Smart data re-sampling for bus fleet management

Author

Peripimeno, Angelo ; Anguita, Davide ; Chiappini, Piero

Author_Institution

Dept. of Biophys. & Electron. Eng., Genoa Univ., Italy

fYear

2004

fDate

14-17 June 2004

Firstpage

178

Lastpage

181

Abstract

In this paper we focus on bus fleets and propose an application of artificial intelligence (transductive inference for function estimation) which utilizes data from the vehicle tracking system in order to enforce the schedule monitoring of the bus and thus providing more accurate information for decision making activities. This is achieved by estimating the time of arrivals and departures of the buses at certain points of the journey (main bus stops, interchange points, crossroads) which are crucial for the management of the fleet.

Keywords

artificial intelligence; decision making; estimation theory; inference mechanisms; interpolation; regression analysis; sampling methods; tracking; traffic information systems; transportation; arrival time estimation; artificial intelligence; bus fleet management; bus schedule monitoring; decision making; departure time estimation; function estimation; interpolation; regression analysis; smart data resampling; transductive inference; vehicle tracking system; Artificial intelligence; Automotive engineering; Consumer electronics; Decision making; Electric breakdown; Frequency estimation; Intelligent vehicles; Monitoring; Road transportation; Scheduling;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Vehicles Symposium, 2004 IEEE

Print_ISBN

0-7803-8310-9

Type

conf

DOI

10.1109/IVS.2004.1336377

Filename

1336377