Price Elasticity Literature Review

Literature Review

Price Elasticity as a Transit Fare Pricing Strategy

Over the past fifty years, public transportation systems have been overcrowded during peak hours to the extent that commuters occasionally cannot board and large delays are incurred due to waiting time. Transit agencies approach the peak hour shortage with two different strategies: increase peak supply and shift peak demands to off-peak. Increasing peak supply, however, can be sometimes prohibitively expensive and often inadequate as it invites more off-peak commuters to peak hours; consequently, many transit operators pursue transportation demand management (TDM) strategies, such as implementing various pricing policies, to reduce peak hour demand (Pluntke and Prabhakar,

That is, the computation of transit elasticity without considering factors such as type, distance travelled, and mode of the trip, does not yield a conclusive analysis; instead, it occasionally produces misleading conclusions. Accordingly, Litman (2004) published an influential paper on transit price elasticity, comprehending impacts of various factors on transit elasticity. Among nine leading factors is the time-of-day. The compilation of studies consistently shows that peak hour commuters react to peak surcharges less significantly than off-peak commuters reacting to reduction in fare discounts, It implies that peak pricing, the differential fare pricing for peak and off-peak trips, induces commuters to travel at less congested time, but at different level depending on whether it is peak surcharge or fare

The project called INSINC, was launched in January 2012 as a six-month pilot program, and later extended for further 18 months. INSINC utilized the same INSTANT mechanism, awarding one credit for every weekday trip and three credits for every off-peak shoulder hour trips, defined by 6:30 A.M. – 7:30 A.M and 8:30 A.M – 9:30 A.M. Unlike the Bangalore case, the participants of INSINC were able to monitor their progress using the Magic Box, and were given extra credits for accomplishing the goals set for the succeeding weeks and inviting their friends to participate. The total 20,319 commuters were initially admitted to participate, and 12,163 were referred to join the experiment by the existing participants. The total of S$137,639 was paid out as rewards, at the rate of S$0.30 per commuters per week. The before-and-after comparisons illustrate important findings. The percentage of peak trips decreased from over 10% to 7.49%. However, it took participants four weeks on average to start adjusting to off-peak hours, and those who visit the website and monitor their progress more often illustrated more significant changes. The peak-shift percentage was 7.46% in the month before the Magic Box was introduced, whereas it increased to 9.34% in the month after the system was introduced. The before-and-after result does not show as significant shifts as it did in the INSTANT project, possibly due to