A driver guidance system for taxis in Singapore

• Main Authors: JHA, Shashi Shekhar, CHENG, Shih-Fen, LOWALEKAR, Meghna, WONG, Nicholas, RAJENDRAM, Rishikeshan, VARAKANTHAM, Pradeep, TROUNG, Nghia Troung, BIN ABD RAHMAN, Firmansyah
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: mobility-on-demand; multiagent optimization; taxi driver guidance; Databases and Information Systems; Transportation
• Online Access: https://ink.library.smu.edu.sg/sis_research/4118; https://ink.library.smu.edu.sg/context/sis_research/article/5121/viewcontent/dgs_aamas18_demo.pdf
• Institution: Singapore Management University

Traditional taxi fleet operators world-over have been facing intense competitions from various ride-hailing services such as Uber and Grab.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 multiagent optimization technology could help taxi drivers compete against more technologically advanced service platforms. Our system has been in field trial with close to 400 drivers, and our initial results show that by following our recommendations, drivers on average save 21.5% on roaming time.