research-article

TripImputor: Real-Time Imputing Taxi Trip Purpose Leveraging Multi-Sourced Urban Data

Authors:

Yasha WangAuthors Info & Claims

IEEE Transactions on Intelligent Transportation Systems, Volume 19, Issue 10

Pages 3292 - 3304

https://doi.org/10.1109/TITS.2017.2771231

Published: 01 October 2018 Publication History

Abstract

Travel behavior understanding is a long-standing and critically important topic in the area of smart cities. Big volumes of various GPS-based travel data can be easily collected, among which the taxi GPS trajectory data is a typical example. However, in GPS trajectory data, there is usually little information on travelers’ activities, thereby they can only support limited applications. Quite a few studies have been focused on enriching the semantic meaning for raw data, such as travel mode/purpose inferring. Unfortunately, trip purpose imputation receives relatively less attention and requires no real-time response. To narrow the gap, we propose a probabilistic two-phase framework named <italic>TripImputor</italic>, for making the <italic>real-time</italic> taxi trip purpose imputation and recommending services to passengers at their dropoff points. Specifically, in the first phase, we propose a two-stage clustering algorithm to identify candidate activity areas (CAAs) in the urban space. Then, we extract fine-granularity spatial and temporal patterns of human behaviors inside the CAAs from foursquare check-in data to approximate the priori probability for each activity, and compute the posterior probabilities (i.e., infer the trip purposes) using Bayes’ theorem. In the second phase, we take a sophisticated procedure that clusters historical dropoff points and matches the dropoff clusters and CAAs to immerse the real-time response. Finally, we evaluate the effectiveness and efficiency of the proposed two-phase framework using real-world data sets, which consist of road network, check-in data generated by over 38 000 users in one year, and the large-scale taxi trip data generated by over 19 000 taxis in a month in Manhattan, New York City, USA. Experimental results demonstrate that the system is able to infer the trip purpose accurately, and can provide recommendation results to passengers within 1.6 s in Manhattan on average, just using a single normal PC.

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Cited By

Liao CChen CZhang WGuo SLiu C(2024)AGENDA: Predicting Trip Purposes with A New Graph Embedding Network and Active Domain AdaptationACM Transactions on Knowledge Discovery from Data10.1145/367702018:8(1-25)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3677020
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
Guo SChen CLi ZLiao CLiu YXu KZhang D(2024)Privacy Leakage From Dynamic Prices: Trip Purpose Mining as an ExampleIEEE Transactions on Mobile Computing10.1109/TMC.2024.340841923:12(12378-12395)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TMC.2024.3408419
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Index Terms

TripImputor: Real-Time Imputing Taxi Trip Purpose Leveraging Multi-Sourced Urban Data
1. Human-centered computing
  1. Visualization
    1. Visualization application domains
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems

Index terms have been assigned to the content through auto-classification.

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cover image IEEE Transactions on Intelligent Transportation Systems

IEEE Transactions on Intelligent Transportation Systems Volume 19, Issue 10

Oct. 2018

342 pages

ISSN:1524-9050

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1524-9050 © 2018 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

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Published: 01 October 2018

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Cited By

Liao CChen CZhang WGuo SLiu C(2024)AGENDA: Predicting Trip Purposes with A New Graph Embedding Network and Active Domain AdaptationACM Transactions on Knowledge Discovery from Data10.1145/367702018:8(1-25)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3677020
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
Guo SChen CLi ZLiao CLiu YXu KZhang D(2024)Privacy Leakage From Dynamic Prices: Trip Purpose Mining as an ExampleIEEE Transactions on Mobile Computing10.1109/TMC.2024.340841923:12(12378-12395)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TMC.2024.3408419
Wei TLin YLin YGuo SZhang LWan H(2024)Micro-Macro Spatial-Temporal Graph-Based Encoder-Decoder for Map-Constrained Trajectory RecoveryIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.339615836:11(6574-6587)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3396158
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https://dl.acm.org/doi/10.1016/j.asoc.2024.111260
Isufaj KElshrif MAbbar SMokbel MRenz MNascimento M(2023)GTI: A Scalable Graph-based Trajectory ImputationProceedings of the 31st ACM International Conference on Advances in Geographic Information Systems10.1145/3589132.3625620(1-10)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3589132.3625620
Liu HGuo QZhu HFu YZhuang FMa XXiong H(2023)Characterizing and Forecasting Urban Vibrancy Evolution: A Multi-View Graph Mining PerspectiveACM Transactions on Knowledge Discovery from Data10.1145/356868317:5(1-24)Online publication date: 28-Feb-2023
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Liu ZZheng JLin JWang LWu K(2023)$\mathtt {Radar}$: Adversarial Driving Style Representation Learning With Data AugmentationIEEE Transactions on Mobile Computing10.1109/TMC.2022.320826522:12(7070-7085)Online publication date: 1-Dec-2023
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Liao CChen CGuo SWang LGu FLuo JXu K(2023)Enriching Large-Scale Trips With Fine-Grained Travel Purposes: A Semi-Supervised Deep Graph Embedding FrameworkIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.320346424:11(13228-13239)Online publication date: 1-Nov-2023
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