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Sensing the Pulse of Urban Refueling Behavior: A Perspective from Taxi Mobility

Authors:

Nicholas Jing Yuan,

Xing XieAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 6, Issue 3

Article No.: 37, Pages 1 - 23

https://doi.org/10.1145/2644828

Published: 21 April 2015 Publication History

Abstract

Urban transportation is an important factor in energy consumption and pollution, and is of increasing concern due to its complexity and economic significance. Its importance will only increase as urbanization continues around the world. In this article, we explore drivers’ refueling behavior in urban areas. Compared to questionnaire-based methods of the past, we propose a complete data-driven system that pushes towards real-time sensing of individual refueling behavior and citywide petrol consumption. Our system provides the following: detection of individual refueling events (REs) from which refueling preference can be analyzed; estimates of gas station wait times from which recommendations can be made; an indication of overall fuel demand from which macroscale economic decisions can be made, and a spatial, temporal, and economic view of urban refueling characteristics. For individual behavior, we use reported trajectories from a fleet of GPS-equipped taxicabs to detect gas station visits. For time spent estimates, to solve the sparsity issue along time and stations, we propose context-aware tensor factorization (CATF), a factorization model that considers a variety of contextual factors (e.g., price, brand, and weather condition) that affect consumers’ refueling decision. For fuel demand estimates, we apply a queue model to calculate the overall visits based on the time spent inside the station. We evaluated our system on large-scale and real-world datasets, which contain 4-month trajectories of 32,476 taxicabs, 689 gas stations, and the self-reported refueling details of 8,326 online users. The results show that our system can determine REs with an accuracy of more than 90%, estimate time spent with less than 2 minutes of error, and measure overall visits in the same order of magnitude with the records in the field study.

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Cited By

Liu DKan ZKwan MTang L(2024) Space–Time Analysis of Refueling Patterns of Alternative Fuel Vehicles Using GPS Trajectory Data and Machine Learning Transactions in GIS10.1111/tgis.13258Online publication date: 6-Oct-2024
https://doi.org/10.1111/tgis.13258
Qian SChen HXue DFang QXu C(2023)Open-World Social Event ClassificationProceedings of the ACM Web Conference 202310.1145/3543507.3583291(1562-1571)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583291
Saldana-Perez MTorres-Ruiz MMoreno-Ibarra M(2022)Classification of Traffic Events in Mexico City Using Machine Learning and Volunteered Geographic InformationResearch Anthology on Machine Learning Techniques, Methods, and Applications10.4018/978-1-6684-6291-1.ch058(1107-1127)Online publication date: 13-May-2022
https://doi.org/10.4018/978-1-6684-6291-1.ch058
Show More Cited By

Index Terms

Sensing the Pulse of Urban Refueling Behavior: A Perspective from Taxi Mobility
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications
    1. Spatial-temporal systems

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Information & Contributors

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 6, Issue 3

Survey Paper, Regular Papers and Special Section on Participatory Sensing and Crowd Intelligence

May 2015

319 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2764959

Editor:
Huan Liu
Arizona State University, USA

Issue’s Table of Contents

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 April 2015

Accepted: 01 July 2014

Revised: 01 May 2014

Received: 01 December 2013

Published in TIST Volume 6, Issue 3

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Cited By

Liu DKan ZKwan MTang L(2024) Space–Time Analysis of Refueling Patterns of Alternative Fuel Vehicles Using GPS Trajectory Data and Machine Learning Transactions in GIS10.1111/tgis.13258Online publication date: 6-Oct-2024
https://doi.org/10.1111/tgis.13258
Qian SChen HXue DFang QXu C(2023)Open-World Social Event ClassificationProceedings of the ACM Web Conference 202310.1145/3543507.3583291(1562-1571)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583291
Saldana-Perez MTorres-Ruiz MMoreno-Ibarra M(2022)Classification of Traffic Events in Mexico City Using Machine Learning and Volunteered Geographic InformationResearch Anthology on Machine Learning Techniques, Methods, and Applications10.4018/978-1-6684-6291-1.ch058(1107-1127)Online publication date: 13-May-2022
https://doi.org/10.4018/978-1-6684-6291-1.ch058
Lu DSun JPeng YChen X(2022)Optimized Operation Plan for Hydrogen Refueling Station with On-Site Electrolytic ProductionSustainability10.3390/su1501034715:1(347)Online publication date: 26-Dec-2022
https://doi.org/10.3390/su15010347
Sun JPeng YLu DChen XXu WWeng LWu J(2022)Optimized Configuration and Operating Plan for Hydrogen Refueling Station with On-Site Electrolytic ProductionEnergies10.3390/en1507234815:7(2348)Online publication date: 23-Mar-2022
https://doi.org/10.3390/en15072348
Wang XZhang ZWang PChen JWu J(2022)Characteristic density peak clustering algorithm for taxi hot spots detectionJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22032743:4(5147-5164)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/JIFS-220327
Verma RPargal SDas DParbat TKambalapalli SMitra BChakraborty S(2022)Impact of Driving Behavior on Commuter’s Comfort During Cab Rides: Towards a New Perspective of Driver RatingACM Transactions on Intelligent Systems and Technology10.1145/352306313:6(1-25)Online publication date: 22-Sep-2022
https://dl.acm.org/doi/10.1145/3523063
Wang EDing RYang ZJin HMiao CSu LZhang FQiao CWang X(2022)Joint Charging and Relocation Recommendation for E-Taxi Drivers via Multi-Agent Mean Field Hierarchical Reinforcement LearningIEEE Transactions on Mobile Computing10.1109/TMC.2020.302217321:4(1274-1290)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TMC.2020.3022173
He SShin K(2022)Information Fusion for (Re)Configuring Bike Station Networks With CrowdsourcingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.299100034:2(736-752)Online publication date: 1-Feb-2022
https://doi.org/10.1109/TKDE.2020.2991000
Peng CChen JVijayakumar PKumar NHe D(2021)Efficient Distributed Decryption Scheme for IoT Gateway-based ApplicationsACM Transactions on Internet Technology10.1145/341447521:1(1-23)Online publication date: 5-Jan-2021
https://dl.acm.org/doi/10.1145/3414475
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