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• 1. [Article] Tri-Met's Experience With Automatic Passenger Counter and Automatic Vehicle Location Systems

  The Tri-County Metropolitan Transportation District of Oregon (Tri-Met) is one of about 30 metropolitan transit agencies that have deployed both automatic vehicle location (AVL) and automatic passenger ...

  Citation × Citation Citation Abstract Copy Title: Tri-Met's Experience With Automatic Passenger Counter and Automatic Vehicle Location Systems Author: Strathman, James G. Year: 2002 The Tri-County Metropolitan Transportation District of Oregon (Tri-Met) is one of about 30 metropolitan transit agencies that have deployed both automatic vehicle location (AVL) and automatic passenger counter (APC) systems (Casey, 1999). These technologies are important components of the agency’s new automated bus dispatching system (BDS). The AVL and APC systems at Tri-Met recover comprehensive operations and passenger activity data at the bus stop level that is archived for later analysis. The agency has gained a reputation as an industry leader in the areas of data archiving and the application of archived data to performance monitoring and analysis. Prior to the implementation of AVL and APC technology, Tri-Met, like others in the transit industry, relied on costly manual data recovery. During lean budget years in the early 1980s, the agency began using stand-alone APCs instead of ride checkers, and recovered very little operating data beyond what was required for Federal Transit Administration reporting. With its AVL and APC systems, Tri-Met is now automatically collecting and archiving over 500,000 stop and event data records per day. Offline analysis of this data supports a wide array of agency activities. The transformation from scarce to abundant data has contributed to a variety of functional changes that have both enhanced service quality and improved efficiency. This report examines factors related to the development of Tri-Met’s BDS that have contributed to its success in data archiving and analysis. The organization of the report includes a description of the BDS, a review of the types of analyses that have been supported by archived data, a discussion of factors that influenced decisions made on the design of the system, reflections on unrealized expectations and missed opportunities, and concluding observations. Title: Tri-Met's Experience With Automatic Passenger Counter and Automatic Vehicle Location Systems Author: Strathman, James G. Year: 2002 The Tri-County Metropolitan Transportation District of Oregon (Tri-Met) is one of about 30 metropolitan transit agencies that have deployed both automatic vehicle location (AVL) and automatic passenger counter (APC) systems (Casey, 1999). These technologies are important components of the agency’s new automated bus dispatching system (BDS). The AVL and APC systems at Tri-Met recover comprehensive operations and passenger activity data at the bus stop level that is archived for later analysis. The agency has gained a reputation as an industry leader in the areas of data archiving and the application of archived data to performance monitoring and analysis. Prior to the implementation of AVL and APC technology, Tri-Met, like others in the transit industry, relied on costly manual data recovery. During lean budget years in the early 1980s, the agency began using stand-alone APCs instead of ride checkers, and recovered very little operating data beyond what was required for Federal Transit Administration reporting. With its AVL and APC systems, Tri-Met is now automatically collecting and archiving over 500,000 stop and event data records per day. Offline analysis of this data supports a wide array of agency activities. The transformation from scarce to abundant data has contributed to a variety of functional changes that have both enhanced service quality and improved efficiency. This report examines factors related to the development of Tri-Met’s BDS that have contributed to its success in data archiving and analysis. The organization of the report includes a description of the BDS, a review of the types of analyses that have been supported by archived data, a discussion of factors that influenced decisions made on the design of the system, reflections on unrealized expectations and missed opportunities, and concluding observations. Close

• 2. [Article] An Evaluation of Automatic Passenger Counters: Validation, Sampling, and Statistical Inference

  While automatic passenger counters (APC's) offer the potential for cait effective data recovery and management, they also introduce new complications in the data recovery process. This report addresses ...

  Citation × Citation Citation Abstract Copy Title: An Evaluation of Automatic Passenger Counters: Validation, Sampling, and Statistical Inference Author: Strathman, James G. Year: 1989 While automatic passenger counters (APC's) offer the potential for cait effective data recovery and management, they also introduce new complications in the data recovery process. This report addresses three issues associated with the implementation of APC's, based on an evaluation of the recent experiences of the Tri-County Metropolitan Transportation District of Oregon (Tri-Met). First is the issue of validation, which is concerned with both the recovery and accuracy of APC passenger data. The second issue concerns the development of a sampling methodology for APC's compatible with UMTA's Section 15 reporting requirements. Third is the issue of inferring system-level ridership from sample data in the presence of selective APC failures. We find that the APC's are providing systematically accurate passenger counts. Analysis of the data recovered from September to November 1988 also shows that sampling was representative, based on the set of "trains" from which ridership data were successfully recovered. The initial selection/assignment of trains, however, was not representative. Given that APC's record operating data for all bus trips comprising a train assignment, a cluster sampling method is formulated that ensures an overall random selection of bus trips via a random first stage selection of trains. Selective data recovery failures can hamper the process of inferring system-level ridership from the sample estimates. For example, when failure rates vary by bus type or time of day, inferences drawn from the sample of recovered data may over or under-represent total system ridership. In such circumstances, post hoc stratification of the sample data may be required. We outline several alternative corrections based on a-priori knowledge of the mix of bus types and schedule characteristics in the system. Title: An Evaluation of Automatic Passenger Counters: Validation, Sampling, and Statistical Inference Author: Strathman, James G. Year: 1989 While automatic passenger counters (APC's) offer the potential for cait effective data recovery and management, they also introduce new complications in the data recovery process. This report addresses three issues associated with the implementation of APC's, based on an evaluation of the recent experiences of the Tri-County Metropolitan Transportation District of Oregon (Tri-Met). First is the issue of validation, which is concerned with both the recovery and accuracy of APC passenger data. The second issue concerns the development of a sampling methodology for APC's compatible with UMTA's Section 15 reporting requirements. Third is the issue of inferring system-level ridership from sample data in the presence of selective APC failures. We find that the APC's are providing systematically accurate passenger counts. Analysis of the data recovered from September to November 1988 also shows that sampling was representative, based on the set of "trains" from which ridership data were successfully recovered. The initial selection/assignment of trains, however, was not representative. Given that APC's record operating data for all bus trips comprising a train assignment, a cluster sampling method is formulated that ensures an overall random selection of bus trips via a random first stage selection of trains. Selective data recovery failures can hamper the process of inferring system-level ridership from the sample estimates. For example, when failure rates vary by bus type or time of day, inferences drawn from the sample of recovered data may over or under-represent total system ridership. In such circumstances, post hoc stratification of the sample data may be required. We outline several alternative corrections based on a-priori knowledge of the mix of bus types and schedule characteristics in the system. Close