DISPAQ: Distributed Profitable-Area Query from Big Taxi Trip Data

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.

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

“…The proposed system was capable of handling read and write access for navigation and positioning data in a millisecond and the performance improved by around two percent compared with the traditional model. Putri et al proposed a big data processing system based on Apache Spark and MongoDB to identify profitable areas from large amounts of taxi trip data [ 36 ]. The experimental results showed that the proposed system was scalable and efficient enough in processing profitable-area queries from huge amounts of big taxi trip data.…”

“…Big data analysis has led to significant improvements in the manufacturing industry, such as reducing energy consumption [ 17 ], improving production scheduling and logistics planning [ 18 ], mitigating social risks [ 19 ], and facilitating better decision making [ 20 ]. Previous studies have shown significant benefits from several big data technologies in processing and storing large volumes of data quickly, such as with the application of Apache Kafka [ 21 , 22 , 23 , 24 , 25 , 26 ], Apache Storm [ 27 , 28 , 29 , 30 , 31 ], and NoSQL MongoDB [ 32 , 33 , 34 , 35 , 36 , 37 ]. Previous studies showed significant advantages from the integration of big data technologies such as reducing the processing time for home automation systems [ 38 ], providing effective and efficient solutions for processing IoT-generated data for smart cities [ 39 ], and handling large amounts of smart environmental data in real-time [ 40 ].…”

A Taxi Trajectory and Social Media Data Management Platform for Tourist Behavior Analysis

“…Repository stores data on tourist trajectories and social media with a document-based database called MongoDB. The database supports a significant amount of data and scales it with a cloud computing platform [34,35]. It also includes functions that manage geospatial data, including geospatial indexes and geospatial query.…”

Uncovering the Relationship between Urban Road Network Topology and Taxi Drivers’ Income: A Perspective from Spatial Design Network Analysis

“…Li et al [51] proposed a taxi recommendation system based on inter-regional passenger mobility which can maximize the profit of drivers. In the meantime, many scholars have put forward high-income solutions from the perspectives of high profit area mining [20,21], high-quality passenger analysis [22,23], and profitable routes selection [24][25][26]. Machine learning algorithms, heuristic algorithms, clustering algorithms, and the Markov decision process are also used to optimize taxi revenue efficiency in some studies such as [26,[52][53][54].…”

“…In the past few years, scholars have discussed the influencing factors of taxi drivers' income from different perspectives, including drivers' operation strategies [1,[3][4][5][6][7][8][9][10][11][12], regulation [13], price structure and fare [14,15], urban regional characteristics [16], traffic conditions [8], weather [17,18], COVID-19 [19], etc. In addition, many high-income solutions based on big data analysis technology have been proposed, such as high profit areas mining [20,21], high-quality passenger analysis [22,23], and profitable routes selection [24][25][26]. These studies have contributed to increasing the income of drivers and improving the governance level of the taxi industry.…”