ABSTRACT
Current practice in transit agencies shows that sufcient data seldom exist for service operationsplanning. Manual data collection efforts are costly and, consequently, must be used sparingly. Automated data collection systems, though growing rapidly, are not yet perfectly linked to the requirements of planning data. Extraction of data from an automated system, which is ostensibly asimple task, may turn out in actuality to be rather complex. Having too much data is often as bad as having too little. The only concept we can trust is that in any collection of data, the gure most obviously correct beyond all need for checking is the mistake. At the same time, the data are essential for responding to basic passenger needs, namely, the route: where is the closest stop, what time should I be at the stop, and more. The data are certainly crucial, too, for responding to the operations planning needs of each transitagency: How can the network of routes, stops, and terminals be improved? How can each
route be improved? What is the best timetable to deliver? How can ¦eet size be minimized while maintaining the same level of service? How can crew cost be minimized without servicechanges? No doubt, acommon element for all transit agencies is their pursuit of data to aid in improving the efciency, productivity, and effectiveness of their systems.
