extended-abstract

Can crowdsourcing create the missing crash data?

Authors:

Arianna LegoviniAuthors Info & Claims

COMPASS '20: Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies

Pages 305 - 306

https://doi.org/10.1145/3378393.3402264

Published: 01 July 2020 Publication History

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Abstract

UPDATED---June 1, 2020. Road traffic crashes (RTCs) are the primary cause of death among children and young adults. Yet data on RTCs is incomplete, hindering effective road safety policymaking in many developing countries where mortality is purportedly highest. We web-scrape 850,000 tweets to create crash data and develop a machine learning algorithm to geolocate RTCs. Our algorithm is nearly twice as precise as a standard geoparsing algorithm in identifying the set of locations that include the crash location. Above and beyond, it identifies the unique location of a crash from the set of possible locations in a majority of cases. We dispatch a set of motorcycle drivers to the site of the presumed crash in real time to verify the validity of the crowdsourced data and document the performance of the algorithm. The study can be used as a proof of concept for countries interested to improve RTC data at low cost through a machine learning approach and substantially increase the data available to analyze RTCs and prioritize road safety policies.

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Cited By

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Ibtissem KMarwen KNeila RSami F(2023)A road classification approach based on road traffic severity analysis2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA)10.1109/INISTA59065.2023.10310365(1-6)Online publication date: 20-Sep-2023
https://doi.org/10.1109/INISTA59065.2023.10310365
(2021)Creative reuses of data for greater valueWorld Development Report 2021: Data for Better Lives10.1596/978-1-4648-1600-0_ch4(121-149)Online publication date: 22-Jun-2021
https://doi.org/10.1596/978-1-4648-1600-0_ch4
(2021)Making roads safer by repurposing private intent traffic dataWorld Development Report 2021: Data for Better Lives10.1596/978-1-4648-1600-0_Spotlight4_2(152-153)Online publication date: 22-Jun-2021
https://doi.org/10.1596/978-1-4648-1600-0_Spotlight4_2

Index Terms

Can crowdsourcing create the missing crash data?
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms

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Published In

COMPASS '20: Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies

June 2020

359 pages

ISBN:9781450371292

DOI:10.1145/3378393

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 01 July 2020

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COMPASS '20

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COMPASS '20: ACM SIGCAS Conference on Computing and Sustainable Societies

June 15 - 17, 2020

Ecuador

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Overall Acceptance Rate 25 of 50 submissions, 50%

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Cited By

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Ibtissem KMarwen KNeila RSami F(2023)A road classification approach based on road traffic severity analysis2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA)10.1109/INISTA59065.2023.10310365(1-6)Online publication date: 20-Sep-2023
https://doi.org/10.1109/INISTA59065.2023.10310365
(2021)Creative reuses of data for greater valueWorld Development Report 2021: Data for Better Lives10.1596/978-1-4648-1600-0_ch4(121-149)Online publication date: 22-Jun-2021
https://doi.org/10.1596/978-1-4648-1600-0_ch4
(2021)Making roads safer by repurposing private intent traffic dataWorld Development Report 2021: Data for Better Lives10.1596/978-1-4648-1600-0_Spotlight4_2(152-153)Online publication date: 22-Jun-2021
https://doi.org/10.1596/978-1-4648-1600-0_Spotlight4_2

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Analyzing influencing factors of crash injury severity incorporating FARS data

Artificial intelligence techniques for driving safety and vehicle crash prediction

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