Abstract
One of the crucial problems for taxi drivers is to efficiently locate passengers in order to increase profits. The rapid advancement and ubiquitous penetration of Internet of Things (IoT) technology into transportation industries enables us to provide taxi drivers with locations that have more potential passengers (more profitable areas) by analyzing and querying taxi trip data. In this paper, we propose a query processing system, called Distributed Profitable-Area Query (DISPAQ) which efficiently identifies profitable areas by exploiting the Apache Software Foundation’s Spark framework and a MongoDB database. DISPAQ first maintains a profitable-area query index (PQ-index) by extracting area summaries and route summaries from raw taxi trip data. It then identifies candidate profitable areas by searching the PQ-index during query processing. Then, it exploits a Z-Skyline algorithm, which is an extension of skyline processing with a Z-order space filling curve, to quickly refine the candidate profitable areas. To improve the performance of distributed query processing, we also propose local Z-Skyline optimization, which reduces the number of dominant tests by distributing killer profitable areas to each cluster node. Through extensive evaluation with real datasets, we demonstrate that our DISPAQ system provides a scalable and efficient solution for processing profitable-area queries from huge amounts of big taxi trip data.
Highlights
Internet of Things (IoT) technology enables interconnections between large volumes of distributed and heterogeneous smart devices allowing them to communicate seamlessly with users
To improve the performance of distributed query processing, we propose local Z-Skyline optimization, which reduces the number of dominant tests by distributing killer profitable areas to each cluster node
Through extensive evaluation with real datasets, we demonstrate that our Distributed Profitable-Area Query (DISPAQ) system provides a scalable and efficient solution for processing profitable-area queries from huge amounts of big taxi trip data
Summary
Internet of Things (IoT) technology enables interconnections between large volumes of distributed and heterogeneous smart devices allowing them to communicate seamlessly with users. IoT devices such such as sensors, global positioning systems (GPSs), and cameras have become widely used in transportation industries. Several countries such as the USA [1], Germany [2], Japan [3] and Korea [4], collect diverse data from taxis equipped with IoT devices. Big data analytics as a big part of data science enables us to provide intelligent services to customers, and to improve work efficiency and profitability of taxi drivers by analyzing the collected data. Finding good taxi strategies for improving services and profits is one of the core applications in smart transportation [6]. Most existing approaches analyze collected GPS sensor data to extract taxi strategies, e.g., increasing traffic system efficiency [7], measuring graph-based efficiency of taxi
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