The Effects of Park and Ride Supply and Pricing on Public Transport Demand

9 Appendix A2: Analysis of stated intention data

9.1 The Stated Intention data was combined to denote the proportionate changes in demand after some particular increase in rail fare, car parking charge or likelihood of not getting a parking space. The modelling methodology then distinguished, to the extent that the sample size in any cell allowed, by ticket type (season or non-season for consistency with the ticket sales analysis), origin station, since the local parking conditions vary across these, and whether the journey was inter-urban or not. This process yield 246 demand changes for modelling purpose, and the same form of model was estimated, as set out in equation 3 (in appendix A1 above), for the change in demand.

9.2 The estimated model contains three primary variables. These are:

  • the rail fare, specified in constant elasticity form
  • the chance of not getting a parking space, specified in difference form (as with variable O in equation 3), since the chance of not getting a space is often zero in the base case
  • the parking charge at the station, which is also specified in difference form since it too can often be zero

9.3 The models are contained in Table A2.1. The demand parameters was then tested for the above three terms, depending on ticket type, whether the journey was inter-urban and whether the origin station had no local parking, ample free local parking or ample paid local parking. The only statistically significant effects we were able to discern were that the parking charge had a lesser impact (-0.0005+0.0002) when there was ample free local parking and that the rail fare elasticity varied by ticket type and distance.

9.4 The rail fare elasticities obtained from the Stated Intention data are clearly unreasonable. The base elasticity, for non-seasons and urban trips, exceeds 6! The elasticities for seasons and inter-urban travel would similarly be far too high. This is the potential problem of the Stated Intention approach. If respondents perceive that fares might be in line to increase, they have every incentive to state that they would no longer use train if the fares were increased. This clearly seems to have occurred here.

9.5 With regard to the chance of not finding a parking space, this demand parameter was invariant with respect to local parking conditions, ticket type and distance. Taking the exponential of the product of the demand parameter (-0.0022) and the change in the chance of not finding a parking space (10=20-10) indicates the proportionate change in demand after that change in the chance of finding a space.

9.6 Thus moving from a 0 to 20 percent chance of not finding a space would lead to a 4.3% reduction in rail demand. A change from 0 to 10 percent would reduce demand by 2.2%.

The calculations for the parking charge work in exactly the same fashion. If the parking charge is increased by £1 (100p) then demand would be reduced by 4.9%. This value would reduce to 3.0% if there is ample free local parking.

Table A2.1: Result of the demand models
Parameter Output
Rail Fare -6.3230 12.8)
+Season 1.9401 (3.4)
+Inter 2.2078 (3.9)
Chance -0.0022 (4.0)
Park Charge -0.0005 (7.5)
+AmpleFree 0.0002 (1.3)
Adj R2 0.46
Obs 246

Note: Adj R2 is for model with constant included. A % chance of not getting a parking space is specified as 10 whilst parking charge is specified in pence.