research-article

GTFS-Viz: tool for preprocessing and visualizing GTFS data

Authors:

Narumon Kunama,

Mudtana Worapan,

Santi Phithakkitnukoon,

Merkebe DemissieAuthors Info & Claims

UbiComp '17: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers

Pages 388 - 396

https://doi.org/10.1145/3123024.3124415

Published: 11 September 2017 Publication History

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Abstract

This paper presents a tool called GTFS-Viz for preprocessing and visualizing General Transit Feed Specification (GTFS) data. GTFS data is a common format for the public transportation schedules with associated geographic information The tool consists of two main parts: preprocessor and visualizer. The preprocessor retrieves and transfers the GTFS data into a new data format for the visualizer. The visualizer displays an animation of public transport movement according to its schedule specified in the GTFS data on a map. It also displays a graph of the number of public transport vehicles per hour, and allows the user to choose a specific transport route, trip, and stations to visualize. This is the first attempt to build a tool that preprocesses and visualizes GTFS data that is publically available in a format that is not so easy to process with existing standard data visualization tools.

References

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A. Antrim and S. J. Barbeau. The Many Uses of GTFS Data - Opening the Door to Transit and Multimodal Applications. In Proc. ITS America's 23rd Annual Meeting & Exposition, 2013.

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J. Raghothama, V. Magal Shreenath and S. Meijer. Analytics on Public Transport delays with Spatial Big Data. In Proc. ACM BigSpatial, 2016.

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H. Bast, P. Brosi and S. Storandt Real-Time Movement Visualization of Public Transit Data. In Proc ACM SIGSPATIAL, 2014.

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F. Guo 2012. More Than Usability: The Four Elements of User Experience Retrieved April 22, 2017 from http://www.uxmatters.com/mt/archives/2012/04/more-than-usability-the-four-elements-ofuserexperience-part-i.php

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Cited By

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Para SWirotsasithon TJundee TDemissie MSekimoto YBiljecki FPhithakkitnukoon S(2024)G2Viz: an online tool for visualizing and analyzing a public transit system from GTFS dataPublic Transport10.1007/s12469-024-00362-x16:3(893-928)Online publication date: 8-May-2024
https://doi.org/10.1007/s12469-024-00362-x
Bannur CBhat CGoutham GMamatha H(2023)Traffic Congestion Prediction Based on Spatio-Temporal Graph Structure Learning2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)10.1109/IITCEE57236.2023.10090902(264-269)Online publication date: 27-Jan-2023
https://doi.org/10.1109/IITCEE57236.2023.10090902
Dardas AHall BSalter JHosseini H(2022)A geospatial workflow for the assessment of public transit system performance using near real‐time dataTransactions in GIS10.1111/tgis.1294226:4(1642-1664)Online publication date: 2-May-2022
https://doi.org/10.1111/tgis.12942
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Index Terms

GTFS-Viz: tool for preprocessing and visualizing GTFS data
1. Human-centered computing

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Published In

UbiComp '17: Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers

September 2017

1089 pages

ISBN:9781450351904

DOI:10.1145/3123024

General Chairs:
Seungyon "Claire" Lee
Google
,
Leila Takayama
UC Santa Cruz
,
Khai Truong
University of Toronto

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 September 2017

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UbiComp '17

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UbiComp '17: The 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 11 - 15, 2017

Hawaii, Maui

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Overall Acceptance Rate 764 of 2,912 submissions, 26%

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Cited By

View all

Para SWirotsasithon TJundee TDemissie MSekimoto YBiljecki FPhithakkitnukoon S(2024)G2Viz: an online tool for visualizing and analyzing a public transit system from GTFS dataPublic Transport10.1007/s12469-024-00362-x16:3(893-928)Online publication date: 8-May-2024
https://doi.org/10.1007/s12469-024-00362-x
Bannur CBhat CGoutham GMamatha H(2023)Traffic Congestion Prediction Based on Spatio-Temporal Graph Structure Learning2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE)10.1109/IITCEE57236.2023.10090902(264-269)Online publication date: 27-Jan-2023
https://doi.org/10.1109/IITCEE57236.2023.10090902
Dardas AHall BSalter JHosseini H(2022)A geospatial workflow for the assessment of public transit system performance using near real‐time dataTransactions in GIS10.1111/tgis.1294226:4(1642-1664)Online publication date: 2-May-2022
https://doi.org/10.1111/tgis.12942
Demissie MKattan L(2022)Understanding the temporal and spatial interactions between transit ridership and urban land-use patterns: an exploratory studyPublic Transport10.1007/s12469-022-00296-214:2(385-417)Online publication date: 8-Apr-2022
https://doi.org/10.1007/s12469-022-00296-2
Godfrid JRadnic PVaisman AZimányi E(2022)Analyzing public transport in the city of Buenos Aires with MobilityDBPublic Transport10.1007/s12469-022-00290-814:2(287-321)Online publication date: 1-Mar-2022
https://doi.org/10.1007/s12469-022-00290-8
Phithakkitnukooon SPatanukhom KDemissie M(2021)Predicting Spatiotemporal Demand of Dockless E-Scooter Sharing Services with a Masked Fully Convolutional NetworkISPRS International Journal of Geo-Information10.3390/ijgi1011077310:11(773)Online publication date: 13-Nov-2021
https://doi.org/10.3390/ijgi10110773
Demissie MKattan LPhithakkitnukoon SHomem de Almeida Correia GVeloso MBento C(2021)Modeling Location Choice of Taxi Drivers for Passenger Pickup Using GPS DataIEEE Intelligent Transportation Systems Magazine10.1109/MITS.2020.301409913:1(70-90)Online publication date: Sep-2022
https://doi.org/10.1109/MITS.2020.3014099
Boonchai PJaiban PPhithakkitnukoon SKhunlertgit N(2018)EnergisProceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers10.1145/3267305.3274158(1033-1040)Online publication date: 8-Oct-2018
https://dl.acm.org/doi/10.1145/3267305.3274158

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