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The efficiency of the public transport system in any city depends on integration of its major public transport modes. Suburban railway and public buses are the modes normally used by the majority of commuters in metropolitan cities of developed and developing countries. Integration of these two services reduces overall journey time of an individual. In this research, a model is developed for operational integration of suburban trains and public buses. The model has two sub models: a Routing Sub Model and a Scheduling Sub Model. In the Routing Sub Model, feeder routes are generated for public buses which originate from a railway station. A Heuristic Feeder Route Generation Algorithm is developed for generation of feeder routes. In the Scheduling Sub Model, optimal coordinated schedules for feeder buses are developed for the given schedules of suburban trains. As a case study the Dun Laoghaire DART (Dublin Area Rapid Transit) (heavy rail suburban service) station of Dublin in Ireland is selected. Feeder bus services are coordinated with existing schedules of the DART on the developed feeder route network. Genetic Algorithms, which are known to be a robust optimization technique for this type of problem, are used in the Scheduling Sub Model. Finally the outcome of the research is a generated feeder route network and coordinated services of feeder buses on it for the DART station.

This paper presents the results of a study undertaken in Dublin, to ascertain passenger preferences for public transport information. The study examines the need for public transport information across three stages of a public transport trip. The stages examined are as follows; stage one: pre-trip planning from home to work, stage two: at-stop/station information, and stage three: pre-trip planning from work to home. At each of these stages, the respondents were asked to choose between several methods of receiving information, via the internet, mobile phone, call centre or at stop real-time information displays. The study utilised a stated preference approach to ascertain what type of information respondents required at each stage and their willingness to pay for this information. This paper presents the findings of the study undertaken and reports several factors which affect respondent’s willingness to pay for public transport information. This paper examines several factors relating to the impact of the geographical location of a respondent in relation to the utility they derive from real-time public transport information. The first factor examined measures the impact that the proximity of an individual to a high quality public transport option has upon utility derived from real-time information. The second factor examines how the residential location of the respondent impacts upon the utility derived from real-time information. Both of these geographical factors were examined using the results of a stated choice experiment conducted in Dublin, Ireland. The first section of this paper presents the survey methodology applied in this study. The following sections present the demographic characteristics of the sample collected and the results of the multinomial logit modelling conducted on the stated choice results. The paper concludes with a discussion and conclusions section. Institute of Transport and Logistics Studies. Faculty of Economics and Business. The University of Sydney

Most urban bus operators collect detailed data on their respective transportation networks using electronic fare collection systems. However, contrary to the opinion of other service industries that this data is a valuable resource, many bus operators have tended not to fully utilise these resources. International experience suggests using innovative technologies and methodologies such as data warehousing, Online Analytical Processing (OLAP), and data mining, to derive the maximum benefit from this data. Still bus operators tend not to keep the full range of data in a form, which is easy to access or utilise, and therefore, are not able to apply these technologies. The aim of the research project on which this paper reports is to describe the initial data structure of an electronic fare collection system (installed by a public transport operator in Ireland), the storage and enrichment of that data in a relational database, and finally, the representation of the public transport data in a data warehouse. This data warehouse forms the basis of all future data analysis. A 4-phase framework describes the import process leading to a relational database storing the transactional data. The paper concludes with the development of a data warehouse using the star schema.