On improving operational planning and control in Public Transportation Networks using streaming Data: A Machine Learning Approach

• Type: Article in International Conference Proceedings Book
• Date: 2014

Authors

Luís Moreira Matias (Author) FEUP; João Mendes Moreira (Author) FEUP; João Gama (Author) FEP; Michel Ferreira (Author) FCUP

Conference proceedings International

Title: ECML/PKDD 2014 PhD Session

Pages: 41-50

ECML/PKDD 2014 PhD Session

Nancy, France, 15 a 19 de Setembro de 2014

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

Other information

Abstract (EN): Nowadays, transportation vehicles are equipped with intelligent sensors. Together, they form collaborative networks that broadcast real-time data about mobility patterns in urban areas. Online intelligent transportation systems for taxi dispatching, time-saving route finding or automatic vehicle location are already exploring such information in the taxi/buses transport industries. In this PhD spotlight paper, the authors present two ML applications focused on improving the operation of Public Transportation (PT) systems: 1) Bus Bunching (BB) Online Detection and 2) Taxi-Passenger Demand Prediction. By doing so, we intend to give a brief overview of the type of approaches applicable to these type of problems. Our frameworks are straightforward. By employing online learning frameworks we are able to use both historical and real-time data to update the inference models. The results are promising.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11