e27a2c063b9d59cf84c2d3adfd2506b2 applsci1404432.pdf 39db6b1ec8359503f1da8c923264ec343be2ecf9 applsci1404432.pdf 92ba94d9ea7b14103f7d43e9f077800bfcc2755925a3c705feb666b5a57c72bc applsci1404432.pdf Title: Predictive Analyses of Traffic Level in the City of Barcelona: From ARIMA to eXtreme Gradient Boosting Subject: This study delves into the intricate dynamics of urban mobility, a pivotal aspect for policymakers, businesses, and communities alike. By deciphering patterns of movement within a city, stakeholders can craft targeted interventions to mitigate traffic congestion peaks, optimizing both resource allocation and individual travel routes. Focused on Barcelona, Spain, this paper draws on data sourced from the city council's open data service. Through a blend of exploratory analysis, visualization techniques, and modeling methodologies—including time series analysis and the eXtreme Gradient Boosting (XGBoost) algorithm—the research endeavors to forecast traffic conditions. Additionally, a study of variable importance is carried out, and Shapley Additive Explanations are applied to enhance the interpretability of model outputs. Findings underscore the limitations of traditional forecasting methods in capturing the nuanced spatial and temporal dependencies present in traffic flows, particularly over medium- to long-term horizons. However, the XGBoost model demonstrates robust performance, with the area under ROC curves consistently exceeding 80%, indicating its efficacy in handling non-linear traffic data variables. Keywords: traffic level; eXtreme Gradient Boosting; forecasting; mobility; open data Author: Eloi Garcia, Laura Calvet, Patricia Carracedo, Carles Serrat, Pau Miró and Mohammad Peyman Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25 CreationDate: Thu May 23 12:59:12 2024 CEST ModDate: Thu May 23 13:17:00 2024 CEST Custom Metadata: no Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 18 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 3828201 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- NXXYVP+URWPalladioL-Bold Type 1 Custom yes yes yes 10 0 LQILCQ+URWPalladioL-Roma Type 1 Custom yes yes yes 16 0 CDWXIO+URWPalladioL-Ital Type 1 Custom yes yes yes 21 0 CRVMKC+TimesNewRomanPS-BoldItalicMT Type 1C WinAnsi yes yes no 37 0 MUFUZY+Cambria CID TrueType Identity-H yes yes yes 63 0 OWPBDO+PazoMath-Italic Type 1 Builtin yes yes yes 86 0 SOSTRQ+CMR10 Type 1 Builtin yes yes yes 91 0 TEWTSE+CMSY10 Type 1 Builtin yes yes yes 96 0 UVMIOG+URWPalladioL-BoldItal Type 1 Custom yes yes yes 101 0 LNLYMK+PazoMath Type 1 Builtin yes yes yes 106 0 QDTWCG+MSBM10 Type 1 Builtin yes yes yes 111 0 OBYGUJ+CMEX10 Type 1 Builtin yes yes yes 119 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2024-10-22 02:42:09 CEST RepresentationInformation: applsci1404432.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2024-10-16 11:57:12 CEST Size: 3828201 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 323 FreeObjects: 8 IncrementalUpdates: 0 DocumentCatalog: PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Destination: section.1 Item: Title: Related Work Destination: section.2 Children: Item: Title: Traffic Flow Prediction Destination: subsection.2.1 Item: Title: Traffic Models in the City of Barcelona Destination: subsection.2.2 Item: Title: Methodology Destination: section.3 Children: Item: Title: Visualization Techniques Destination: subsection.3.1 Item: Title: Time Series Analysis Destination: subsection.3.2 Item: Title: eXtreme Gradient Boosting Destination: subsection.3.3 Children: Item: Title: Evaluation Metrics Destination: subsubsection.3.3.1 Item: Title: Shapley Additive Explanations Destination: subsubsection.3.3.2 Item: Title: Variable Analysis Destination: subsection.3.4 Item: Title: Application Destination: section.4 Children: Item: Title: Description of the Dataset Destination: subsection.4.1 Item: Title: Visualization Techniques Destination: subsection.4.2 Item: Title: Time Series Analysis Destination: subsection.4.3 Item: Title: eXtreme Gradient Boosting Destination: subsection.4.4 Item: Title: Discussion Destination: section.5 Item: Title: Conclusions and Further Research Destination: section.6 Item: Title: References Destination: section.7 Info: Title: Predictive Analyses of Traffic Level in the City of Barcelona: From ARIMA to eXtreme Gradient Boosting Author: Eloi Garcia, Laura Calvet, Patricia Carracedo, Carles Serrat, Pau Miró and Mohammad Peyman Subject: This study delves into the intricate dynamics of urban mobility, a pivotal aspect for policymakers, businesses, and communities alike. By deciphering patterns of movement within a city, stakeholders can craft targeted interventions to mitigate traffic congestion peaks, optimizing both resource allocation and individual travel routes. Focused on Barcelona, Spain, this paper draws on data sourced from the city council's open data service. Through a blend of exploratory analysis, visualization techniques, and modeling methodologies including time series analysis and the eXtreme Gradient Boosting (XGBoost) algorithm the research endeavors to forecast traffic conditions. Additionally, a study of variable importance is carried out, and Shapley Additive Explanations are applied to enhance the interpretability of model outputs. Findings underscore the limitations of traditional forecasting methods in capturing the nuanced spatial and temporal dependencies present in traffic flows, particularly over medium- to long-term horizons. However, the XGBoost model demonstrates robust performance, with the area under ROC curves consistently exceeding 80%, indicating its efficacy in handling non-linear traffic data variables. 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