research-article

Transportation mode detection using mobile phones and GIS information

Authors:

Bo XuAuthors Info & Claims

GIS '11: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 54 - 63

https://doi.org/10.1145/2093973.2093982

Published: 01 November 2011 Publication History

Abstract

The transportation mode such as walking, cycling or on a train denotes an important characteristic of the mobile user's context. In this paper, we propose an approach to inferring a user's mode of transportation based on the GPS sensor on her mobile device and knowledge of the underlying transportation network. The transportation network information considered includes real time bus locations, spatial rail and spatial bus stop information. We identify and derive the relevant features related to transportation network information to improve classification effectiveness. This approach can achieve over 93.5% accuracy for inferring various transportation modes including: car, bus, aboveground train, walking, bike, and stationary. Our approach improves the accuracy of detection by 17% in comparison with the GPS only approach, and 9% in comparison with GPS with GIS models. The proposed approach is the first to distinguish between motorized transportation modes such as bus, car and aboveground train with such high accuracy. Additionally, if a user is travelling by bus, we provide further information about which particular bus the user is riding. Five different inference models including Bayesian Net, Decision Tree, Random Forest, Naïve Bayesian and Multilayer Perceptron, are tested in the experiments. The final classification system is deployed and available to the public.

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Cited By

Molina-Campoverde JRivera-Campoverde NMolina Campoverde PBermeo Naula A(2024)Urban Mobility Pattern Detection: Development of a Classification Algorithm Based on Machine Learning and GPSSensors10.3390/s2412388424:12(3884)Online publication date: 15-Jun-2024
https://doi.org/10.3390/s24123884
Li GXu RShi TDeng XLiu YDi DZhao CLiu G(2024)Fine-Grained Metro-Trip Detection from Cellular Trajectory Data Using Local and Global Spatial–Temporal CharacteristicsISPRS International Journal of Geo-Information10.3390/ijgi1309031413:9(314)Online publication date: 30-Aug-2024
https://doi.org/10.3390/ijgi13090314
Saleh Namadi STahmasbi BMehditabrizi ADarzi ANiemeier D(2024)Using Geographically Weighted Models to Explore Temporal and Spatial Varying Impacts on Commute Trip Change Resulting from COVID-19Transportation Research Record: Journal of the Transportation Research Board10.1177/03611981241231797Online publication date: 3-Mar-2024
https://doi.org/10.1177/03611981241231797
Show More Cited By

Index Terms

Transportation mode detection using mobile phones and GIS information
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees

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Published In

cover image ACM Conferences

GIS '11: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2011

559 pages

ISBN:9781450310314

DOI:10.1145/2093973

Editors:
Divyakant Agrawal
University of California at Santa Barbara
,
Isabel Cruz
University of Illinois at Chicago
,
Christian S. Jensen
Aarhus University, Denmark
,
Eyal Ofek
Microsoft, USA
,
Egemen Tanin
University of Melbourne, Australia

Copyright © 2011 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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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 November 2011

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GIS '11

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SIGSPATIAL

GIS '11: 19th SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 1 - 4, 2011

Illinois, Chicago

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Overall Acceptance Rate 220 of 1,116 submissions, 20%

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Cited By

Molina-Campoverde JRivera-Campoverde NMolina Campoverde PBermeo Naula A(2024)Urban Mobility Pattern Detection: Development of a Classification Algorithm Based on Machine Learning and GPSSensors10.3390/s2412388424:12(3884)Online publication date: 15-Jun-2024
https://doi.org/10.3390/s24123884
Li GXu RShi TDeng XLiu YDi DZhao CLiu G(2024)Fine-Grained Metro-Trip Detection from Cellular Trajectory Data Using Local and Global Spatial–Temporal CharacteristicsISPRS International Journal of Geo-Information10.3390/ijgi1309031413:9(314)Online publication date: 30-Aug-2024
https://doi.org/10.3390/ijgi13090314
Saleh Namadi STahmasbi BMehditabrizi ADarzi ANiemeier D(2024)Using Geographically Weighted Models to Explore Temporal and Spatial Varying Impacts on Commute Trip Change Resulting from COVID-19Transportation Research Record: Journal of the Transportation Research Board10.1177/03611981241231797Online publication date: 3-Mar-2024
https://doi.org/10.1177/03611981241231797
Musleh MMokbel M(2024)Let's Speak Trajectories: A Vision to Use NLP Models for Trajectory Analysis TasksACM Transactions on Spatial Algorithms and Systems10.1145/365647010:2(1-25)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3656470
Aziz AYoukee NAhmed FTasnim SFahim-Ul-Islam MChakrabarty A(2024)Enhancing Transparency in Transport Mode Detection: An Interpretable Ensemble Model Classifier2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS)10.1109/iCACCESS61735.2024.10499511(1-6)Online publication date: 8-Mar-2024
https://doi.org/10.1109/iCACCESS61735.2024.10499511
Taherinavid SMoravvej SChen YYang JKu CYee P(2024)Automatic Transportation Mode Classification Using a Deep Reinforcement Learning Approach With Smartphone SensorsIEEE Access10.1109/ACCESS.2023.334687512(514-533)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2023.3346875
Heo JHwang SMoon JYou JKim HCha JKim K(2024)A framework of transportation mode detection for people with mobility disabilityJournal of Intelligent Transportation Systems10.1080/15472450.2024.2329901(1-16)Online publication date: 19-Mar-2024
https://doi.org/10.1080/15472450.2024.2329901
Casillo MColace FLorusso ASantaniello DValentino C(2024)A multilevel graph approach for IoT-based complex scenario management through situation awareness and semantic approachesJournal of Reliable Intelligent Environments10.1007/s40860-024-00224-0Online publication date: 28-May-2024
https://doi.org/10.1007/s40860-024-00224-0
Sowlati SAli Abbaspour RChehreghan A(2024)An approach to assess the role of features in detection of transportation modesTransportation10.1007/s11116-024-10492-7Online publication date: 18-May-2024
https://doi.org/10.1007/s11116-024-10492-7
Cheng SLiu Y(2023)Research on Transportation Mode Recognition Based on Multi-Head Attention Temporal Convolutional NetworkSensors10.3390/s2307358523:7(3585)Online publication date: 29-Mar-2023
https://doi.org/10.3390/s23073585
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