Ridership Estimation of New LRT System: A Direct Demand Model Approach

Light Rail Transit (LRT) systems offer high capacity and enhanced quality of services at a relatively low investment and operating cost compared to other fixed route modes. The potential introduction of an LRT system is inevitably related to proper estimation of its ridership; this is particularly true for cases of no prior experience in the use of such modes. This paper presents a practical approach for developing a direct demand model, for the case of a planned LRT system in Cyprus. The proposed approach is based on existing traffic demand data and limited roadside surveys. Results indicate that the introduction of the proposed LRT would attract 23000 passengers in a daily basis and shift a small percentage of traffic to the system.

[1]  Graham Currie,et al.  Exploring the drivers of light rail ridership: an empirical route level analysis of selected Australian, North American and European systems , 2011 .

[2]  Richard Burns,et al.  Business Research Methods and Statistics Using SPSS , 2008 .

[3]  Darcin Akin.  Model for Estimating Increased Ridership Caused by Integration of Two Urban Transit Modes: Case Study of Metro and Bus-Minibus Transit Systems, Istanbul, Turkey , 2006 .

[4]  Xiaokun Wang,et al.  Transit ridership estimation with network Kriging: a case study of Second Avenue Subway, NYC , 2014 .

[5]  Martyn Leslie Senior,et al.  Impacts on travel behaviour of Greater Manchester’s light rail investment (Metrolink Phase 1): evidence from household surveys and Census data , 2009 .

[6]  Christos Pyrgidis,et al.  THE NEW LIGHT RAIL TRANSIT SYSTEMS OF ATHENS, GREECE , 2004 .

[7]  Simon Blainey.  Trip end models of local rail demand in England and Wales , 2010 .

[8]  Helena Sjöstrand.  Passenger assessments of quality in local public transport- measurement, variability and planning implications , 2001 .

[9]  M. Kuby,et al.  Factors influencing light-rail station boardings in the United States , 2004 .

[10]  Clayton Lane,et al.  Sketch models to forecast commuter and light rail ridership : Update to TCRP report 16 , 2006 .

[11]  Corinne Mulley,et al.  Using geographically weighted regression to forecast rail demand in the Sydney Region , 2013 .

[12]  R. Cervero.  Transit-Oriented Development's Ridership Bonus: A Product of Self-Selection and Public Policies , 2006 .

[13]  Vukan R Vuchic,et al.  Urban Transit : Operations, Planning and Economics , 2005 .

[14]  Martin T. Pietrucha,et al.  FIELD STUDIES OF PEDESTRIAN WALKING SPEED AND START-UP TIME , 1996 .

[15]  Ming Zhang,et al.  Accessibility-Based Approach to Forecasting LRT Trip Production: Application Example from Wuhan, China , 2013 .

[16]  M. McNally.  On the formation of household travel/activity patterns : a simulation approach , 1986 .

[17]  Jani Beko.  Estimation of demand functions for services of public railway passenger transportation : evidence from Slovenia , 2004 .

[18]  Sevgi Erdogan,et al.  How to Increase Rail Ridership in Maryland: Direct Ridership Models for Policy Guidance , 2016 .

[19]  G. McLachlan.  Discriminant Analysis and Statistical Pattern Recognition , 1992 .

[20]  Herbert S Levinson,et al.  Estimating Transit Station Ridership: A Method for Calculating Daily Boardings and Evaluating Access Alternatives , 2012 .

[21]  Keemin Sohn,et al.  An analysis of Metro ridership at the station-to-station level in Seoul , 2012 .

[22]  Robert Cervero,et al.  Direct Ridership Model of Bus Rapid Transit in Los Angeles County, California , 2009 .

[23]  T. Litman.  Evaluating rail transit benefits: A comment , 2007 .

[24]  G J Saur,et al.  NEW METHOD FOR TRANSIT RIDERSHIP FORECASTING , 2004 .

[25]  Javier Gutiérrez,et al.  Transit ridership forecasting at station level: an approach based on distance-decay weighted regression , 2011 .

[26]  Milena Scherer,et al.  Is Light Rail More Attractive to users than bus Transit?: Arguments Based on Cognition and Rational Choice , 2010 .

[27]  Yan Song,et al.  Analysis of Metro ridership at station level and station-to-station level in Nanjing: an approach based on direct demand models , 2014 .