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46 Guidelines for Providing Access to Public Transportation Stations Exhibit 5-2. Work trip rail mode share by distance from residential sites to station. Source: TCRP Report 95 (12, 14 ) The research shows that there are many factors other than distance that affect the decision on whether to walk, including urban design, pedestrian facilities, crime, and individual charac- teristics. By considering these factors, agencies have the potential to increase walking mode share to stations. Station Access Model A station access model was developed based on land use and ridership information assem- bled from public transport systems across the United States, including heavy rail and com- muter systems serving strong downtown areas (New Jersey Transit, New York Metro-North, Washington, D.C., Boston, and San Francisco) as well as light rail systems in smaller cities (Denver and Portland). In total, data for over 450 stations were used in development of the station access tool. The planning model can be accessed on the accompanying CD or downloaded from Exhibit 5-3 gives the linear regression ridership models (equations) for estimating station ridership for each of the auto, bicycle, walking, and transit access modes. These equations can help quantify the likely ridership at new stations along an existing line or future growth at existing stations. The table also gives the relevant input variables used for each mode, linear regression coefficients, and statistical measures-of-fit. Note that the presence of heavy rail public transport acts essentially as a surrogate for CBD employment. In general, Exhibit 5-3 shows relatively high R-squared coefficients (greater than 0.7) for each of the access modes with the exception of feeder bus service. This is likely the result of a lack of data

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Travel Demand Considerations 47 Exhibit 5-3. Station ridership estimation model. Coefficient t Significance 2 Auto Ridership Model (R = 0.821) Constant 133.597 2.290 .023 Heavy rail dummy variablea 782.449 13.319 .000 Car parking spaces 1.282 35.452 .000 Percent zero-car households -347.494 -1.900 .058 2 Bicycle Ridership Model (R = 0.771) Constant -102.015 -6.594 .000 Jobs within mile -0.001 -.864 .389 Population within mile 0.008 4.446 .000 Bicycle parking spaces 1.032 6.980 .000 Bicycle commute mode share 3,241.579 5.730 .000 Percent zero-car households within 249.852 3.164 .002 mile Walk Ridership Model (R2 = 0.717) Constant -456.090 -3.665 .000 Heavy rail dummy variablea 1,444.994 8.069 .000 Jobs within mile 0.015 1.598 .111 Workers within mile 0.481 5.370 .000 Workers who walked to work within 2.390 8.639 .000 mile Feeder Transit Ridership Model (R2 = 0.373) Constant -261.387 -1.733 .084 a Heavy rail dummy variable 520.732 4.868 .000 Connecting transit lines 62.799 9.687 .000 Workers within -mile .019 1.554 .121 Parking utilization at station 211.484 1.661 .098 a Heavy rail = 1; other = 0 collected on available feeder transit due to limited resources and lack of a centralized data source. Only the total number of routes serving a given station was collected, meaning that information on the overall quality of the service at a given station (e.g., reliability, frequency, service coverage) was unavailable for incorporation into the modeling. The results of the modeling effort are consistent with the literature review findings: population density, employment density, and available parking are the most important factors determining station access decisions. Additional notes on each of the modal models include: Automobile--As expected, the number of available parking spaces was the primary determinant of auto access. The percentage of zero-car households was also found to be significant, with auto access to transit decreasing in areas with lower auto ownership. Bicycle--Bicycle access to transit increased in areas with higher population density and also those with lower auto ownership. Bike access was also higher in areas where more people travel by bike in general (measured by bicycle commute mode share) indicating the overall