1. Examining Potential Demand For Public Transit --A Case Study of City of Long Beach Presented to UP206, Dec 2010 By Jixuan Jiang, Master in UPlanning

3. Part I. A Review of Mid-Term Finding  Finding 1: Supply  Service Area: 84% Coverage;  Finding 2: Demand (TDI)  Inequality;  Highest Transit Dependency appears in downtown area;  Finding 3: Supply meets Demand  High Transit Dependency areas fully covered by 0.25 mi walkable service area buffer.

4. Part II. Objective Statement  Based on the Mid-Term Study, this study continue to investigate the potential demand for public transit for commuting trips;  Potential demand for public transit: Hotzone Analysis  Generate Regression based on the Hotzone Factors to forecast Ridership  Test the Statistical Power of Regression

5. Part III. Methodology 1. Data Processing 2. Identify Contributing Factors 3. Examine the Magnitude and Spatial Distribution 4. Regression Model 5. Qualitative Test the Significance of Regression Model

8. Part III. Methodology 1. Data Processing  GIS Modeling  Metadata  Measurement  Originate Data

11. Part III. Methodology 2. Metadata  GIS Modeling  Metadata  Measurement  Originate Data

15. Part IV. Case Study

16. Part IV. Case Study Calculate Distance

17. These Outliers are classified as “Long Distance, High Ridership”. Take a closer look into the map, we discover these census tracks are in the port area, which is defined as “work-demand bus stop”

18. Part IV. Case Study Identify the Hotzone Factor: Std. Err.

19. Regression Results for the prediction of Ridership a Model Unstandardized Coefficients Standardized Coefficients tSig. BStd. ErrorBeta 1(Constant).994.034.586.011 %15MinCom-3.215.060-.198-1.294.004 %PeakComm uter -3.451.036-.206-1.900.001 PopDensity-.082.001-.120-1.331.004 %Senior+You ng.811.044.043.369.014 %NoCarPop3.804.040.3301.920.001 %Poverty1.333.025.1621.059.006 %Working3.130.405.2471.546.002 Area.298.008.116.771.009 Distance.000 -.061-.398.014 Part IV. Case Study Identify the Hotzone Factor: Std. Err. Model Summary ModelRR Square Adjusted R Square Std. Error of the Estimate 1.387 a.150.0841.4826260

20.  HotZone=∑ weight of factor * Reclassification of Factor  Weight of Factor = Beta Value of Regression Part III. Methodology 4. Originate Data

22. Part IV. Case Study Regression Model  Anticipated 08 Ridership= -3.45 * x1+ - 0.081* x2 + 0.811 * x3 + 3.80* x4 + 1.33 * x5  X1= %PeakCommuter  X2 = PopDensity  X3= %Senior+Young  X4= %NoCarPop  X5= %Poverty

24. Part IV. Case Study Qualification Regression Model  To test the statistical explanation power of the regression model, MAP 13 shows the gap between the real 2008 ridership and the anticipated 2008 ridership with the regression model.  This study indicates that the downtown area ridership is highly under-estimated (High Positive Gap).  The 3D modeling further reveals that the high TDI areas are more likely under-estimated for further ridership. For the transit agency, this information could be used to further improve the area with existing high public transit demands.

25. Part IV. Case Study Qualification Regression Model

27. Conclusion  As a further exploration, the analysis quantitatively depicts a spatial inequality distribution of public transit service in city of Long Beach.  Focusing on the demographical and transit ridership information, this study reveals that there existing a potential insufficient public transit service associated with a high level of transit dependent in the downtown. The spatial mismatch requires transit service improvement.  By simulation and analyzing the transit service condition with a multiple regression, we can quantify the further ridership demand, serving as a handful tool for the public transit agency to change their current service to meet these potential needs.  A statistical significance test is conducted to test the regression model and reveals a problem of under-estimation.

28. Thank You. Question?