摘要: |
Data from bus and rail intelligent transportation systems (ITS) are a valuable resource for transit service planning and management. In particular, vehicle location and passenger activity data from automatic vehicle location (AVL), automatic passenger counter (APC), and automatic fare collection (AFC) systems can be used to provide essential insight into transit operations and to inform decision making to increase the efficiency, productivity, and safety of transit service. There are, however, significant challenges for transit agencies in accessing and using this data. Many agencies cannot get to the data at all or do not understand the data they have. Data validation and quality control, integration and matching across various data sets, and aggregating data are all difficult, as is developing the types of reports, tools, and analytics that contribute to informed decision making. Even when transit agencies, researchers, and consultants do address these challenges, they often have difficulty sharing their work with their peers in the industry because the same types of data may be managed differently among transit agencies. The result is that transit ITS data is rarely used to its full benefit.
Creating a common approach to accessing and managing transit ITS data would facilitate the development and exchange of data management practices, of advanced reports and tools, and of new analytical techniques among transit agencies. The use of the General Transit Feed Specification (GTFS) format as the basis for a wide variety of service planning and customer information tools is a useful comparison. By providing a common way of representing schedule data, GTFS has facilitated significantly faster development of tools than would otherwise occur if every transit agency had their data in different formats. As a result, there is a recognized need for research to generate similar advancements in the use of transit ITS data, replicating improvements comparable to what GTFS has achieved for schedule data.
The objective of this research is to develop a common, practical approach to storing, accessing, and managing fixed-route transit ITS data. This data management approach should address the following: (1) Current data sources, access, quality, and integration challenges; (2) Relationship between historical schedule data and ITS data;
(3) Differing needs of transit agencies and other users of transit ITS data; (4) Optimal configuration allowing regular improvement and sharing across the industry; (5) Exchange of reports, tools, and analytical techniques based on transit ITS data; and (6) Methods and procedures for developing operations management reporting and decision-support systems. |