TY - GEN

T1 - Marginal cost pricing with a fixed error factor in traffic networks

AU - Sharon, Guni

AU - Boyles, Stephen D.

AU - Alkoby, Shani

AU - Stone, Peter

N1 - Publisher Copyright:
© 2019 International Foundation for Autonomous Agents and Multiagent Systems. All rights reserved.

PY - 2019

Y1 - 2019

N2 - It is well known that charging marginal cost tolls (MCT) from self interested agents participating in a congestion game leads to optimal system performance, i.e., minimal total latency. However, it is not generally possible to calculate the correct marginal costs tolls precisely, and it is not known what the impact is of charging incorrect tolls. This uncertainty could lead to reluctance to adopt such schemes in practice. This paper studies the impact of charging MCT with some fixed factor error on the system's performance. We prove that under-estimating MCT results in a system performance that is at least as good as that obtained by not applying tolls at all. This result might encourage adoption of MCT schemes with conservative MCT estimations. Furthermore, we prove that no local extrema can exist in the function mapping the error value, r, to the system's performance, T(r). This result implies that accurately calibrating MCT for a given network can be done by identifying an extremum in T(r) which, consequently, must be the global optimum. Experimental results from simulating several large-scale, real-life traffic networks are presented and provide further support for our theoretical findings.

AB - It is well known that charging marginal cost tolls (MCT) from self interested agents participating in a congestion game leads to optimal system performance, i.e., minimal total latency. However, it is not generally possible to calculate the correct marginal costs tolls precisely, and it is not known what the impact is of charging incorrect tolls. This uncertainty could lead to reluctance to adopt such schemes in practice. This paper studies the impact of charging MCT with some fixed factor error on the system's performance. We prove that under-estimating MCT results in a system performance that is at least as good as that obtained by not applying tolls at all. This result might encourage adoption of MCT schemes with conservative MCT estimations. Furthermore, we prove that no local extrema can exist in the function mapping the error value, r, to the system's performance, T(r). This result implies that accurately calibrating MCT for a given network can be done by identifying an extremum in T(r) which, consequently, must be the global optimum. Experimental results from simulating several large-scale, real-life traffic networks are presented and provide further support for our theoretical findings.

KW - Congestion games

KW - Flow optimization

KW - Marginal-cost pricing

KW - Routing games

KW - Traffic flow

UR - http://www.scopus.com/inward/record.url?scp=85076933396&partnerID=8YFLogxK

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AN - SCOPUS:85076933396

T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS

SP - 1539

EP - 1546

BT - 18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019

T2 - 18th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2019

Y2 - 13 May 2019 through 17 May 2019

ER -