encourage riders to switch to heavy and light rail at those times when commuter rail is no longer in service. In addition, the decline in commuter rail ridership is likely caused by a number of riders switching to the automobile mode as the already high cost of commuter rail makes driving relatively more competitive with commuter rail than it is for users of other transit modes. It appears that as much as third of the decrease in commuter rail riders and of the increase in heavy rail and light rail riders could be diversions from commuter rail to heavy and light rail. In terms of annual fare revenue, projections from the travel demand model set show a gain of $134.6 million, or a 28.2 percent increase, compared to a gain of $123.2 million, or a 25.8 percent increase, from the spreadsheet model. The smaller estimates of ridership loss from the travel demand model set result in greater estimates of fare revenue gains given the size of the proposed fare increase in Scenario 1. The greater ridership losses estimated by the spreadsheet model are correlated with smaller estimated fare revenue gains. When comparing modes, these differences are most apparent for heavy and light rail. However, as was the case in the ridership projections, it appears that this difference, as well as that for commuter rail fare revenue, can be at least partly explained by the diversion of commuter rail riders to heavy rail and light rail apparently projected by the travel demand model set. Overall, this diversion would represent a loss in fare revenue, as riders switch from the higher-priced mode to modes. Taken together, the projections shown in these tables
encourage riders to switch to heavy and light rail at those times when commuter rail is no longer in service. In addition, the decline in commuter rail ridership is likely caused by a number of riders switching to the automobile mode as the already high cost of commuter rail makes driving relatively more competitive with commuter rail than it is for users of other transit modes. It appears that as much as of the decrease in commuter rail riders and of the increase in heavy rail and light rail riders could be diversions from commuter rail to heavy and light rail. In terms of annual fare revenue, projections from the travel demand model set show a gain of $134.6 million, or a 28.2 percent increase, compared to a gain of $123.2 million, or a 25.8 percent increase, from the spreadsheet model. The smaller estimates of ridership loss from the travel demand model set result in greater estimates of fare revenue gains given the size of the proposed fare increase in Scenario 1. The greater ridership losses estimated by the spreadsheet model are correlated with smaller estimated fare revenue gains. When comparing modes, these differences are most apparent for heavy and light rail. However, as was the case in the ridership projections, it appears that this difference, as well as that for commuter rail fare revenue, can be at least partly explained by the diversion of commuter rail riders to heavy rail and light rail apparently projected by the travel demand model set. Overall, this diversion would represent a loss in fare revenue, as riders switch from the higher-priced mode to modes. Taken together, the projections shown in these tables third
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