by Jixuan Jiang, MA Urban Planning Candidate Instructor: Leo F. Estrada TA:Nic Jay Aulston, Madeline Brozen

 For transit dependent residents, who are 1) too young, 2) too old, or 3) cannot afford personal automobile, they need the convenient access to public transit for the purpose of commute.

 Planning Focus: the Demand and Supply Assessment of Public Transit Service  Case: City of Long Beach  Data Source: Census 2000, FactFinder, Long Beach Transit, Metro, OCTA, and FY10 City of Long Beach Ridership Checker Program.  Methods: Transit Dependency Index (TDI) as a Demand Analysis Tool), Walkable Distance Coverage as a Supply Analysis Tool.

Finding 1: 84% Area of City of Long Beach is covered within the walkable distance buffer zone of all existing public transit service. * Walkable Distance=10 mins * average walking speeds (which is 2.5 – 3 mph)= 0.25 mi

Correlation No_Car_PopPoverty Percent65+_Pec18-_Per No_Car_Pop Percentage Pearson Correlation ** **.080 Significance (Double) N Poverty Percentage Pearson Correlation.554 ** **.474 ** Significance (Double).000 N _Percentage Pearson Correlation ** ** ** Significance (Double) N _Percentage Pearson Correlation ** ** 1 Significance (Double) N233 **. Significant at.01 Level TDI =0.38*%[zero_car_hholds] *%[ 65+_yrs] *[< 15_yrs]}.

Beyond looking at the old, the young, the poor, and the no_car population, the index Transit Dependent is utilized to evaluate the transit needs of the area. TDI =w no_car %[zero_car_hholds] + w income %[ 65+_yrs] + w 15 [< 15_yrs]}. Considering the correlation among these variables, the adopted TDI calculation formula is adjusted with harmonic weights of the correlation to eliminate the potential overlap of variable set. The harmonic weight is defined as: Weight i =(1/correlation i )/(Σ1/correlation i )

Finding 2: TDI indicates the transit service demand inequality. The highest Transit Demand appears in downtown area. The TDI analysis could serve as a quantitative identification for the “hottest” public transit needs for the purpose of equality service planning.

By Adding XY Data (Longitude, Latitude) of each stop, we can see the stops serving as the supply of public transit. The size of points varies by Daily Ridership at a typical weekday service.

Finding 3: Buffering the stop shapefile with the 0.25mile walking distance, we see 74.48% area of City of Long Beach is covered by the walking distance supply buffer by using calculating the geometry. Adding the TDI map we discover the high rank TDI areas are all within the supply buffer.

Finding 3: Linking the ridership data (which is another source of potential demand information) with current service supply (stops and routes), we can use this supply information for the purpose of improving the current service by adjusting/adding at the place with high demand.