Document Library

This brief document outlines the AASHTO CPBM Subcommittee on Asset Management's Strategic Plan for 2011-2015. Colorado DOT scanned their copy for archival purposes. Check out the home of the CPBM Subcommittee on Asset Management for more resources, news, and events: https://www.tam-portal.com/community/cpbm-am/

This document provides step by step instructions for how to upload documents to the TAM Portal.

A compilation of TAM resources categorized and tagged in an excel sheet. Tagging includes topic area, regulatory implications, resource cost, complexity level, and key words.

Provides an agreed and consistent approach for the valuation and depreciation of infrastructure assets including; roads, water supply, sewerage, storm water, parks and recreation, land drainage, property, cultural and heritage assets. The general principles are also applicable to telecommunications and energy assets. While specifically written to New Zealand conditions and legislative requirements the manual does provide a framework and methodology that is applicable worldwide. The guide was originally published in 2006 and updated in 2025.

The aim of this study is to create a manual that will aid airports in establishing and sustaining a procedure for the handover of asset information. This includes the acceptance of new and replacement physical assets vital to major airport stakeholders. Asset information encompasses, but is not limited to, electronic data, blueprints, and operations and maintenance (O&M) details.

The manual should at least cover the following aspects:

• A list of recommended assets and associated data that airports should keep track of;
• The identification and coordination with individuals and entities that use or provide this asset information;
• Best practices for the oversight and documentation of the asset information handover process;
• Case studies showcasing a variety of airport sizes, types, and levels of experience; and
• A narrative aimed at airport executives, discussing how to implement the asset handover process effectively, including the necessary staff support.

This is a companion document to NCHRP Report 1122: Implementing Machine Learning at State Departments of Transportation.

This report provides an overview of how state Departments of Transportation (DOTs) are meeting federal requirements for annual reporting on their Transportation Asset Management Plans (TAMPs). The study, conducted in September 2024, analyzes consistency reports from multiple states, examining their approaches to reporting on asset inventory and condition, risks, funding, and narrative explanations. It highlights variability in reporting practices, discusses common elements and best practices, and offers insights into potential improvements for future reporting. The report is useful for state DOT officials, federal highway administrators, and anyone involved in transportation asset management and performance reporting.

Thought for 6 seconds

Over the past two decades, machine learning—the leading branch of artificial intelligence—has risen rapidly on the strength of exploding data volumes, cheaper storage and computation, and continual algorithmic breakthroughs. Deep-learning techniques and generative AI systems such as ChatGPT are now transforming day-to-day business practices. Yet state and local transportation agencies are drowning in new data streams faster than they can translate them into actionable insight. TRB’s NCHRP Research Report 1122, *Implementing Machine Learning at State Departments of Transportation: A Guide*, equips DOTs with a primer on promising ML use cases, tools for weighing costs, benefits, risks, and constraints, and a roadmap for fostering the data-driven culture needed to scale these capabilities across transportation programs.

The operation of connected and autonomous vehicle (CAV) technology is expected to impact various components such as traffic control devices, markings, signals, guardrails, computing systems, and communication infrastructure, both on a permanent and temporary basis. It is crucial for state transportation departments to pinpoint deficiencies in understanding and expertise, and to ready themselves for the hurdles of deploying CAV technologies while ensuring that the current road network continues to function at a satisfactory service level.

Artificial intelligence (AI) has become a powerful force in transportation departments, particularly for managing and streamlining traffic flow. By using real-time data and predictive analytics, AI solutions can ease congestion, decrease travel times, and enhance safety through early hazard detection. In addition, AI-driven simulations offer a cost-effective method for evaluating and refining transportation networks, reducing the need for extensive physical testing.