. Straightness centrality measures the efficiency in the communication between two nodes in a system that increases when there is less deviation of their shortest path from the virtual straight line. P. Crucitti et al. Centrality measures in spatial networks of urban streets. Phys. Rev. E, 73:036125, 2006.

7. Finding and evaluating community structure in networks, M. E. J. Newman and M. Girvan, Phys. Rev. E 69, 026113 (2004). Betweenness centrality is an indicator of a node's centrality in a network. It is equal to the number of shortest paths from all vertices to all others that pass through that node. 1. Calculate betweenness scores for all edges in the network. 2. Find the edge with the highest score and remove it from the network. 3. Recalculate betweenness for all remaining edges. 4. Repeat from step 2.

8. e ii, the fraction of all edges in the network that link vertices in community i to vertices in this community which represent the fraction of edges that connect vertices in community i to vertices in community j. (in random structures ) Famous Newman Modularity metric Q compares the number of in-community edges to the edges in a random graph with the same number of nodes.

9. 9 Betweeness Centrality of road in the City of Dresden S. Lammer, B. Gehlsen, and D. Helbing. Scaling laws in the spatial structure of urban road networks. Physica A, 363:89, 2006.

10. 10 (a) Location of commercial and service activities (red dots); (b) Kernel density estima- tion (KDE) (c) Street global betweenness (d) KDE of (b C ) Street vs. Betweenness and commercial activity E. Strano at al Env. And Plann. B: Planning and Design, 36:450 { 465, 2009.

12. Quantify? Validate? Applications?

13. 90% of US population own mobile phones (2014)

14. Mobile Phone Data (Basic Info)

15. Regularity of the most common location by time of the day (average over mobile phone users) Number of different locations visited by time of the day (average over mobile phone users) Mobile Phone Data (Basic Info) Song, Qu, Blumm, Barabasi, Science 327,108(2010)

16. Data in Two Metropolitan Areas 360,000 Mobile Phone Users 892 Towers 680,000 Mobile Phone Users 700 Census Tracts

17. Understanding Road Usage Patterns in Urban Areas With Records of Mobile Calls P. Wang

18. From phone Data to t-OD

19. GPS Probes A. Zoomed Neighborhood B. GPS reads (7.5 per sq. meter) C. GPS connections D. Estimated Travel Time: Mon 8am

20. Validation of Travel times with GPS probe vehicles α = 0.15, β =4 BPR function (Bureau of Public Roads function measures congestion on the link VOC = Volatile Organic Compounds PCC = Pesrson’s Correlation Coefficient

21. P(V)~ e^(-V/ν)  = 414  =259 [veh/hour] Distribution of Traffic Flow (Quantify) Traffic Flow V Pollution as Volatile Organic Compounds

22. 1.6% of sources produce 60% of volume in a road Fraction of Flow vs. Rank Road Usage Patterns based on Rank of Sources of Flow

23. Gini distribution Road Usage Patterns Using Gini Distribution Definition

24. Number of Major Driver Sources 90% of roads have less than 80 N MDS (Major Driver Sources) Road Usage Patterns Using Major Driver Sources

25. Comparison with other metrics

26. Gini is a new property of the streets

27. Mitigation of Congestion We can target few affected neighborhoods.

28. Mitigation of Congestion We find that when m=1%: Bay Area: δT reaches 26,210 minutes, corresponding to a 14% reduction of one hour morning commute (triangles in Fig. E). Boston Area: δT reaches 11,762 corresponding to 18% reduction of additional travel time during a one hour morning commute (diamonds in Fig. E)

29. Mitigation of Congestion The reason….

30. 1% in car usage reduction 16% reduction in travel time (vs. 3% obtained traditionally)

32. Why they are travelling? S. Jang & J. Ferreira (DUSP) S. Jang, J. Ferreira and M.C. Gonzalez "Clustering Temporal Patterns of Human Activities in the City", Data Mining and Knowledge Discovery 25.3 (2012): 478-510.

33. - City 2,695,598 - Rank 3rd US - Density 4,447.4/km2 Highly Populated US City: Chicago!

35. Chicago 1% representative sample by activity survey

36. Weekday: Temporal Activity Patterns

37. Weekday: Eigenactivities 1-3 37 Low probability of staying at home from 7:00 am till 5:00 pmHigh probability of working from 7:00 am till 5:00 pm Most components of each eigenactivity are close to the sample mean

38. Weekday: Clusters (K=8)

40. Spatio-temporal Patterns of Urban Human Mobility Measured from Subway Smart Cards

41. Aereal Image of London Source: google image

42. Smartcards from London

44. Structure of flows

53. C. Schneider (Postdoc) Motifs in Daily Mobility

54. Percentage of trips in daily routine (from Survey)

55. 40,000 active users with calls during the day at at least 8 time windows (time step 30min)

56. Perturbation based model

58. Summary Spatial Networks properties have several applications: Communities of airports, road characteristics. There is a hierarchical network in the way neighborhoods connect to different streets: “Street popularity index”. Individuals can be clustered by their daily travel activities. A preferential attachment model can describe that heterogeneity of fluxes. A perturbation based model can describe the Burts and motifs of daily trips.