research-article

Sensing the pulse of urban refueling behavior

Authors:

Xing XieAuthors Info & Claims

UbiComp '13: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing

Pages 13 - 22

https://doi.org/10.1145/2493432.2493448

Published: 08 September 2013 Publication History

Abstract

Urban transportation is increasingly studied due to its complexity and economic importance. It is also a major component of urban energy use and pollution. The importance of this topic will only increase as urbanization continues around the world. A less researched aspect of transportation is the refueling behavior of drivers. In this paper, we propose a step toward real-time sensing of refueling behavior and citywide petrol consumption. We use reported trajectories from a fleet of GPS-equipped taxicabs to detect gas station visits, measure the time spent, and estimate overall demand. For times and stations with sparse data, we use collaborative filtering to estimate conditions. Our system provides real-time estimates of gas stations' waiting times, from which recommendations could be made, an indicator of overall gas usage, from which macro-scale economic decisions could be made, and a geographic view of the efficiency of gas station placement.

References

[1]

J. H. Friedman, "Stochastic gradient boosting," in Computational Statistics & Data Analysis, vol. 1, no. 3, 2002, pp. 367--378.

Digital Library

[2]

G. Adomavicius and A. Tuzhilin, Recommender Systems Handbook. Boston, MA: Springer US, 2011, pp. 217--253.

[3]

A. Karatzoglou, X. Amatriain, and N. Oliver, "Multiverse Recommendation : N-dimensional Tensor Factorization for Context-aware Collaborative Filtering," pp. 79--86.

Digital Library

[4]

L. De Lathauwer, B. De Moor, and J. Vandewalle, "A Multilinear Singular Value Decomposition," SIAM Journal on Matrix Analysis and Applications, vol. 21, no. 4, pp. 1253--1278, Jan. 2000.

Digital Library

[5]

G. Iyer and P. B. Seetharaman, "Quality and location in retail gasoline markets," 2005.

[6]

L. Baltrunas, B. Ludwig, and F. Ricci, "Matrix Factorization Techniques for Context Aware," pp. 301--304.

Digital Library

[7]

T. Zhang, "Solving large scale linear prediction problems using stochastic gradient descent algorithms," in Twenty-first international conference on Ma-chine learning - ICML '04, 2004, p. 116.

Digital Library

[8]

P. Jensen, "A network-based prediction of retail stores commercial categories and optimal locations," pp. 1--5, 2008.

[9]

D. L. Huff, "Defining and Estimating a Trading Area," Journal of Marketing, vol. Vol. 28, N, pp. 34--38.

[10]

L. Kleinrock and J. Wiley, "Queueing Systems," IEEE Transactions on Communications, vol. Volume 1:, pp. 178--179, 1977.

[11]

Kelley, M. Kuby, G. Sciences, and U. Planning, "On the Way or Around the Corner" Observed Refueling Choices of Alternative Fuel Vehicle Drivers in Southern California," no. 1, p. 1025313, 2006.

[12]

K. Li, M. Lu, F. Lu, Q. Lv, L. Shang, and D. Maksimovic, "Personalized Driv-ing Behavior Monitoring and Analysis for Emerging Hybrid Vehicles," in Pervasive Computing, 2012, p. pp 1--19.

Digital Library

[13]

T. Y. Chan, An Econometric Model of Location and Pricing in the Gasoline Market, no. August 2004. 2006, pp. 1--37.

[14]

G. Iyer and P. B. Seetharaman, "Too close to be similar: Product and price competition in retail gasoline markets," Quantitative Marketing and Economics, vol. 6, no. 3, pp. 205--234, May 2008.

[15]

Y. Zheng, Y. Liu, J. Yuan, and X. Xie, "Urban computing with taxicabs," in Proceedings of the 13th international conference on Ubiquitous computing - UbiComp '11, 2011, p. 89.

Digital Library

[16]

T. Kindberg, M. Chalmers, and E. Paulos, "Urban Computing," 2007.

[17]

I. Leontiadis, G. Marfia, D. Mack, G. Pau, C. Mascolo, and M. Gerla, "On the Effectiveness of an Opportunistic Traffic Management System for Vehicular Networks," IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 4, pp. 1537--1548, Dec. 2011.

Digital Library

[18]

J. Yuan, Y. Zheng, C. Zhang, W. Xie, X. Xie, G. Sun, and Y. Huang, "T-Drive : Driving Directions Based on Taxi Trajectories."

[19]

N. J. Yuan, Y. Zheng, L. Zhang, and X. Xie, "T-Finder: A Recommender System for Finding Passengers and Vacant Taxis," IEEE Transactions on Knowledge and Data Engineering, pp. 1--1, 2012.

Cited By

Kim JConti AWin M(2025)Travel Demand Modeling and Estimation for High-Dimensional MobilityIEEE Transactions on Mobile Computing10.1109/TMC.2024.343543624:3(1264-1277)Online publication date: Mar-2025
https://doi.org/10.1109/TMC.2024.3435436
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
Kraft RBlasi MSchickler MReichert MPryss R(2024)Operationalizing the Use of Sensor Data in Mobile Crowdsensing: A Systematic Review and Practical GuidelinesCollaborative Computing: Networking, Applications and Worksharing10.1007/978-3-031-54531-3_13(229-248)Online publication date: 23-Feb-2024
https://doi.org/10.1007/978-3-031-54531-3_13
Show More Cited By

Index Terms

Sensing the pulse of urban refueling behavior
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
      1. Geographic visualization
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems

Recommendations

Sensing the Pulse of Urban Refueling Behavior: A Perspective from Taxi Mobility
Survey Paper, Regular Papers and Special Section on Participatory Sensing and Crowd Intelligence

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, ...
DSS of vehicle refueling

The fixed-route vehicle-refueling problem (FRVRP) is a mathematical problem widely used in the U.S. trucking industry. The FRVRP seeks the best refueling policy (sequence of fuel stations to use, along with the fueling quantity at each station) for a ...
Simulation-Based Analysis of Hydrogen Refuelling Station to Support Future Hydrogen Trucks and Technological Advances
WSC '24: Proceedings of the Winter Simulation Conference

Hydrogen is becoming increasingly relevant as an energy source to decrease carbon dioxide emissions across transportation, industry, and power generation sectors. One step to achieving this goal is to endorse the utilisation of hydrogen as a fuel in the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

UbiComp '13: Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing

September 2013

846 pages

ISBN:9781450317702

DOI:10.1145/2493432

General Chairs:
Friedemann Mattern
ETH Zurich, CH
,
Silvia Santini
TU Darmstadt, DE
,
Program Chairs:
John F. Canny
UC Berkeley, US
,
Marc Langheinrich
Università della Svizzera italiana, CH
,
Jun Rekimoto
University of Tokyo, JP

Copyright © 2013 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]

Sponsors

SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
University of Florida: University of Florida
SIGCHI: ACM Special Interest Group on Computer-Human Interaction

In-Cooperation

SIGSPATIAL: ACM Special Interest Group on Spatial Information

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 September 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

UbiComp '13

Sponsor:

SIGMOBILE
University of Florida
SIGCHI

UbiComp '13: The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 8 - 12, 2013

Zurich, Switzerland

Acceptance Rates

UbiComp '13 Paper Acceptance Rate 92 of 394 submissions, 23%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

66
Total Citations
View Citations
1,054
Total Downloads

Downloads (Last 12 months)33
Downloads (Last 6 weeks)1

Reflects downloads up to 19 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Kim JConti AWin M(2025)Travel Demand Modeling and Estimation for High-Dimensional MobilityIEEE Transactions on Mobile Computing10.1109/TMC.2024.343543624:3(1264-1277)Online publication date: Mar-2025
https://doi.org/10.1109/TMC.2024.3435436
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
Kraft RBlasi MSchickler MReichert MPryss R(2024)Operationalizing the Use of Sensor Data in Mobile Crowdsensing: A Systematic Review and Practical GuidelinesCollaborative Computing: Networking, Applications and Worksharing10.1007/978-3-031-54531-3_13(229-248)Online publication date: 23-Feb-2024
https://doi.org/10.1007/978-3-031-54531-3_13
Hashem IUsmani RAlmutairi MIbrahim AZakari AAlotaibi FAlhashmi SChiroma H(2023)Urban Computing for Sustainable Smart Cities: Recent Advances, Taxonomy, and Open Research ChallengesSustainability10.3390/su1505391615:5(3916)Online publication date: 21-Feb-2023
https://doi.org/10.3390/su15053916
Wang GChen YWang SZhang FZhang D(2023)ForETaxi: Data-Driven Fleet-Oriented Charging Resource Allocation in Large-Scale Electric Taxi NetworksACM Transactions on Sensor Networks10.1145/357095819:3(1-25)Online publication date: 2-Mar-2023
https://dl.acm.org/doi/10.1145/3570958
Kim JConti AWin M(2023)Inferring Spatiotemporal Mobility Patterns from Multidimensional Trip DataICC 2023 - IEEE International Conference on Communications10.1109/ICC45041.2023.10279019(3333-3338)Online publication date: 28-May-2023
https://doi.org/10.1109/ICC45041.2023.10279019
Katarya R(2022)Towards the significance of taxi recommender systems in smart citiesConcurrency and Computation: Practice and Experience10.1002/cpe.747535:2Online publication date: 4-Nov-2022
https://doi.org/10.1002/cpe.7475
Nie JGao RKang Y(2021)Urban Noise Inference Model Based on Multiple Views and Kernel Tensor DecompositionFluctuation and Noise Letters10.1142/S021947752150027920:03(2150027)Online publication date: 25-Jan-2021
https://doi.org/10.1142/S0219477521500279
Wang XYang LChen XDeen MJin J(2021)Improved Multi-Order Distributed HOSVD with Its Incremental Computing for Smart City ServicesIEEE Transactions on Sustainable Computing10.1109/TSUSC.2018.28814396:3(456-468)Online publication date: 1-Jul-2021
https://doi.org/10.1109/TSUSC.2018.2881439
Jiang ZChen LZhou BHuang JXie TFan XWang C(2021)iTV: Inferring Traffic Violation-Prone Locations With Vehicle Trajectories and Road Environment DataIEEE Systems Journal10.1109/JSYST.2020.301274315:3(3913-3924)Online publication date: Sep-2021
https://doi.org/10.1109/JSYST.2020.3012743
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten