1. Transportation Networks September 9, 2014 Michael Lin Alex Farrell Ziqi Zhu Sanjeev Ramachadra

2. Sample Path in Where’s George Network

3. Where’s George Node Map

4. Where’s George Vertex Map

5. Creating the Network When entering bills, the information included is: Serial number Current zip code User comment (optional) Timestamp (automatically added by Where’s George) Information calculated for members from this information includes: Distance, total and between nodes Time, total and between nodes Overall average speed Distance from start to last recorded location Zip of each node Timestamp of each node Pros: Free and easy! Cons: Questionable veracity, requires outside help to thrive

6. Urban Road Network Representations “Primary” representation: Intersection = node Street = edge “Dual” representation / line graph: Intersection = edge Street = node Dual graph

7. Urban Road Networks Data Dual representation / Line graph Primal graph / primary representation S. Lammer et al (2006)

8. Degree Distribution of the Dual Network Log(k) Log(P(k)) Jiang (2007) http://www.mrc-mbu.cam.ac.uk/slidepage/119

9. Clustering Coefficient as a function of Degree in Dual Representation C(k) for four cities, 1mi^2 each S. Porta et al (2006)

10. Low degree streets, high Clustering coefficient High degree streets, low Clustering coefficient

11. Betweeness Centrality in the Primary Representation S. Lammer et al (2006)

12. Conclusions Taken together, the approximately power law distributions of degree and betweeness centrality, and the dependence of clustering coefficient on k suggest that urban road networks have a hierarchical structure Main roads branch out into less critical, less traversed roads, which again branch out, and so on