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

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

Public transit networks in the city of Atlanta

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

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

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

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

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

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

Predictive Variables (Unstandardized) CoefficientsSig.Collinearity Statistics BStd. Error ToleranceVIF (Constant) Percentage of home workers Percentage of workers below poverty line (x 1 ) Percentage of workers with income from 100% to 150% of poverty line (x 2 ) Percentage of worker with 0 vehicle in the household (x 3 ) Percentage of worker with 1 vehicle in the household (x 4 ) Employment rate (x 5 ) Average # of workers per household Population Density (x 6 ) Job Density (x 7 ) Rail station Density Bus stop Density Regression Results

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

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

Need Index for the Atlanta Case

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

2. The SDM approach : Self- organizing maps

Self-organizing maps: how it works

SOM in this study (weighted vector space )

Visualizing the SOM patterns

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

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.