| description abstract | Accurate forecasts of public transportation ridership are key components to a transit agency’s management program. These forecasts are used to develop revenue projections that are then imputed into short-term and long-term maintenance, rehabilitation, and capital investment programming. The forecasts are complicated by the fact that many of the causes of the underlying variability, such as socioeconomic and land-use factors, are not constant over the network. Failure to account for these spatial processes will yield biased, inefficient, and inconsistent demand estimates. To overcome these challenges, the current paper presents a spatial Durbin model for analyzing the change in the New York City subway ridership between 2011 and 2016. The results indicate that subway stations experienced a greater increase in ridership over the study period when the stations served more train lines, were located in areas comprised of census tracts with a greater number of tax units (residential, commercial, etc.), or served lower mean household incomes. Furthermore, the subway stations located in areas surrounded by census tracts with more commercial property or higher median family income are also expected to have a greater increase in ridership. Lastly, ridership at a given station decreases due to an increase in ridership at neighboring stations. This may indicate that a change in ridership at a station is due, in part, to riders in a region changing which station they use instead of riders shifting from alternative modes of transportation. | |
