Taxis Strike Back: A Field Trial of the Driver Guidance System

Shih-Fen Cheng, Shashi Shekhar Jha, Rishikeshan Rajendram

Abstract: Traditional taxi fleet operators world-over have been facing intense competitions from various ride-hailing services such as Uber and Grab (specific to the Southeast Asia region). Based on our studies on the taxi industry in Singapore, we see that the emergence of Uber and Grab in the ride-hailing market has greatly impacted the taxi industry: the average daily taxi ridership for the past two years has been falling continuously, by close to 20% in total. In this work, we discuss how efficient real-time data analytics and large-scale multi-agent optimization technology could potentially help taxi drivers compete against more technologically advanced service platforms. Our technology is based on an earlier theoretical work proven to work in a series of simulation studies. Our major contribution in this paper is the demonstration that the proposed design, when coupled with a real-time data feed of close to 20,000 taxis around Singapore, can indeed help drivers to improve their performances. To provide concrete real-world evidence that such technology can indeed benefit taxi drivers, we have tested the driver guidance system (DGS) operationally since September 2017. With 361 recruited drivers and 5 months of operational data, we have demonstrated that when drivers actively follow our guidance during their roaming (more than 60% of roaming time before acquiring a trip), their expected roaming times can be reduced by 22% when compared to the cases where guidances are not followed. By further breaking down the analysis by time periods, workdays, and areas, we point out the spatial-temporal combinations in which the DGS is most useful.