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Spotting Trip Purposes from Taxi Trajectories: A General Probabilistic Model

Published: 11 December 2017 Publication History

Abstract

What is the purpose of a trip? What are the unique human mobility patterns and spatial contexts in or near the pickup points and delivery points of trajectories for a specific trip purpose? Many prior studies have modeled human mobility patterns in urban regions; however, these analytics mainly focus on interpreting the semantic meanings of geographic topics at an aggregate level. Given the lack of information about human activities at pick-up and dropoff points, it is challenging to convert the prior studies into effective tools for inferring trip purposes. To address this challenge, in this article, we study large-scale taxi trajectories from an unsupervised perspective in light of the following observations. First, the POI configurations of origin and destination regions closely relate to the urban functionality of these regions and further indicate various human activities. Second, with respect to the functionality of neighborhood environments, trip purposes can be discerned from the transitions between regions with different functionality at particular time periods.
Along these lines, we develop a general probabilistic framework for spotting trip purposes from massive taxi GPS trajectories. Specifically, we first augment the origin and destination regions of trajectories by attaching neighborhood POIs. Then, we introduce a latent factor, POI Topic, to represent the mixed functionality of the regions, such that each origin or destination point in the city can be modeled as a mixture over POI Topics. In addition, considering the transitions from origins to destinations at specific time periods, the trip time is generated collaboratively from the pairwise POI Topics at both ends of the O-D pairs, constituting POI Links, and hence the trip purpose can be explained semantically by the POI Links. Finally, we present extensive experiments with the real-world data of New York City to demonstrate the effectiveness of our proposed method for spotting trip purposes, and moreover, the model is validated to perform well in predicting the destinations and trip time among all the baseline methods.

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  • (2023)Predicting Urban Region Heat via Learning Arrive-Stay-Leave Behaviors of Private CarsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.327670424:10(10843-10856)Online publication date: Oct-2023
  • (2023)A Dual-Flow Attentive Network With Feature Crossing for Chained Trip Purpose InferenceIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.321396924:1(631-644)Online publication date: Jan-2023
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    Published In

    cover image ACM Transactions on Intelligent Systems and Technology
    ACM Transactions on Intelligent Systems and Technology  Volume 9, Issue 3
    Regular Papers and Special Issue: Urban Intelligence
    May 2018
    370 pages
    ISSN:2157-6904
    EISSN:2157-6912
    DOI:10.1145/3167125
    • Editor:
    • Yu Zheng
    Issue’s Table of Contents
    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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    Publication History

    Published: 11 December 2017
    Accepted: 01 March 2017
    Revised: 01 March 2017
    Received: 01 November 2016
    Published in TIST Volume 9, Issue 3

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    Author Tags

    1. Human mobility
    2. probabilistic model
    3. taxi trajectories
    4. trip purposes

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    • University of Missouri Research Board
    • National Key Research Program of China
    • Natural Science Foundation of China

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    Cited By

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    • (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: Dec-2024
    • (2023)Predicting Urban Region Heat via Learning Arrive-Stay-Leave Behaviors of Private CarsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.327670424:10(10843-10856)Online publication date: Oct-2023
    • (2023)A Dual-Flow Attentive Network With Feature Crossing for Chained Trip Purpose InferenceIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.321396924:1(631-644)Online publication date: Jan-2023
    • (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
    • (2023)Inferring nonwork travel semantics and revealing the nonlinear relationships with the community built environmentSustainable Cities and Society10.1016/j.scs.2023.10488999(104889)Online publication date: Dec-2023
    • (2022)Understanding Urban Area Attractiveness Based on Private Car Trajectory Data Using a Deep Learning ApproachIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.311370523:8(12343-12352)Online publication date: Aug-2022
    • (2022)Towards the Inference of Travel Purpose with Heterogeneous Urban DataIEEE Transactions on Big Data10.1109/TBDATA.2019.29218238:1(166-177)Online publication date: 1-Feb-2022
    • (2021)Context-Aware Semantic Annotation of Mobility RecordsACM Transactions on Knowledge Discovery from Data10.1145/347704816:3(1-20)Online publication date: 22-Oct-2021
    • (2021)Automated Feature-Topic PairingProceedings of the 29th International Conference on Advances in Geographic Information Systems10.1145/3474717.3484212(450-453)Online publication date: 2-Nov-2021
    • (2021)App2Vec: Context-Aware Application Usage PredictionACM Transactions on Knowledge Discovery from Data10.1145/345139615:6(1-21)Online publication date: 28-Jun-2021
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