Anas El Kouaiti, Francesco Percassi, A. Saetti, T. McCluskey, M. Vallati
{"title":"Deployable Yet Effective Traffic Signal Optimisation via Automated Planning (Extended Abstract)","authors":"Anas El Kouaiti, Francesco Percassi, A. Saetti, T. McCluskey, M. Vallati","doi":"10.1609/socs.v17i1.31575","DOIUrl":null,"url":null,"abstract":"The use of planning techniques in traffic signal optimisation has proven effective in managing unexpected traffic conditions as well as typical traffic patterns. However, significant challenges concerning the deployability of generated signal plans remain, as planning systems need to consider constraints and features of the actual real-world infrastructure on which they will be implemented. \n\nTo address this challenge, we introduce a range of PDDL+ models embodying technological requirements as well as insights from domain experts. The proposed models have been extensively tested on historical data using a range of well-known search strategies and heuristics, as well as alternative encodings. Results demonstrate their competitiveness with the state of the art.","PeriodicalId":425645,"journal":{"name":"Symposium on Combinatorial Search","volume":"16 8","pages":"269-270"},"PeriodicalIF":0.0000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Symposium on Combinatorial Search","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1609/socs.v17i1.31575","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The use of planning techniques in traffic signal optimisation has proven effective in managing unexpected traffic conditions as well as typical traffic patterns. However, significant challenges concerning the deployability of generated signal plans remain, as planning systems need to consider constraints and features of the actual real-world infrastructure on which they will be implemented.
To address this challenge, we introduce a range of PDDL+ models embodying technological requirements as well as insights from domain experts. The proposed models have been extensively tested on historical data using a range of well-known search strategies and heuristics, as well as alternative encodings. Results demonstrate their competitiveness with the state of the art.