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Article

APPRAISING THE STRONGLY ASSOCIATED IMPACTS TO CHOOSE THE MODE OF PUBLIC TRANSPORT: A CASE STUDY OF COLOMBO METROPOLITAN AREA IN SRI LANKA

DOI: 10.7708/ijtte.2016.6(1).06


6 / 1 / 63-76 Pages

Author(s)

Manjula Madhuwanthi - Department of Information Science and Control Engineering, Graduate School of Engineering - Doctoral Program, Nagaoka University of Technology, Nagaoka, 940-2137, Japan -

Ashuboda Marasinghe - Department of Information Science and Control Engineering, Graduate School of Engineering - Doctoral Program, Nagaoka University of Technology, Nagaoka, 940-2137, Japan -

Shusaku Nomura - Department of Information Science and Control Engineering, Graduate School of Engineering - Doctoral Program, Nagaoka University of Technology, Nagaoka, 940-2137, Japan -


Abstract

This study examines a model to evaluate the probability of choosing the mode of public transport with finding most significant aspects related to the characteristic of the journey, characteristic of the traveler and the personal behavior of the traveler. The study area was focused on eleven Divisional Secretariat Divisions of the Colombo Metropolitan Area in the Western Province of Sri Lanka. This area has the most economically advanced functions as the commercial capital of Sri Lanka. The current transportation sector in the focusing area has impact of increasing vehicle ownership and serious inadequacies in the road network such as traffic congestions, shortfall of road capacity and low speed level on road. Hence this study attempts to encourage people for the public transport by focusing about their perception related to the mode choice as a solution for the above issues. The results showed that “Number of Earning Members”, “Vehicle Ownership”, ”Education”, “Age”, “Gender”, “Occupation”, “Trip Distance.”, “Trip Time”, “Total Cost” and “Safety” of the mode were the most significant factors for affecting to choose the public transport. The obtained logistic model with the significant variables had the 78.4% of accuracy for the prediction of probability in using public transport.


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