1. Examining Potential Demand of Public Transit for Commuting Trips Xiaobai Yao Department of Geography University of Georgia, USA 5 July 2006

2. Outline The trend of public transit in the US Objectives of the study Methodology Case study Conclusions

5. Renaissance of Public Transit in the US Traffic congestion Economic growth Gas price vs affordable transit fare Environment sustainability

6. Public transit networks in the city of Atlanta

7. Research on Public Transportation Accessibility for special groups Land use / transportation relationship Cost, benefit, pricing Network analysis …?

8. Research objectives of the study Measure the potential need of public transportation Identify and visualize clusters of high potential needs areas

9. Methodology Identify Predictive Factors Identifying and Visualizing Potential Demand Distribution –The Need Index approach –A data mining approach Case study

10. Data Land-use, socioeconomic, and transportation (trips by mode) data at TAZ level.

11. Identify Predictive Factors where R is the proportion of workers taking public transit as the primary mode, vi ’s are the identified independent variables, and k is the total number of these variables. Multiple Regression

12. Identify Predictive Factors - the Atlanta case Independent variables: Land-use characteristics –Population density - Average number of workers per HH –Employment rate - Job density –Percentage of home workers Socioeconomic characteristics –Income - Car ownership Network structure –Density of bus stops in the TAZ - Density of rail stations in TAZ

13. Predictive Variables (Unstandardized) CoefficientsSig.Collinearity Statistics BStd. Error ToleranceVIF (Constant) 1.334.824.106 Percentage of home workers.008.034.816.8641.157 Percentage of workers below poverty line (x 1 ).074.019.000.6291.589 Percentage of workers with income from 100% to 150% of poverty line (x 2 ).103.026.000.6791.474 Percentage of worker with 0 vehicle in the household (x 3 ).421.017.000.5101.961 Percentage of worker with 1 vehicle in the household (x 4 ).033.010.001.5521.812 Employment rate (x 5 ) -.045.014.001.5411.847 Average # of workers per household -.007.512.989.5511.816 Population Density (x 6 ).036.006.000.6321.583 Job Density (x 7 ) -.026.002.000.3362.974 Rail station Density.098.198.623.8321.201 Bus stop Density.080.006.000.2513.982 Regression Results

14. Identifying and Visualizing Potential Demand Distribution 1.The Need Index approach 2.A data mining approach – self-organizing maps

15. 1. The Need Index approach yi ’s: variables accounting for the network structure and level of service of transit systems xi ’s: variables that are not about the transit systems. R = NI + Net NI = R-Net

16. Need Index for the Atlanta Case

17. Critique on the Need-Index approach Simple calculation Easy interpretation Possible to rank and/or to quantify the difference Classification/Visu alization Dilemma (where are the magic breaks) The validity of linear relationship assumption

18. 2. The SDM approach : Self- organizing maps

19. Self-organizing maps: how it works

20. SOM in this study (weighted vector space )

21. 7 8 9 4 5 6 1 2 3

22. Visualizing the SOM patterns

24. Critiques on the SOM approach No assumption on the relationship Self-assigned clusters No quantitative measure No ranking

25. Conclusions The integrative approach is successful. The Need Index approach and the spatial data mining approach are complementary and mutually confirmative. Confirmed by the other approach, the Need Index approach provides an efficient and effective solution to transportation planners.