{"title":"每日动态分配的移动平均与指数平滑成本更新滤波器:定点稳定性和分岔理论分析","authors":"G.E. CANTARELLA , C. FIORI , P. VELONÀ","doi":"10.1016/j.trb.2025.103253","DOIUrl":null,"url":null,"abstract":"<div><div>Deterministic process (DP) models for day-to-day dynamic assignment can be cast in the general two-equation assignment modelling approach, including the following:</div><div>- the arc cost updating recursive equation in the case of day-to-day dynamic assignment; instances are exponential smoothing (ES) or moving average (MA) filters;</div><div>- the arc flow updating recursive equation in the case of day-to-day dynamic assignment; instances are ES filters.</div><div>Even though ES filters for cost updating may well approximate MA filters, somebody in the scientific community argue against the underlying hypothesis of infinite memory for ES filters with respect to MA ones; numerical results support significant differences for small memory depths, say 2 or 3 days.</div><div>The main original contribution of this study is a formal fixed-point stability and bifurcation analysis of MA-ES DP models with memory depth 2, and a comparison with ES-ES DP. At this aim the Omega method 2.0, suitable for carrying out general fixed-point stability and bifurcation analysis has been developed and discussed. Extremely long proofs have not been included for brevity. This study focused on methodological aspects; thus, numerical examples were not included.</div></div>","PeriodicalId":54418,"journal":{"name":"Transportation Research Part B-Methodological","volume":"199 ","pages":"Article 103253"},"PeriodicalIF":6.3000,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Moving average vs. exponential smoothing cost-updating filters for day-to-day dynamic assignment: fixed-point stability and bifurcation theoretical analysis\",\"authors\":\"G.E. CANTARELLA , C. FIORI , P. VELONÀ\",\"doi\":\"10.1016/j.trb.2025.103253\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Deterministic process (DP) models for day-to-day dynamic assignment can be cast in the general two-equation assignment modelling approach, including the following:</div><div>- the arc cost updating recursive equation in the case of day-to-day dynamic assignment; instances are exponential smoothing (ES) or moving average (MA) filters;</div><div>- the arc flow updating recursive equation in the case of day-to-day dynamic assignment; instances are ES filters.</div><div>Even though ES filters for cost updating may well approximate MA filters, somebody in the scientific community argue against the underlying hypothesis of infinite memory for ES filters with respect to MA ones; numerical results support significant differences for small memory depths, say 2 or 3 days.</div><div>The main original contribution of this study is a formal fixed-point stability and bifurcation analysis of MA-ES DP models with memory depth 2, and a comparison with ES-ES DP. At this aim the Omega method 2.0, suitable for carrying out general fixed-point stability and bifurcation analysis has been developed and discussed. Extremely long proofs have not been included for brevity. This study focused on methodological aspects; thus, numerical examples were not included.</div></div>\",\"PeriodicalId\":54418,\"journal\":{\"name\":\"Transportation Research Part B-Methodological\",\"volume\":\"199 \",\"pages\":\"Article 103253\"},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2025-06-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Research Part B-Methodological\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S019126152500102X\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part B-Methodological","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S019126152500102X","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
Moving average vs. exponential smoothing cost-updating filters for day-to-day dynamic assignment: fixed-point stability and bifurcation theoretical analysis
Deterministic process (DP) models for day-to-day dynamic assignment can be cast in the general two-equation assignment modelling approach, including the following:
- the arc cost updating recursive equation in the case of day-to-day dynamic assignment; instances are exponential smoothing (ES) or moving average (MA) filters;
- the arc flow updating recursive equation in the case of day-to-day dynamic assignment; instances are ES filters.
Even though ES filters for cost updating may well approximate MA filters, somebody in the scientific community argue against the underlying hypothesis of infinite memory for ES filters with respect to MA ones; numerical results support significant differences for small memory depths, say 2 or 3 days.
The main original contribution of this study is a formal fixed-point stability and bifurcation analysis of MA-ES DP models with memory depth 2, and a comparison with ES-ES DP. At this aim the Omega method 2.0, suitable for carrying out general fixed-point stability and bifurcation analysis has been developed and discussed. Extremely long proofs have not been included for brevity. This study focused on methodological aspects; thus, numerical examples were not included.
期刊介绍:
Transportation Research: Part B publishes papers on all methodological aspects of the subject, particularly those that require mathematical analysis. The general theme of the journal is the development and solution of problems that are adequately motivated to deal with important aspects of the design and/or analysis of transportation systems. Areas covered include: traffic flow; design and analysis of transportation networks; control and scheduling; optimization; queuing theory; logistics; supply chains; development and application of statistical, econometric and mathematical models to address transportation problems; cost models; pricing and/or investment; traveler or shipper behavior; cost-benefit methodologies.