Authors:
Matej Petrouš
1
;
Evženie Suzdaleva
2
and
Ivan Nagy
1
Affiliations:
1
Department of Signal Processing, The Czech Academy of Sciences, Institute of Information Theory and Automation, Pod vodárenskou věží 4, 18208 Prague, Czech Republic, Faculty of Transportation Sciences, Czech Technical University, Na Florenci 25, 11000 Prague and Czech Republic
;
2
Department of Signal Processing, The Czech Academy of Sciences, Institute of Information Theory and Automation, Pod vodárenskou věží 4, 18208 Prague and Czech Republic
Keyword(s):
Mixture Estimation, Poisson Components, Passenger Demand.
Related
Ontology
Subjects/Areas/Topics:
Engineering Applications
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Robotics and Automation
;
Signal Processing, Sensors, Systems Modeling and Control
;
System Modeling
Abstract:
The paper deals with the problem of modeling the passenger demand in the tram transportation network. The
passenger demand on the individual tram stops is naturally influenced by the number of boarding and disembarking
passengers, whose measuring is expensive and therefore they should be modeled and predicted. A
mixture of Poisson components with the dynamic pointer estimated by recursive Bayesian estimation algorithms
is used to describe the mentioned variables, while their prediction is solved with the help of the Poisson
regression. The main contributions of the presented approach are: (i) the model of the number of boarding
and disembarking passengers; (ii) the real-time data incorporation into the model; (iii) the recursive estimation
algorithm with the normal approximation of the proximity function. The results of experiments with real data
and the comparison with theoretical counterparts are demonstrated.