Efficient macroscopic urban traffic models for reducing congestion: A PDDL+ planning approach

M. Vallati, D. Magazzeni, Bart De Schutter, L. Chrpa, T.L. McCluskey

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

47 Citations (Scopus)

Abstract

The global growth in urbanisation increases the demand for services including road transport infrastructure, presenting challenges in terms of mobility. In this scenario, optimising the exploitation of urban road networks is a pivotal challenge. Existing urban traffic control approaches, based on complex mathematical models, can effectively deal with planned-ahead events, but are not able to cope with unexpected situations –such as roads blocked due to car accidents or weather-related events– because of their huge computational requirements. Therefore, such unexpected situations are mainly dealt with manually, or by exploiting pre-computed policies.
Our goal is to show the feasibility of using mixed discrete-continuous planning to deal with unexpected circumstances in urban traffic control. We present a PDDL+ formulation of urban traffic control, where continuous processes are used to model flows of cars, and show how planning can be used to efficiently reduce congestion of specified roads by controlling traffic light green phases. We present simulation results on two networks (one of them considers Manchester city centre) that demonstrate the effectiveness of the approach, compared with fixed-time and reactive techniques.
Original languageEnglish
Title of host publicationProceedings of the 13th AAAI Conference on Artificial Intelligence
Subtitle of host publicationAAAI-16
EditorsDale Schuurmans, Michael Wellman
Place of PublicationPalo Alto, CA, USA
PublisherAmerican Association for Artificial Intelligence (AAAI)
Pages3188-3194
Publication statusPublished - 2016
Event13th AAAI Conference on Artificial Intelligence - Phoenix, United States
Duration: 12 Feb 201617 Feb 2016

Conference

Conference13th AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI-16
CountryUnited States
CityPhoenix
Period12/02/1617/02/16

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