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Introduc4on  

1Ins.tue  of  Transporta.on  Studies,  University  of  California,  Irvine  ,  CA,  USA.  2Dept.  of  Civil    Eng.,  University  of  California,  Irvine  ,  CA,  USA  

Thuy  T.B.  Luong  1  ,  Will  Recker  2  

 

ANALYSING  HOW  SOCIO-­‐DEMOGRAPHIC  CHARACTERISTICS  ASSOCIATE  PUBLIC  TRANSIT  USAGE      A  COMPARISON  BETWEEN  SOUTHERN  AND  NORTHERN  CALIFORNIA      

94th  Annual  Mee.ng  of  Transporta.on  Research  Board,  January  2015,  Washington,  D.C.,  USA.    

•  The National Household Travel Survey, 2009

•  Select by Metropolitan Statistical Areas (CBSA) Codes •  South: 2446 individuals from Los Angeles-Long Beach-

Santa Ana, San Diego-Carlsbad-San Marcos, Riverside-San Bernardino-Ontario

• North: 1605 individuals from San Jose-Sunnyvale-Santa Clara, Sacramento--Arden-Arcade—Roseville, San Francisco-Oakland-Fremont

Data  

• California needs transit systems that are smart and diverse to make them more viable and effective for commutes.

• Knowledge about California transit riders’ characteristics and how they affect transit usage still is lacking.

•  Investigates the differences in transit usage between people in Southern and Northern California.

•  Transit riders’ socio-demographic and travel attitudes are accounted for in the comparison between the regions.

Source:  Google  Map  2014,  US  Census  Bureau  

Most of the determining socio-demographic variables - race, education, employment status, age, household income – are significantly different between the Northern and the Southern transit riders, while gender showed no significant difference (chi square test).

Results   A series of multivariate regressions were performed to identify the characteristics of those in each region who prefer to take transit. The results show that the socio-demographic factors associated with frequency of taking transit that vary between the regions.

Predictors   South   North  Model  2  

y  =  PTUSED  Model  1  

y  =  log(PTUSED)  Model  3    

y  =  PTUSED  Model  4  

y  =  log(PTUSED)  

(Intercept)   16.473  ***   1.837  ***   14.761  ***   1.568  ***  African  American,  Black   -­‐0.400   0.114   0.095  Asian  Only   0.999   0.248  *   -­‐0.058  Other  race   -­‐1.247   -­‐0.037   3.313  Hispanic/Mexican   3.200  **   0.237  **   -­‐3.828  Number  of  drivers   1.602  *   0.169  **   1.287  $25,000  -­‐  $44,999   -­‐2.476  *   -­‐0.248  **   -­‐1.249  $45,000  -­‐  $64,999   -­‐3.215  **   -­‐0.436  ***   0.255  $65,000  -­‐  $79,999   -­‐3.609  **   -­‐0.436  ***   -­‐0.962  $80,000  -­‐  $99,999   -­‐3.712  **   -­‐0.403  ***   -­‐1.865  more  than  $100,000   -­‐4.218  ***   -­‐0.343  ***   -­‐1.034  HH  size   -­‐0.616  *   -­‐0.065  **   -­‐1.347  **   -­‐0.127  ***  Number  of  vehicles   -­‐2.597  ***   -­‐0.236  ***   -­‐2.399  ***   -­‐0.209  ***  Number  of  adults   1.345   0.084   1.313   0.261  ***  Number  of  workers   -­‐0.085   0.245  Foreign  born   -­‐0.759   2.048  *   0.152  *  Has  driver  status   -­‐0.805   0.234  *   2.578   0.264  Some  college  or  Voca4onal   0.456   0.044   1.399   0.264  **  Bachelor   -­‐1.440   -­‐0.127   1.227   0.170  Graduate  or  Professional   -­‐1.949   -­‐0.112   1.099   0.197  *  Access  to/availability  of  public  transit   6.203  ***   0.508  ***   6.405  ***   0.684  ***  Lack  of  walkways  or  sidewalks   2.715   0.041   0.538   -­‐0.157  Price  of  travel   0.205   0.041   0.946   0.219  **  Aggressive  /  distracted  drivers   0.178   -­‐0.087   0.701   0.082  Safety  concerns   0.075   0.006   -­‐0.132   0.026  AGE   -­‐0.040   -­‐0.006  ***   -­‐0.105  ***   -­‐0.007  ***  Male   -­‐0.659   -­‐0.112  *   -­‐1.192   -­‐0.174  **  Not  a  worker   -­‐3.193  ***   -­‐0.203  ***   -­‐4.113  ***   -­‐0.327  ***  

DF   2418   2410   1577   1578  p-­‐value   <2.2e-­‐16   <2.2e-­‐16   <2.2e-­‐16   <2.2e-­‐16  Adjusted  R-­‐squared   0.104   0.147   0.101   0.142  

signif.codes:  '***'  0.001  '**'  0.01  '*'  0.05    

•  A significant association between region and views on public transit.

•  The models were able to distinguish between respondents

who did and did not take transit and between regions.

•  Major differences between regions in trends in propensity to use public transit related primarily to race, ethnicity, and income.

Conclusion  

Income  

Race   Educa.on  

Rela.onship  with  other  factors  

AXtudes    

Clustering  family  characteris.cs  

North  

South