Work

Passenger Flows Estimation of Light Rail Transit (LRT) System in Izmir, Turkey Using Multiple Regression and ANN Methods

Year: 1970

Type: article

Abstract: Passenger flow estimation of transit systems is essential for new decisions about additional facilities and feeder lines. For increasing the efficiency of an existing transit line, stations which are insufficient for trip production and attraction should be examined first. Such investigation supports decisions for feeder line projects which may seem necessary or futile according to the findings. In this study, passenger flow of a light rail transit (LRT) system in Izmir, Turkey is estimated by using multiple regression and feed-forward back-propagation type of artificial neural networks (ANN). The number of alighting passengers at each station is estimated as a function of boarding passengers from other stations. It is found that ANN approach produced significantly better estimations specifically for the low passenger attractive stations. In addition, ANN is found to be more capable for the determination of trip-attractive parts of LRT lines. Keywords: light rail transit, multiple regression, artificial neural networks, public transportation

Source: PROMET - Traffic&Transportation

DOI: 10.7307/ptt.v24i1.264

Authors: Mustafa Özuysal, Gökmen Tayfur, Serhan Tanyel

Institutions: Izmir Institute of Technology, Dokuz Eylül University

Language: English

Topic: Transportation Planning and Optimization

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $970