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HTTP: a new framework for bus travel time prediction based on historical trajectories

Published: 06 November 2012 Publication History

Abstract

In this paper, we develop a new bus travel time prediction framework, called Historical Trajectory based Travel/Arrival Time Prediction (HTTP) for real-time prediction of travel time over future segments (and thus the arrival time at stops) of an on-going bus journey. The basic idea behind HTTP is to use a collection of historical trajectories "similar" to the current bus trajectory to predict the future segments. Specifically, the HTTP framework (1) samples a set of similar trajectories as the basis for travel time estimation instead of relying on only one historical trajectory best matching the on-going bus journey; and (2) explores different prediction schemes, namely, passed segments, temporal features, and hybrid methods, to identify the sample set of similar trajectories. We conduct a comprehensive empirical experimentation using real bus trajectory data collected from Taipei City, Taiwan to validate our ideas and to evaluate the proposed schemes. Experimental result shows that the proposed prediction schemes significantly outperforms the state-of-the-art and baseline techniques.

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      cover image ACM Conferences
      SIGSPATIAL '12: Proceedings of the 20th International Conference on Advances in Geographic Information Systems
      November 2012
      642 pages
      ISBN:9781450316910
      DOI:10.1145/2424321
      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: 06 November 2012

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      Author Tags

      1. bus
      2. prediction
      3. trajectory
      4. travel time

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      View all
      • (2023)Effects of Data Characteristics on Bus Travel Time Prediction: A Systematic StudySustainability10.3390/su1506473115:6(4731)Online publication date: 7-Mar-2023
      • (2023)OPTI: Order Preparation Time Inference for On-demand DeliveryACM Transactions on Sensor Networks10.1145/359261019:4(1-18)Online publication date: 13-Apr-2023
      • (2023)A Multitask Attention Network for Food Delivery Time PredictionJournal of Circuits, Systems and Computers10.1142/S021812662450025733:02Online publication date: 21-Jul-2023
      • (2023)Optimizing Cross-Line Dispatching for Minimum Electric Bus FleetIEEE Transactions on Mobile Computing10.1109/TMC.2021.311942122:4(2307-2322)Online publication date: 1-Apr-2023
      • (2022)CatETA: A Categorical Approximate Approach for Estimating Time of ArrivalIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.320789423:12(24389-24400)Online publication date: Dec-2022
      • (2022)Cross-Area Travel Time Uncertainty Estimation From Trajectory Data: A Federated Learning ApproachIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.320345723:12(24966-24978)Online publication date: Dec-2022
      • (2022)What Do We Know When? Modeling Predictability of Transit OperationsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.314524323:9(15684-15695)Online publication date: 1-Sep-2022
      • (2022)CoDriver ETA: Combine Driver Information in Estimated Time of Arrival by Driving Style Learning Auxiliary TaskIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.304038623:5(4037-4048)Online publication date: May-2022
      • (2022)Bus Travel Time Prediction Based on Ensemble Learning MethodsIEEE Intelligent Transportation Systems Magazine10.1109/MITS.2020.299017514:2(174-189)Online publication date: Mar-2022
      • (2022)Spatio-temporal modelling and prediction of bus travel time using a higher-order traffic flow modelPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2022.127086596(127086)Online publication date: Jun-2022
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