The impact of ride-hail surge factors on taxi bookings

• Main Authors: AGARWAL, Sumit, CHAROENWONG, Ben, CHENG, Shih-Fen, KEPPO, Jussi
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Ride-hailing surge pricing; Taxi demand; Cross-price elasticity of taxi bookings; Taxi demand prediction; Asian Studies; Computer Sciences; Operations Research, Systems Engineering and Industrial Engineering; Transportation
• Online Access: https://ink.library.smu.edu.sg/sis_research/6955; https://ink.library.smu.edu.sg/context/sis_research/article/7958/viewcontent/Ride_Hail_Surge_Factors_av.pdf
• Institution: Singapore Management University

We study the role of ride-hailing surge factors on the allocative efficiency of taxis by combining a reduced-form estimation with structural analyses using machine-learning-based demand predictions. Where other research study the effect of entry on incumbent taxis, we use higher frequency granular data to study how location-time-specific surge factors affect taxi bookings to bound the effect of customer decisions while accounting for various confounding variables. We find that even in a unique market like Singapore, where incumbent taxi companies have app-based booking systems similar to those from ride-hailing companies like Uber, the estimated upper bound on the cross-platform substitution between ride-hailing services and taxi bookings is only 0.26. On the other hand, we show that incorporating surge price factor improves the precision of demand prediction by 12% to 15%. Our structural analyses based on a driver guidance system finds this improved accuracy in demand prediction reduces drivers’ vacant roaming times by 9.4% and increases the average number of trips per taxi by 2.6%, suggesting the price information is valuable across platforms, even if elasticities are low.