Social cost guarantees in smart route guidance
Social cost guarantees in smart route guidance
We model and study the problem of assigning traffic in an urban road network infrastructure. In our model, each driver submits their intended destination and is assigned a route to follow that minimizes the social cost (i.e., travel distance of all the drivers). We assume drivers are strategic and try to manipulate the system (i.e., misreport their intended destination and/or deviate from the assigned route) if they can reduce their travel distance by doing so. Such strategic behavior is highly undesirable as it can lead to an overall suboptimal traffic assignment and cause congestion. To alleviate this problem, we develop moneyless mechanisms that are resilient to manipulation by the agents and offer provable approximation guarantees on the social cost obtained by the solution. We then empirically test the mechanisms studied in the paper, showing that they can be effectively used in practice in order to compute manipulation resistant traffic allocations.
482-495
Serafino, Paolo
e0138324-dca1-4577-9af2-80583b80e1f4
Ventre, Carmine
9abfa84f-266a-4296-82f1-ae3bdecaea38
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Zhang, Jie
6bad4e75-40e0-4ea3-866d-58c8018b225a
An, Bo
4b0743f9-91c9-4452-868c-1d12b4e9f456
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30
30 August 2019
Serafino, Paolo
e0138324-dca1-4577-9af2-80583b80e1f4
Ventre, Carmine
9abfa84f-266a-4296-82f1-ae3bdecaea38
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Zhang, Jie
6bad4e75-40e0-4ea3-866d-58c8018b225a
An, Bo
4b0743f9-91c9-4452-868c-1d12b4e9f456
Jennings, Nick
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Serafino, Paolo, Ventre, Carmine, Tran-Thanh, Long, Zhang, Jie, An, Bo and Jennings, Nick
(2019)
Social cost guarantees in smart route guidance.
Nayak, A. and Sharma, A.
(eds.)
In PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019.
vol. 11671,
Springer Cham.
.
(doi:10.1007/978-3-030-29911-8_37).
Record type:
Conference or Workshop Item
(Paper)
Abstract
We model and study the problem of assigning traffic in an urban road network infrastructure. In our model, each driver submits their intended destination and is assigned a route to follow that minimizes the social cost (i.e., travel distance of all the drivers). We assume drivers are strategic and try to manipulate the system (i.e., misreport their intended destination and/or deviate from the assigned route) if they can reduce their travel distance by doing so. Such strategic behavior is highly undesirable as it can lead to an overall suboptimal traffic assignment and cause congestion. To alleviate this problem, we develop moneyless mechanisms that are resilient to manipulation by the agents and offer provable approximation guarantees on the social cost obtained by the solution. We then empirically test the mechanisms studied in the paper, showing that they can be effectively used in practice in order to compute manipulation resistant traffic allocations.
Text
Social Cost Guarantees in Smart Route Guidance
- Accepted Manuscript
More information
Accepted/In Press date: 3 June 2019
e-pub ahead of print date: 23 August 2019
Published date: 30 August 2019
Venue - Dates:
The 16th Pacific Rim International Conference on Artificial Intelligence, , Yanuca Island, Fiji, 2019-08-26 - 2019-08-30
Identifiers
Local EPrints ID: 435249
URI: http://eprints.soton.ac.uk/id/eprint/435249
PURE UUID: 1d3da788-b195-4360-b2e1-0278cf6b9219
Catalogue record
Date deposited: 28 Oct 2019 17:30
Last modified: 17 Mar 2024 05:12
Export record
Altmetrics
Contributors
Author:
Paolo Serafino
Author:
Carmine Ventre
Author:
Long Tran-Thanh
Author:
Jie Zhang
Author:
Bo An
Author:
Nick Jennings
Editor:
A. Nayak
Editor:
A. Sharma
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics