Social Cost Guarantees in Smart Route Guidance

Paolo Serafino, Carmine Ventre, Long Tran-thanh, Jie Zhang, Bo An, Nick Jennings

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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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.
Original languageEnglish
Title of host publicationPacific Rim International Conference on Artificial Intelligence
Subtitle of host publicationTrends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Proceedings
EditorsAbhaya C. Nayak, Alok Sharma
Pages482-495
Number of pages14
ISBN (Electronic)9783030299118
DOIs
Publication statusE-pub ahead of print - 23 Aug 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11671 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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