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Hybrid human machine workflows for mobility management

Hybrid human machine workflows for mobility management
Hybrid human machine workflows for mobility management

Sustainable mobility is one of the main goals of both European and United Nations plans for 2030. The concept of Smart Cities has arisen as a way to achieve this goal by leveraging IoT interconnected devices to collect and analyse large quantities of data. However, several works have pointed out the importance of including the human factor, and in particular, citizens, to make sense of the collected data and ensure their engagement along the data value chain. This paper presents the design and implementation of two end-to-end hybrid human-machine workflows for solving two mobility problems: modal split estimation, and mapping mobility infrastructure. For modal split, we combine the use of i-Log, an app to collect data and interact with citizens, with reinforcement learning classifiers to continuously improve the accuracy of the classification, aiming at reducing the required interactions from citizens. For mobility infrastructure, we developed a system that uses remote crowdworkers to explore the city looking for Points of Interest, that is more scalable than sending agents on the field. Crowdsourced maps are then fused with existing maps (if available) to create a final map that then is validated on the field by citizens engaged through the i-Log app.

Crowdsourcing, Hybrid workflows, Map generation, Modal split
102-109
ACM
Maddalena, Eddy
397dbaba-4363-4c11-8e52-4a7ba4df4bae
Zeni, Mattia
6c2787fd-9447-496b-b645-e383dd231652
Ibanez, Luis Daniel
65a2e20b-74a9-427d-8c4c-2330285153ed
Song, Donglei
24400f91-6b03-4e28-9901-decd03bf2896
Simperl, Elena
40261ae4-c58c-48e4-b78b-5187b10e4f67
Gomer, Richard
71c5969f-2da0-47ab-b2fb-a7e1d07836b1
Giunchiglia, Fausto
7f9d6117-1198-47f5-bb71-9ef8effdca90
Liu, Ling
White, Ryen
Maddalena, Eddy
397dbaba-4363-4c11-8e52-4a7ba4df4bae
Zeni, Mattia
6c2787fd-9447-496b-b645-e383dd231652
Ibanez, Luis Daniel
65a2e20b-74a9-427d-8c4c-2330285153ed
Song, Donglei
24400f91-6b03-4e28-9901-decd03bf2896
Simperl, Elena
40261ae4-c58c-48e4-b78b-5187b10e4f67
Gomer, Richard
71c5969f-2da0-47ab-b2fb-a7e1d07836b1
Giunchiglia, Fausto
7f9d6117-1198-47f5-bb71-9ef8effdca90
Liu, Ling
White, Ryen

Maddalena, Eddy, Zeni, Mattia, Ibanez, Luis Daniel, Song, Donglei, Simperl, Elena, Gomer, Richard and Giunchiglia, Fausto (2019) Hybrid human machine workflows for mobility management. Liu, Ling and White, Ryen (eds.) In The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019. ACM. pp. 102-109 . (doi:10.1145/3308560.3317056).

Record type: Conference or Workshop Item (Paper)

Abstract

Sustainable mobility is one of the main goals of both European and United Nations plans for 2030. The concept of Smart Cities has arisen as a way to achieve this goal by leveraging IoT interconnected devices to collect and analyse large quantities of data. However, several works have pointed out the importance of including the human factor, and in particular, citizens, to make sense of the collected data and ensure their engagement along the data value chain. This paper presents the design and implementation of two end-to-end hybrid human-machine workflows for solving two mobility problems: modal split estimation, and mapping mobility infrastructure. For modal split, we combine the use of i-Log, an app to collect data and interact with citizens, with reinforcement learning classifiers to continuously improve the accuracy of the classification, aiming at reducing the required interactions from citizens. For mobility infrastructure, we developed a system that uses remote crowdworkers to explore the city looking for Points of Interest, that is more scalable than sending agents on the field. Crowdsourced maps are then fused with existing maps (if available) to create a final map that then is validated on the field by citizens engaged through the i-Log app.

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More information

e-pub ahead of print date: 13 May 2019
Venue - Dates: 2019 World Wide Web Conference, WWW 2019, San Francisco, United States, 2019-05-13 - 2019-05-17
Keywords: Crowdsourcing, Hybrid workflows, Map generation, Modal split

Identifiers

Local EPrints ID: 432302
URI: https://eprints.soton.ac.uk/id/eprint/432302
PURE UUID: 113dbe84-9066-4f13-97b4-bb6eba8e9441
ORCID for Elena Simperl: ORCID iD orcid.org/0000-0003-1722-947X

Catalogue record

Date deposited: 09 Jul 2019 16:30
Last modified: 19 Jul 2019 10:05

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Contributors

Author: Eddy Maddalena
Author: Mattia Zeni
Author: Luis Daniel Ibanez
Author: Donglei Song
Author: Elena Simperl ORCID iD
Author: Richard Gomer
Author: Fausto Giunchiglia
Editor: Ling Liu
Editor: Ryen White

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