CHAPTER 2
Literature Review

AVM and other automatic monitoring capabilities have been increasingly used as a tool in the transit industry (among others) over the past few decades. The capacities of these tools are continually being studied to better understand the potential, capabilities, and future of the technology.

In 1994, AI was beginning to be used in transit systems (Mulholland and Oren 1994). Transit specialists were just beginning to understand the capabilities of the technology and wanted to explore it further, in order to determine whether it was beneficial in practice. Specialists began using AI to assist with transit railcar diagnostics. The technology was being used to help improve maintenance fault-diagnostic capacities in the railcars. The use of AI for this task was found to be cost-effective after further investigation of the technologyʼs capabilities.

Two years later, a study was conducted to explore the development and availability of Advanced Public Transportation Systems (APTS) technologies (Khattak et al. 1996). APTS technologies have many benefits, including improving service, increasing transit efficiency, reducing operating costs, and producing direct benefits for travelers—such as reduced travel times, increased safety and security, and reduced stress in dealing with the unreliability of transit. The study provided an early yet accurate depiction of what AVM and VHM would be able to do in the following years as the technology became more advanced, accessible, and affordable.

In 1997, an advanced AVM system was integrated into the bus network of the Trieste, a city and seaport located in northeastern Italy (Caltabiano et al. 1997). Their use of AVM led to operational benefits for transit users, since the main objective of AVM usage was to keep the buses on a consistent and reliable schedule. This led to an increase in public transportation use in Trieste. The use of AVM was subsequently replicated by other cities around the world because the benefits that the technology could bring to transit systems had become apparent.

By 2022, there had been three decades of technological improvement since AI was first used for railcar diagnostics (Nessen 2022). Over that time, there had been a significant increase in the use of CAD/AVL to track the location of vehicles during service. There are now companies that perform predictive maintenance. For example, the Metropolitan Transportation Authority in New York City uses PMT from Preteckt to identify potentially serious equipment failures before they take place. This allows for maintenance efforts to be performed proactively; minor and less expensive issues can be fixed before they turn into more damaging and costly ones. In 2023, the Jacksonville Transportation Authority collaborated with Clever Devices to enhance its fleetʼs reliability through the use of AI (Jacksonville Transportation Authority 2023). The effort proved to be successful; the initial goals of enhancing the overall passenger experience, reducing operational challenges, and minimizing service disruptions were met. This collaboration outlined a start-to-finish process for a transit agency that is looking to integrate this technology into its system but may not know where to start.

In 2010, TCRP carried out a study with the purpose of educating and assisting transit administrators, engineers, and researchers who were facing issues in the transit industry (Schiavone 2010). This study explored and synthesized existing information from a wide variety of sources in a way that could be easily referenced in one document. The 2010 study, along with the technological innovations being used and the many successes being seen at individual agencies, led to TCRPʼs interest in conducting a more widespread and comprehensive study of how agencies across the United States view and utilize AVM, VHM, and PMT.

In 2023, TCRP initiated this synthesis in order to learn more about specific agencies that use this technology, how the technology is deployed, the training required to carry it out, how integration processes took place, the benefits that have been seen, and the lessons that were learned from the process. TCRPʼs goal is for this synthesis to encourage more agencies to employ AVM, VHM, and PMT in order to enhance the overall passenger experience, reduce operational challenges, and minimize service disruptions, while also reducing the costs associated with labor and misdiagnoses within a transit agencyʼs maintenance department.

A list of references can be found at the end of this document. An annotated bibliography of the studies and reports referenced in the literature review can be found in Appendix B.