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Exploring Potential Demand for Public Transit in Urban Commuting: A Case Study in Atlanta, USA

This study examines the potential demand for public transit in urban commuting trips, focusing on factors such as traffic congestion, economic growth, gas prices, sustainability, and public transit networks in Atlanta. The research aims to measure the need for public transportation, identify high-demand areas, and visualize clusters. The methodology involves predictive factor identification, data mining, and multiple regression analysis. The study uses land-use, socioeconomic, and transportation data at the TAZ level, incorporating variables like population density, employment rate, network structure, and more. The Need Index and spatial data mining approaches are applied to visualize potential demand distribution and self-organizing maps to analyze patterns. The findings suggest that an integrative approach combining the Need Index and spatial data mining can offer efficient solutions for transportation planning, providing valuable insights for transit development.

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Exploring Potential Demand for Public Transit in Urban Commuting: A Case Study in Atlanta, USA

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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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