{"title":"Multiday evaluation of adaptive traffic signal system based on license plate recognition detector data","authors":"Ruimin Li, Fan Yang, Shi Lin","doi":"10.1049/pbtr026e_ch13","DOIUrl":"https://doi.org/10.1049/pbtr026e_ch13","url":null,"abstract":"This study evaluates the performance of an adaptive traffic signal control system (ATSCS) based on license plate recognition (LPR) detector data. The LPR detector data can provide abundant individual vehicle-based information for comprehensive, detailed evaluation of traffic signal control. Several measurements including travel time delay, cumulative travel time frequency diagram, Purdue coordination diagram, 95th percentile travel time, and buffer index are applied to reveal the various aspects of traffic signal control performance. A before and after comparison was conducted in the eastbound direction of a road segment, in which the ATSCS was deployed in October 2016 while the time-of-day traffic signal planning was used previously. Results show the improvement in traffic condition in the morning and evening peaks after the deployment of the ATSCS. However, the traffic condition at midnight worsened on certain days after the deployment of the ATSCS.","PeriodicalId":218837,"journal":{"name":"Traffic Information and Control","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115706745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Short-term traffic prediction under disruptions using deep learning","authors":"Yanjie Dong, Fangce Guo, A. Sivakumar, J. Polak","doi":"10.1049/pbtr026e_ch5","DOIUrl":"https://doi.org/10.1049/pbtr026e_ch5","url":null,"abstract":"In this chapter, we have proposed a novel graph -based model with TS-TGAT to predict short-term traffic speed under both normal and abnormal traffic fl ow conditions. The novelty of the proposed prediction model is that it can learn both spatial and temporal propagation rules for traffic on a network. Important concepts and improvements are introduced to the model, for example node -level attention weights, multi -head attention and depth -wise separable CNN module to take account of the unique and complex interactions between traffic fl ows and traffic network characteristics. The proposed prediction model was trained and tested using ILDs on a section of the M25 motorway network just before the Dartford Crossing (between Dartford Tunnel and M25 J2 with all slip roads). In order to make the model generic and reusable, the model was trained using generic data (including both normal and abnormal traffic fl ow data) and was tested under mixed conditions and disrupted conditions. A selection of baseline methods was used to benchmark the proposed model performance, including HA, kNN, GBDTs and LSTM, some of which are state-of-the-art methods in the problem of short-term traffic prediction. The results have shown that the proposed TS-TGAT method outperforms other benchmarking methods under both normal and abnormal traffic conditions.","PeriodicalId":218837,"journal":{"name":"Traffic Information and Control","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132021846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Algorithms and models for signal coordination","authors":"","doi":"10.1049/pbtr026e_ch10","DOIUrl":"https://doi.org/10.1049/pbtr026e_ch10","url":null,"abstract":"","PeriodicalId":218837,"journal":{"name":"Traffic Information and Control","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133131382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Conclusion","authors":"","doi":"10.1049/pbtr026e_ch14","DOIUrl":"https://doi.org/10.1049/pbtr026e_ch14","url":null,"abstract":"","PeriodicalId":218837,"journal":{"name":"Traffic Information and Control","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127058737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}