How much traffic do 121 micro unit infill apartments with no parking generate?
Projects like Blagden Alley in Washington, D.C. defy efforts to accurately estimate traffic generation…Until now. A new web application, MainStreet (powered by MXD+) builds on conventional ITE methods, allowing for a more accurate assessment.
How? As shown below, observed household survey data in the D.C. region indicates that households with low vehicle ownership generate significantly fewer auto trips than those with higher vehicle ownership.
Conventional methods for traffic generation are not sensitive to levels of vehicle ownership or other important variables influencing travel behavior (i.e. what is the context surrounding the project site?). Conventional methods are sensitive to a limited number of these variables. For that reason, they tend to over predict the amount of traffic generation, especially for unique sites like Blagden Alley. MainStreet on the other hand, does account for these variables. MainStreet leverages the geographic location of the project site to auto-populate the variables that influence travel behavior from available data sources. These variables are then used to estimate the travel behavior of the project site.
Blagden Alley is located in an urban environment with excellent transit accessibility via Metro and local bus, nearby retail and office employment, and limited parking availability. MainStreet’s full accounting method accounts for these influencing variables when analyzing Blagden Alley. MainStreet’s sensitivity to these variables means a more accurate assessment. For Blagden Alley, that means an estimate of less than half of the trips predicted by conventional methods.
Higher accuracy means more environmental sensitivity, fewer unnecessary mitigations, and more right-sized developments. To learn more about MainStreet, click here.