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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 Multiple Regression k R v i i i 1 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. Identify Predictive Factors - the Atlanta case Independent variables: • Land-use characteristics – – – • Population density Employment rate Percentage of home workers - Average number of workers per HH - Job density Socioeconomic characteristics – • Income - Car ownership Network structure – Density of bus stops in the TAZ - Density of rail stations in TAZ Regression Results Predictive Variables (Unstandardized) Coefficients B (Constant) Sig. Std. Error Collinearity Statistics Tolerance VIF 1.334 .824 .106 .008 .034 .816 .864 1.157 Percentage of workers below poverty line (x1) .074 .019 .000 .629 1.589 Percentage of workers with income from 100% to 150% of poverty line (x2) .103 .026 .000 .679 1.474 Percentage of worker with 0 vehicle in the household (x3) .421 .017 .000 .510 1.961 Percentage of worker with 1 vehicle in the household (x4) .033 .010 .001 .552 1.812 Employment rate (x5) -.045 .014 .001 .541 1.847 Average # of workers per household -.007 .512 .989 .551 1.816 .036 .006 .000 .632 1.583 -.026 .002 .000 .336 2.974 Rail station Density .098 .198 .623 .832 1.201 Bus stop Density .080 .006 .000 .251 3.982 Percentage of home workers Population Density (x6) Job Density (x7) Identifying and Visualizing Potential Demand Distribution 1. The Need Index approach 2. A data mining approach – self-organizing maps 1. The Need Index approach n R i 1 m i xi i yi i 1 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 NI ( i ) 0 . 074 x1 0 . 103 x 2 0 . 421 x 3 0 . 033 x 4 0 . 045 x 5 0 . 036 x 6 0 . 026 x 7 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 : Selforganizing maps <x1, x2, …. xn> Self-organizing maps: how it works N d j (y i ( t ) w ij ( t )) 2 j 1 w ij ( t 1) w ij ( t ) ( t )( x i ( t 1) w ij ( t )) SOM in this study (weighted vector space ) 1 x1 , 2 x 2 , ... n x n 1 x1 , 2 x 2 , ... n x n 7 8 9 4 5 6 1 2 3 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.