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Examining Potential Demand of Public Transit for Commuting Trips

Examining Potential Demand of Public Transit for Commuting Trips. Xiaobai Yao Department of Geography University of Georgia, USA 5 July 2006. Outline. The trend of public transit in the US Objectives of the study Methodology Case study Conclusions.

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Examining Potential Demand of Public Transit for Commuting Trips

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  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

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

  4. Public transit networks in the city of Atlanta

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

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

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

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

  9. Identify Predictive Factors Multiple Regression 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.

  10. 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

  11. Regression Results

  12. Identifying and Visualizing Potential Demand Distribution • The Need Index approach • A data mining approach – self-organizing maps

  13. 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

  14. Need Index for the Atlanta Case

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

  16. 2. The SDM approach : Self-organizing maps <x1, x2, …. xn>

  17. Self-organizing maps: how it works

  18. SOM in this study(weighted vector space )

  19. Visualizing the SOM patterns

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

  21. 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.

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