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Mining Urban Traffic Condition from Crowd-Sourced Data

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Abstract

Traffic congestion is an inherent and hard issue to be tackled in huge urban areas, particularly in developing countries where transportation infrastructures have not been grown well to fulfill speedy developing request demands. This paper proposes novel solutions to these issues by devising mobile crowd-sourcing based approaches to traffic estimation. A framework for effective collecting, integrating and analyzing traffic-related data shared by mobile crowds has been devised. Besides, essential issues on predicting traffic conditions at streets where real-time data is missed are also resolved by applying data mining techniques to historical data. A prototype system has been developed to validate the proposed solutions. The experimental results show the feasibility and the effectiveness of the proposed methods revealing that they are ready to be applied in the practice.

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Acknowledgements

This research is funded by the Department of Science and Technology (DoST), Ho Chi Minh City, under grant number 34/2018/HD-QKHCN.

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Authors and Affiliations

  1. Department of Information Systems, Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet, District 10, Ho Chi Minh City, Vietnam

    Ha Mai-Tan, Hoang-Nam Pham-Nguyen & Quang Tran Minh

  2. Department of Bridge and Highway Engineering, Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet, District 10, Ho Chi Minh City, Vietnam

    Nguyen Xuan Long

  3. Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam

    Ha Mai-Tan, Hoang-Nam Pham-Nguyen, Nguyen Xuan Long & Quang Tran Minh

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  1. Ha Mai-Tan
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  2. Hoang-Nam Pham-Nguyen
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  3. Nguyen Xuan Long
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  4. Quang Tran Minh
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Correspondence to Quang Tran Minh.

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This article is part of the topical collection “Software Technology and Its Enabling Computing Platforms” guest edited by Lam-Son Lê and Michel Toulouse.

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Mai-Tan, H., Pham-Nguyen, HN., Long, N.X. et al. Mining Urban Traffic Condition from Crowd-Sourced Data. SN COMPUT. SCI. 1, 225 (2020). https://doi.org/10.1007/s42979-020-00244-6

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