A model to assess public transport demand stability Pablo Bass a , Pedro Donoso b , Marcela Munizaga a,⇑ a Universidad de Chile, Casilla 228-3, Santiago, Chile b LABTUS, Universidad de Chile, Casilla 228-3, Santiago, Chile article info Article history: Received 1 February 2011 Received in revised form 28 April 2011 Accepted 17 June 2011 Keywords: Public transport Client retention Demand modeling abstract Transport authorities, especially those in developing countries where rising income stim- ulate increased car ownership rates, are often concerned with maintaining or increasing levels of public transport use. Therefore, the ability to identify clients at risk of abandoning the system can be valuable for remedial measures, allowing for more focused quality improvements. We present and apply a model that determines the probability of migrating from public to private transport at both aggregated and disaggregated levels. In applica- tion, the model predicted migration with 60% accuracy in the first preference recovery measure. The proposed model can improve the understanding of the behavior of public transport users, the analysis of demand stability and the factors influencing migration. This, in turn, can help to focus policy and management measures and increase the effi- ciency of public investment. Ó 2011 Elsevier Ltd. All rights reserved. 1. Introduction Transport authorities, especially those in developing countries where rising income stimulate increased car ownership rates, are often concerned with maintaining or increasing levels of public transport use. In Santiago, Chile, this effect is pre- valent; car ownership is growing rapidly, while public transport ridership is decreasing at the same rate. Therefore, the abil- ity to identify clients at risk of abandoning the system can be valuable for remedial measures, allowing for more focused quality improvements. In this context, a public transport user or customer can be defined as a person who regularly uses public transport to tra- vel to major activities, such as work or school. By investigating disaggregated personal information, we can determine how travelers perceive the system and adapt to external conditions. In other fields, demand stability has been analyzed via data mining and other statistical methods. In transport, very detailed and valuable data from discrete choice models and exper- iments designed to estimate the relative importance of quality of service variables are often used (Hensher et al., 2003). Although customer satisfaction and retention are rarely studied in the public transport field, some examples exist. Brog and Kahn (2003) analyzed the level of service variables and constructed a risk map, identifying the relation between satis- faction and risk of migration, for captive and non captive users. Minser and Webb (2010) applied a structural equation model to study the relation between customer loyalty, customer satisfaction, service quality, problem experience and public image in the Chicago public transport system. Their initial findings are that service quality and customer satisfaction have a positive effect on customer loyalty, and that public image and problem experience have a strong influence on customer satisfaction. More recently, Trepanier and Morency (2010) used long-term smartcard data to analyze rider retention and identify behav- ior by customer type in the Gatineau (Quebec) public transport system, they found that adult users who make mainly home based trips stay in the system for a longer period than a reference user. It is worth pointing out that all these authors say their work is preliminary, and more research is required in this field. 0965-8564/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.tra.2011.06.003 ⇑ Corresponding author. Tel.: +56 2 9784649; fax: +56 2 6894206. E-mail addresses: laclote@gmail.com (P. Bass), pdonoso@labtus.cl (P. Donoso), mamuniza@ing.uchile.cl (M. Munizaga). Transportation Research Part A 45 (2011) 755–764 Contents lists available at ScienceDirect Transportation Research Part A journal homepage: www.elsevier.com/locate/tra