Use of Electronic Ticket Machine Data in Transport Planning Models
MEAL J and CARTER D, MVA, UK
This paper concentrates on Electronic Ticket Machine (ETM) data emanating from bus operations rather than rail systems. This is because rail data generally record true origins and destinations, is normally in a usable format and has been more readily avai
This paper concentrates on Electronic Ticket Machine (ETM) data emanating from bus operations rather than rail systems. This is because rail data generally record true origins and destinations, is normally in a usable format and has been more readily available. Conversely, bus data has been used to a lesser extent in transport planning studies, despite representing the vast majority of public transport trips in most urban areas. As a result, large scale survey/interview approaches have traditionally been used to create base matrices for public transport demand models.
Historically, the use of bus ETM data in transport planning studies has been hampered by three main factors:
* commercial confidentiality on the part of transport operators which limits data availability;
* doubts about the usefulness of data from ETMs in transport planning studies on the part of many practitioners;
* unfamiliarity with the esoterics of ETM data (ie the seemingly mysterious data formats and obscure variations caused by radically different institutional arrangements and forms of bus operation both within and across different countries).
The first of these issues, commercial confidentiality, is in one sense paramount, in that if it cannot be overcome for the use of data to be permitted in a particular study, the debate ends at this point! However, one observation for the UK is very appropriate at this point. MVA's experience of studies over the past five years has shown an increasing level of co-operation in making commercially sensitive data available for specific study purposes. This perhaps reflects the recent emphasis on Quality Partnerships between private transport operators and Councils and the realisation that concerted effort in the face of rising car ownership and usage is better than responses in isolation.
This Paper will however attempt to address the two remaining issues facing the transport planner by discussing how useful ETM data can be in the transport demand modelling process and by attempting to unravel the mysteries of how the richness of ETM data can be harnessed in practice based on MVA's general experience, and a number of quoted examples. The four case studies illustrate how differences in the base data (eg presence/absence of a prior matrix), relative size of the study budget and level of detail in the ETM data have led to different solutions in matrix building and updating.
Association for European Transport