Paper Info
Title
From data to knowledge: city-wide traffic flows analysis and prediction using bing maps
Abstract
Traffic jam is a common contemporary society issue in urban areas. City-wide traffic modeling, visualization, analysis, and prediction are still challenges in this context. Based on Bing Maps information, this work aims to acquire, aggregate, analyze, visualize, and predict traffic jam. Chicago area was evaluated as case study. The flow intensity (free or congested) was analyzed to allow the identification of phase transitions (shocks in the system). Also, a prediction model was developed based on logistic regression to correct discovery future flow intensities for a target street.
Year
DOI
Venue
2013
10.1145/2505821.2505831
UrbComp@KDD
Keywords
Field
DocType
city-wide traffic,discovery future flow intensity,bing maps information,traffic jam,logistic regression,common contemporary society issue,chicago area,city-wide traffic modeling,bing map,case study,prediction model,flow intensity,visualization,analysis
Data mining,Knowledge City,Contemporary society,Computer science,Visualization,Urban computing,Artificial intelligence,Traffic prediction,Logistic regression,Machine learning
Conference
Citations 
PageRank 
References 
8
0.57
7
Authors
5
Name
Order
Citations
PageRank
Anna Izabel Ribeiro1133.41
Fatima de L. P. Duarte-Figueiredo2216.36
Renato Assunção3738.69
Juliana P. Salles4705.26
Antonio Loureiro52406197.77