Conference Proceeding Article
To enhance fleet operation and management, logistics companies instrument their vehicles with GPS receivers and network connectivity to servers. Mobility traces from such large fleets provide significant information on commuter travel patterns, traffic congestion and road anomalies, and hence several researchers have mined such datasets to gain useful urban insights. These logistics companies, however, incur significant cost in deploying and maintaining their vast network of instrumented vehicles. Thus research problems, that are not only of interest to urban planners, but to the logistics companies themselves are important to attract and engage these companies for collaborative data analysis. In this paper, we show how GPS traces from taxis can be used to answer three different questions that are of great interest to a taxi operator. These questions are 1) What is the occupancy rate of the taxi fleet?, 2) What is the effect of route selection on the distance and time of a chosen route?, and 3) Does an analysis of travel times show deviations from the posted speed limits? We provide answers to each of these questions using a 2 month dataset of taxi records collected from over 10,000 taxis located in Singapore. The goal of this paper is to stimulate interest in the questions listed above (as they are of high interest to fleet operators) while also soliciting suggestions for better techniques to solve the problems stated above.
SENSEMINE'13: Proceedings of the 1st International Workshop on Sensing and Big Data Mining (SenseMine)
City or Country
SEN, Rijurekha and BALAN, Rajesh Krishna.
Challenges and Opportunities in Taxi Fleet Anomaly Detection. (2013). SENSEMINE'13: Proceedings of the 1st International Workshop on Sensing and Big Data Mining (SenseMine). 1-6. Research Collection School Of Information Systems.
Available at: https://ink.library.smu.edu.sg/sis_research/2241
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