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Analytics on public transport delays with spatial big data

Published: 31 October 2016 Publication History

Abstract

The increasing pervasiveness of location-aware technologies is leading to the rise of large, spatio-temporal datasets and to the opportunity of discovering usable knowledge about the behaviors of people and objects. Applied extensively in transportation, spatial big data and its analytics can deliver useful insights on a number of different issues such as congestion, delays, public transport reliability and so on. Predominantly studied for its use in operational management, spatial big data can be used to provide insight in strategic applications as well, from planning and design to evaluation and management. Such large scale, streaming spatial big data can be used in the improvement of public transport, for example the design of public transport networks and reliability. In this paper, we analyze GTFS data from the cities of Stockholm and Rome to gain insight on the sources and factors influencing public transport delays in the cities. The analysis is performed on a combination of GTFS data with data from other sources. The paper points to key issues in the analysis of real time data, driven by the contextual setting in the two cities.

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Cited By

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  • (2022)Analysis of Spatio-Temporal Patterns of Public Bus Delay Times Using GTFS Data:A Case Study of Buses of Sendai City Transportation BureauGTFSデータを用いた公共バスの遅延時間に関する時空間パターンの分析── 仙台市営バスを事例に ──Quarterly Journal of Geography10.5190/tga.73.4_26473:4(264-273)Online publication date: 2022
  • (2022)Streaming Detection of Significant Delay Changes in Public Transport SystemsComputational Science – ICCS 202210.1007/978-3-031-08760-8_41(486-499)Online publication date: 15-Jun-2022
  • (2021)gtfs2vecProceedings of the 1st ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data10.1145/3486640.3491392(5-12)Online publication date: 2-Nov-2021
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Published In

cover image ACM Other conferences
BigSpatial '16: Proceedings of the 5th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
October 2016
65 pages
ISBN:9781450345811
DOI:10.1145/3006386
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: 31 October 2016

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Author Tags

  1. big data
  2. decision making
  3. public transport

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  • Research-article

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SIGSPATIAL'16

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BigSpatial '16 Paper Acceptance Rate 8 of 14 submissions, 57%;
Overall Acceptance Rate 32 of 58 submissions, 55%

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Cited By

View all
  • (2022)Analysis of Spatio-Temporal Patterns of Public Bus Delay Times Using GTFS Data:A Case Study of Buses of Sendai City Transportation BureauGTFSデータを用いた公共バスの遅延時間に関する時空間パターンの分析── 仙台市営バスを事例に ──Quarterly Journal of Geography10.5190/tga.73.4_26473:4(264-273)Online publication date: 2022
  • (2022)Streaming Detection of Significant Delay Changes in Public Transport SystemsComputational Science – ICCS 202210.1007/978-3-031-08760-8_41(486-499)Online publication date: 15-Jun-2022
  • (2021)gtfs2vecProceedings of the 1st ACM SIGSPATIAL International Workshop on Searching and Mining Large Collections of Geospatial Data10.1145/3486640.3491392(5-12)Online publication date: 2-Nov-2021
  • (2021)Spatial data mining of public transport incidents reported in social mediaProceedings of the 14th ACM SIGSPATIAL International Workshop on Computational Transportation Science10.1145/3486629.3490696(1-8)Online publication date: 2-Nov-2021
  • (2018)Predicting MRT Trips in Singapore by Creating a Mobility Behavior Model Based on GSM Data2018 IEEE International Conference on Data Mining Workshops (ICDMW)10.1109/ICDMW.2018.00098(632-639)Online publication date: Nov-2018
  • (2018)Big Data Analytics: A Comparison of Tools and ApplicationsInnovations in Smart Cities and Applications10.1007/978-3-319-74500-8_54(587-601)Online publication date: 21-Mar-2018
  • (2017)GTFS-VizProceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 ACM International Symposium on Wearable Computers10.1145/3123024.3124415(388-396)Online publication date: 11-Sep-2017

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