Variables Used in Ridership Forecasts Sample Clauses

Variables Used in Ridership Forecasts. This section focuses on the common input variables used in transit ridership forecast. Accordingly, variables can be classified as 1) socioeconomics and demographics, 2) land use and accessibility measures, and 3) transit level of service characteristics, etc. Based on the methodology used, more variables could be taken into account. For instance route/stop level models also consider station attributes as an explanatory variable. Some of the most popular variables used in a variety of ridership forecast methods are summarized. The stop-level transit ridership models developed by Xxxxxxx and Xxxxxxxxx (2001) for the BRT system in Delaware, USA, included the following variables: household automobile ownership, number of households, average household income, bus stop condition, bus stop accessibility, commercial activities, and quality of transit service. Another stop–level ridership model in San Diego, California, was developed by Xxxx & Xxxxx (2009) considering income, no vehicle households, percentage of female population, percentage of Hispanic population , percentage of White population, and percentage of youth. Xxxxxx et al. (2011) explored how explanatory variables affected the ridership in different locations in their study. They examined data regarding population, income, gas prices, Tulsa Transit budget levels, and seasonality and considered them as potential variables to build their model for the bus transit system in Tulsa, Oklahoma. Two explanatory (service reliability) variables were used in Xxxxxxxxxxx (2015) study in Los Angeles, California. He considered average on-time performance and standard deviation of schedule deviation. The other independent variables (or controls) chosen for estimating regression models included stop neighborhood built environment, socioeconomic and demographic factors, and planned service quality of a line serving a stop (i.e., line population density, employment accessibility, stops per mile, line type, and headway). Aging Effects on Transit (AET) model was conducted in report NCHRP-86 (2006) to predict the usage of public transportation on the travel day as a binary variable, applicable for different counties within the U.S. Independent variables considered in this report all came from household surveys, including 65 and over age group, zero- vehicle household, workforce participant, public transit available within two miles, and different categories based on population. According to Xxxx and Xxxxxxxx (1995), there...
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