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TaxiRec: recommending road clusters to taxi drivers using ranking-based extreme learning machines

Published: 03 November 2015 Publication History

Abstract

Utilizing large-scale GPS data to improve taxi services becomes a popular research problem in the areas of data mining, intelligent transportation, and the Internet of Things. In this paper, we utilize a large-scale GPS data set generated by over 7,000 taxis in a period of one month in Nanjing, China, and propose TaxiRec; a framework for discovering the passenger-finding potentials of road clusters, which is incorporated into a recommender system for taxi drivers to hunt passengers. In TaxiRec, we first construct the road network by defining the nodes and road segments. Then, the road network is divided into a number of road clusters through a clustering process on the mid points of the road segments. Afterwards, a set of features for each road cluster is extracted from real-life data sets, and a ranking-based extreme learning machine (ELM) model is proposed to evaluate the passenger-finding potential of each road cluster. Experimental results demonstrate the feasibility and effectiveness of the proposed framework.

References

[1]
J. A. Hartigan and M. A. Wong. Algorithm AS 136: A k-means clustering algorithm. Applied Statistics, pages 100--108, 1979.
[2]
G. B. Huang, H. Zhou, X. Ding, and R. Zhang. Extreme learning machine for regression and multiclass classification. IEEE TSMCB, (99):1--17, 2010.
[3]
G. B. Huang, Q. Y. Zhu, and C. K. Siew. Extreme learning machine: theory and applications. Neurocomputing, 70(1--3):489--501, 2006.
[4]
B. Li, D. Zhang, L. Sun, C. Chen, S. Li, G. Qi, and Q. Yang. Hunting or waiting? Discovering passenger-finding strategies from a large-scale real-world taxi dataset. In IEEE PerCom Workshops, 2011.
[5]
S. Liu, J. Pu, Q. Luo, H. Qu, L. M. Ni, and R. Krishnan. VAIT: A visual analytics system for metropolitan transportation. IEEE TITS, 14(4):1586--1596, 2013.
[6]
S. Ma, Y. Zheng, and O. Wolfson. T-Share: A large-scale dynamic taxi ridesharing service. In IEEE ICDE, 2013.
[7]
L. Moreira-Matias, J. Gama, M. Ferreira, J. Mendes-Moreira, and L. Damas. On predicting the taxi-passenger demand: A real-time approach. In Progress in Artificial Intelligence, pages 54--65. Springer, 2013.
[8]
A. Nigrin. Neural networks for pattern recognition. The MIT press, 1993.
[9]
G. Qi, G. Pan, S. Li, Z. Wu, D. Zhang, L. Sun, and L. T. Yang. How long a passenger waits for a vacant taxi? Large-scale taxi trace mining for smart cities. In IEEE GreenCom, 2013.
[10]
R. Wang, S. Kwong, and D. D. Wang. An analysis of ELM approximate error based on random weight matrix. IJUFKS, 21(supp02):1--12, 2013.
[11]
R. Wang, S. Kwong, and X. Wang. A study on random weights between input and hidden layers in extreme learning machine. Soft Computing, 16(9):1465--1475, 2012.
[12]
Z. Wang, M. Lu, X. Yuan, J. Zhang, and H. v. d. Wetering. Visual traffic jam analysis based on trajectory data. IEEE TVCG, 19(12):2159--2168, 2013.
[13]
J. Yuan, Y. Zheng, C. Zhang, W. Xie, X. Xie, G. Sun, and Y. Huang. T-Drive: Driving directions based on taxi trajectories. In ACM SIGSPATIAL, 2010.
[14]
N. J. Yuan, Y. Zheng, L. Zhang, and X. Xie. T-Finder: A recommender system for finding passengers and vacant taxis. IEEE TKDE, 25(10):2390--2403, 2013.
[15]
Y. Zheng, Y. Liu, J. Yuan, and X. Xie. Urban computing with taxicabs. In ACM UbiComp, 2011.

Cited By

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  • (2024)T-PickSeer: visual analysis of taxi pick-up point selection behaviorJournal of Visualization10.1007/s12650-024-00968-027:3(451-468)Online publication date: 5-Mar-2024
  • (2023)Deep online recommendations for connected E-taxis by coupling trajectory mining and reinforcement learningInternational Journal of Geographical Information Science10.1080/13658816.2023.2279969(1-27)Online publication date: 15-Nov-2023
  • (2022)SCCS: Smart Cloud Commuting System With Shared Autonomous VehiclesIEEE Transactions on Big Data10.1109/TBDATA.2020.30412638:5(1301-1311)Online publication date: 1-Oct-2022
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        cover image ACM Conferences
        SIGSPATIAL '15: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems
        November 2015
        646 pages
        ISBN:9781450339674
        DOI:10.1145/2820783
        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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        Published: 03 November 2015

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        Author Tags

        1. extreme learning machine
        2. passenger-finding potential
        3. recommender system
        4. taxi trajectory data

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        SIGSPATIAL '15 Paper Acceptance Rate 38 of 212 submissions, 18%;
        Overall Acceptance Rate 257 of 1,238 submissions, 21%

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        Cited By

        View all
        • (2024)T-PickSeer: visual analysis of taxi pick-up point selection behaviorJournal of Visualization10.1007/s12650-024-00968-027:3(451-468)Online publication date: 5-Mar-2024
        • (2023)Deep online recommendations for connected E-taxis by coupling trajectory mining and reinforcement learningInternational Journal of Geographical Information Science10.1080/13658816.2023.2279969(1-27)Online publication date: 15-Nov-2023
        • (2022)SCCS: Smart Cloud Commuting System With Shared Autonomous VehiclesIEEE Transactions on Big Data10.1109/TBDATA.2020.30412638:5(1301-1311)Online publication date: 1-Oct-2022
        • (2021)Proposal for a Pivot-Based Vehicle Trajectory Clustering MethodTransportation Research Record: Journal of the Transportation Research Board10.1177/036119812110584292676:4(281-295)Online publication date: 4-Dec-2021
        • (2021)Predicting Taxi and Uber Demand in Cities: Approaching the Limit of PredictabilityIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.295568633:6(2723-2736)Online publication date: 1-Jun-2021
        • (2021)A rapid density method for taxi passengers hot spot recognition and visualization based on DBSCAN+Scientific Reports10.1038/s41598-021-88822-311:1Online publication date: 3-May-2021
        • (2020)Applying Exponential Family Distribution to Generalized Extreme Learning MachineIEEE Transactions on Systems, Man, and Cybernetics: Systems10.1109/TSMC.2017.278800550:5(1794-1804)Online publication date: May-2020
        • (2020)Optimizing Taxi Driver Profit Efficiency: A Spatial Network-Based Markov Decision Process ApproachIEEE Transactions on Big Data10.1109/TBDATA.2018.28755246:1(145-158)Online publication date: 1-Mar-2020
        • (2019)STIETRProceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery10.1145/3356471.3365228(5-8)Online publication date: 5-Nov-2019
        • (2019)Fusing Geographic Information Into Latent Factor Model for Pick-Up Region Recommendation2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)10.1109/ICMEW.2019.00-65(330-335)Online publication date: Jul-2019
        • Show More Cited By

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