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Analysis of Approaches to Short Term Passenger Volume Prediction in Public Transport

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2015
Authors:
Abhijeet Shingade, Adesh Atole, Piyush Galphat, Shashank Dharmadhikari, Bhushan Thakare
10.5120/ijca2015907434

Abhijeet Shingade, Adesh Atole, Piyush Galphat, Shashank Dharmadhikari and Bhushan Thakare. Article: Analysis of Approaches to Short Term Passenger Volume Prediction in Public Transport. International Journal of Computer Applications 131(10):34-38, December 2015. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Abhijeet Shingade and Adesh Atole and Piyush Galphat and Shashank Dharmadhikari and Bhushan Thakare},
	title = {Article: Analysis of Approaches to Short Term Passenger Volume Prediction in Public Transport},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {131},
	number = {10},
	pages = {34-38},
	month = {December},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Public Transport systems form an integral part in development of city. The development of the city can be correlated to the proportion of its population adopting public transport as its primary mode of transport. For organizations, which provide public transport services in a city, it will be beneficial to have real-time intelligent scheduling and dispatching system. To have a functional intelligent scheduling system, it is necessary to build a passenger flow prediction system, which predicts the flow of passengers based on historical data and environmental conditions. This paper presents various approaches for transit passenger volume prediction, merits and demerits of each.

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Keywords

forecasting, grey model, interactive multiple model, neural networks, public transport, support vector machines