7.0 Comparison of SSAMS to Other Asset Management Systems

7.1 Overview

The previous sections of this report have developed a concept for a SSAMS, and illustrated how a SSAMS might be used to analyze alternative signal system improvements. This section contrasts the SSAMS architecture with two types of asset management systems that are further advanced in terms of maturity and level of implementation: infrastructure-based asset management systems, and systems for management of information technology (IT) assets. Because signal systems consist of both physical assets and IT-type assets, this comparison allows us to understand how asset management systems that are already in place can provide models for further development of the SSAMS concept.

7.2 Comparison with Infrastructure-Based Asset Management Systems

Pavement and bridge infrastructure management systems are most mature with respect to implementation experience and sophistication. Therefore, this section bases the comparison to signal systems on these types of infrastructure management systems. However, much of the discussion would be valid for other classes of transportation infrastructure (e.g., tunnels).

Nature of Assets Being Managed

There are some fundamental similarities between major infrastructure and signal systems assets – both include physical assets that deteriorate over time, and require maintenance, rehabilitation, and replacement. Both have the purpose of serving a transportation function, and therefore measurement of their performance is typically related to impacts on the traveling public.

However, there are many characteristics of pavements and bridges that are distinctly different from signal system assets. These include:

Considerably Longer Service Lives – For example, between 75 and 100 years for bridges; between 40 and 60 years for pavement structures (with surface replacements required every 10 to 15 years). This implies an emphasis on deterioration modeling and determination of optimal maintenance and rehabilitation intervals throughout the life.
Higher Replacement Costs – Costs to reconstruct a road or replace a bridge are orders of magnitude higher than costs to replace a signal.
More Static in Nature – Once constructed, pavement and bridges are static entities, and do not (with the exception of drawbridges) require ongoing monitoring and adjustments like signal systems do.
Lower Incidence of Failure – Pavements and bridges are less subject to malfunctions or failures that cause them to cease providing the function for which they were designed. On the other hand, a failure of a central system controller or a power failure can cause a network of signals to stop working.
Infrastructure asset components consist of materials such as concrete, asphalt, and steel; signal systems are made of electronic components including communications and computer hardware and software. Accordingly, infrastructure asset management emphasizes materials properties, physical condition and structural capacity; whereas signal systems management pays more attention to technology, operational features, characteristics, and performance.
Fewer Systemwide Interdependencies – Whereas pavements and bridges are self-contained entities, decisions about signal systems must consider interrelationships across different system components - for example, coordination of timing, and upgrades that ensure compatibility of equipment.

Data and Information

Asset management systems are based on quality information to support fact-based decisions aimed at improving performance. At a high level, many of the data components identified in the SSAMS architecture are the same as the data components for an infrastructure asset management system. Both types of asset management systems require:

Inventory information indicating the asset location and characteristics;
Condition information about each asset (or proxy measures such as age or failure rate);
Definitions of work activities and their cost components;
Work history information to track maintenance, rehabilitation, and replacement by date, asset, component type, and work type;
Information on reported problems or needs, in order to drive maintenance activities and track performance measures that assess service quality/response time;
Cost and performance model parameters derived from condition and work tracking information (and/or external sources, rules of thumb) for use in life-cycle cost analysis;
Information on personnel and equipment resources available as input to maintenance work planning and scheduling functions;
Information on personnel and equipment resources used as input to development and updating of work cost models; and
Information on identified deficiencies, work candidates, and scheduled work (including funding sources).

At a more detailed level, there are clear differences in the type of information used for major infrastructure asset management and signals asset management:

Inventory data for signal systems is more heterogeneous than that for pavement and bridges, covering distinct sets of information for intersections, different types of equipment, and systems components.
Information about the characteristics of electronic equipment and systems is fundamentally different from characteristics of pavements and bridges. The former includes model numbers, serial numbers, vendor information, and functional specifications whereas the latter includes materials composition, and design and structural properties.
Field inspection data providing time-series information on condition is a fundamental element of major infrastructure asset management. While inspections are required for effective signal systems asset management, there is less emphasis on condition tracking over time and greater emphasis on recording work activities performed and needs identified as part of the inspection process.
As a rule, real-time information on status and failure is not currently part of major infrastructure management systems (though this may be changing with current advances in sensor technology and road weather information systems), whereas newer signal systems do have these capabilities. The time scale from identification of a problem to resolution of that problem is much shorter in the case of signal systems than for major infrastructure. Therefore, there is less need for persistent information on performance.
Information for signals asset management must include tracking of the interdependencies across different system components to a much greater extent than pavement and bridge management systems. For pavement and bridge management, identifying location on a route network is sufficient information to support an understanding of how different projects relate to one another (e.g., coordination of work along a corridor in order to minimize work zone cost, prioritization of bridge improvements based on detour lengths). For signal systems, analysis of technology upgrades is done for groupings of interrelated components. Therefore, it is important to keep track of the different subsystems, and understand how different pieces of equipment within each subsystem relate to one another – if one is replaced, do the others need replacement also?
While pavement and bridge management systems do require information on traffic, this information is used either to understand loading characteristics, or to derive user costs associated with different preservation and improvement strategies. Signal systems asset management requires a more detailed understanding of traffic characteristics (e.g., turning movements, speeds, variations in traffic by time-of-day, day-of-week, and season) and changes in these characteristics over time – these are used to select appropriate control strategies and determine detailed operating parameters.

Condition and Performance Measures

Because signal systems and major infrastructure are both transportation system components, investments in both of these asset classes are evaluated based on benefits to system users. For pavements, impacts on users include comfort, safety, speed, fuel consumption, and vehicle wear-and-tear, which are a function of the road surface characteristics. For bridges, travel time for certain vehicle classes is also considered (e.g., based on the need for weight or height restrictions or bridge closures). Failure risks are also sometimes taken into account. For signal systems, delay/travel time and safety are the primary considerations.

For major infrastructure assets, the impacts on users are driven to a large extent by the physical condition of the asset. Therefore, infrastructure asset management systems tend to emphasize tracking of physical condition, and typically include methods for deriving aggregate condition indices from a variety of physical measurements (e.g., cracking, roughness, and rutting in the case of pavements), or from individual subelement condition measures (e.g., condition of deck, piers, abutments, bearings, etc., for the case of bridges).

For signal systems, impacts on users are driven primarily by operational characteristics: timing plans, synchronization of signals along a corridor, type of control (e.g., traffic-responsive, adaptive, etc.), and response time to failures. Physical condition of the components of the signal system do impact these operational characteristics, by affecting failure rates. However, given the shorter service life of components, age can typically be used as a reasonable proxy value for condition, which de-emphasizes the need for development of the condition inspection methods and condition indices that are such a prominent feature of infrastructure management systems.

A final comment about condition and performance measures is that for both infrastructure and signal systems assets, performance indicators can be constructed based on progress towards defined service or work accomplishment targets that have been established. Examples of these types of indicators for signal systems were provided at the end of Section 6.4 (e.g., “percent of intersections on major corridors that are under closed-loop control”). For major infrastructure assets, similar types of performance indicators are used – for example – “percent of bridges that are load-posted,” or “average percent of pavement miles resurfaced per year.”

Analysis Capabilities

Key analytical capabilities of an infrastructure asset management system are:

Condition inspection data validation, data reduction, and reporting;
Performance Monitoring – Calculation of composite condition indices and performance measures derived from condition indices, traffic, and inventory characteristics;
Deterioration Modeling – Application of deterioration models to predict treatment life and/or derivation of deterioration model parameters based on historical condition data and expert judgment;
Cost Modeling – Estimation of work costs based on variables such as asset element quantity, condition and type, and location;
Deficiency or Needs Analysis – Identification of needs – location, work type, and costs – based on application of user-defined deficiency or need criteria;
Economic Analysis (Benefit/Cost and Life-Cycle Cost Analysis) – Estimation of the benefits and costs of a treatment or a sequence of treatments over an extended time horizon, and use of discounting methods to provide a comparison of the time streams of benefits and costs for different alternatives;
Optimization – Algorithms to determine maintenance and rehabilitation strategies that minimize long-term agency costs (or agency and user costs);
Work Simulation – Application of treatment decision rules, deterioration models, and cost models to simulate work over a defined time horizon, subject to user-specified budget constraints; and
Resource Allocation/Prioritization – Application of simple ranking formulas or incremental benefit/cost methods to select the best set of projects or the best resource allocation to higher-level groupings (based on asset subcategory, geographic area, or subnetwork) – that can be accomplished for a given budget level.

In addition, the following capabilities are included in infrastructure maintenance management systems, which are considered by some agencies as integral to infrastructure asset management:

Complaint Tracking/Trouble-tickets – Recording of problem reports received from the public or observed by agency staff, and tracking their resolution;
Maintenance Quality Monitoring – Recording of information on maintenance condition (e.g., clogged drains), typically on a sample basis;
Maintenance Budgeting and Planning – Allocation of resources by maintenance activity or element, based on level-of-service approach, historical needs, or activity-based approach;
Work Scheduling – Creation of work schedules for planned activities that consider resource requirements and availability;
Work Management – Generation of work orders that specify the scope, procedure, equipment, personnel, and materials; and tracking the completion of these work orders and the labor, materials, and equipment hours utilized; and
Resource Management – Tracking of vehicle, equipment and materials inventories, and aggregate utilization.

Analytical capabilities for signal systems were defined in the SSAMS architecture shown in Figure 5.1. At a high level most of these capabilities overlap with analytics for infrastructure asset management and maintenance management that were listed above. Maintenance management functions tend to be generalizable across different asset classes, so those defined above for infrastructure maintenance are fairly well aligned with those for signal system maintenance (though activity definitions and resource requirements vary).

At a more detailed level, there are several significant differences in emphasis and content of analytic capabilities:

Infrastructure management requires more complex capabilities for deterioration modeling, preservation optimization, and work simulation; given the longer service lives, the high replacement costs, and the significant benefit to be gained through properly timed maintenance and rehabilitation treatments.
Life-cycle cost (LCC) modeling is an important analytical component of both infrastructure management and signal systems asset management, but the analysis requirements are different. LCC analysis for infrastructure depends on more sophisticated interrelated models of condition deterioration, treatment cost and user cost, and an understanding of how infrastructure maintenance needs vary as a function of condition. The complexity in LCC analysis for signal systems lies more in setting up the problem. The analysis must be structured to handle interrelated groupings of components with different service lives, and to consider alternatives that have the same benefits (a fundamental premise of LCC analysis). Representation of uncertainty and provision for sensitivity analysis is important in both infrastructure and signal systems LCC, but it can be argued that signal systems LCC is by nature less amenable to deterministic models, and better suited to probabilistic approaches.
Deficiency or needs analysis for infrastructure assets is more easily based on inventory characteristics and condition information determined from inspections, compared to different sets of standards or requirements. For signals, the relevant characteristics for identification of deficiencies are not as easily determined by observation. Probability of failure must be estimated based on age and/or historical failure rates of similar components. Operational effectiveness needs to be determined based on detailed traffic studies and application of specialized analysis tools.
Because of the real-time nature of signal system management, the shorter service lives, and the relatively rapid changes in technology that affect decisions, analytical functions for signal systems asset management need to be more nimble and less data hungry than those for infrastructure management. They must rely on easily assembled or automatically generated data, and use simple models that are easily adjusted. They must provide support for decisions that occur on very different time scales – including development of five- to 10-year upgrade strategies, annual budgeting, and day-to-day deployment of personnel to tasks.

7.3 Comparison with IT Asset Management Systems

Overview of IT Asset Management Systems

Because IT Asset Management is not as widely understood within the transportation community as infrastructure management, this section begins with a brief overview of what an IT asset management system is, and what it is used for.

The basic IT Management problem can be expressed as: “how to make most effective use of hardware, software, IT personnel, and available capital budget to meet the changing needs of the user population.” This problem involves both an ongoing maintenance and operations component, and an upgrade component. The operations component seeks to maintain service, minimize system down-time and maximize responsiveness to the users. The upgrade component must balance across competing user needs and consider system-level capacity/bandwidth needs and objectives for technology standardization.

IT Asset Management systems are used by managers of medium to large computer networks to track equipment (e.g., personal computers, servers, printers, routers, hubs) from acquisition to disposal. Information that is tracked includes hardware and software characteristics, configurations, licensing information, locations on the network, assignment to groups and specific users, and usage patterns. Much of this information is obtained through automated “discovery” and scheduled auditing functions that operate across the network. Some IT Asset Management systems also include support for hardware and software procurement processes, and provide linkages into enterprise resource planning and procurement systems.

IT Asset Management Systems perform or support the following types of functions:

Tracking deployment of software patches and antivirus software to different computers on the network;
Identification of efficient strategies for deployment of new equipment or redeployment of existing equipment;
Analysis of upgrade needs and options based on current utilization patterns;
Planning for server consolidation, system upgrades, and software upgrades (e.g., checks to see if computers have sufficient memory and processing power to run a new application);
Cost analysis of alternative software licensing options;
Provision of inventory information as backup for negotiations with hardware/software vendors (e.g., to obtain quantity discounts);
Management of hardware and software procurements;
Management of software leases, (e.g., ensuring on-time returns, locating leased equipment);
Monitoring of compliance with license and warranty contracts – Comparison of the number of software licenses purchased with the number of installed copies, and the number of installed copies in active use;
Monitoring of compliance with established IT standards;
Providing Help Desk staff with easy access to configuration information needed to address service requests or problem reports;
IT budget planning, cost allocations and payment management;
Reporting on equipment inventory and usage;
What-if analysis for alternative upgrade/replacement strategies, and evaluation of technology standardization strategies; and
Scheduling of maintenance and repairs with consideration of the best timing given warranties in effect.

Nature of Assets Being Managed

Signal systems include computer hardware, software, and network equipment, so there is some overlap in the types of assets being managed between the two systems. However, IT asset management systems typically cover large numbers of computers, whereas the overall asset inventory of a signal system is less dominated by computer equipment. In addition, the major features of IT management systems – automated discovery of inventory, configuration, and usage information – depends on all of the computer equipment being networked together. This is not necessarily the case for signal system computer equipment.

Data and Information

Information on model numbers, licenses, warranties, configurations, and specifications is included in IT asset management. This information is also required for both the computer equipment and other electronic equipment of a signal system.

Condition and Performance

IT asset management systems emphasize measures of efficiency in use of computing resources, and compliance with license agreements and IT standards. IT asset management systems track utilization of existing equipment and software. Analogous capabilities for signal systems are provided in systems with integrated traffic monitoring functions that track traffic flow efficiency, typically in support of adaptive control algorithms.

IT asset management system information can also be used to derive statistics on progress towards objectives such as the percent of computers that have been upgraded to the latest operating system release.

IT asset management and signal systems asset management share service-oriented objectives of minimizing down-time. IT asset management systems include real-time monitoring capabilities to automatically detect status of devices on the network that are analogous to the features of modern signal management systems. Thus, both signal systems and IT systems asset management track operational performance from a real-time perspective.

However, IT asset management systems are not typically focused on monitoring failure rates for different types of components or maintaining persistent performance metrics on equipment down-time. This is due to the fact that most IT asset management systems are operated in a corporate environment which has a different accountability focus than a public agency environment.

Analysis Capabilities

Analysis capabilities that are shared by IT asset management systems and the SSAMS architecture include:

Query and reporting features on equipment inventory characteristics and operational performance;
Deficiency Analysis Capabilities – To identify equipment that does not conform to standards (which may be based on age, version numbers, or release numbers);
Costing Analysis Capabilities – To support budgeting and comparison of different upgrade alternatives, and
Maintenance Management Capabilities – For work scheduling, and management of the resolution of problem reports.

7.4 Conclusions

All three asset management approaches compared in this section – for signal systems, infrastructure assets and IT assets share the core principles presented in Section 2.0, and seek to maximize the efficiency and effectiveness in the use of available resources to address performance goals. From a birds-eye view, the basic functions of these three asset management approaches are the same – maintaining an inventory, monitoring performance, identifying deficiencies, evaluating options, and allocating resources. However, a closer look reveals distinct differences in emphasis and methods across asset management approaches for these three asset classes. These are due to differences in the characteristics of the assets themselves, and in the system users’ decision-making context.

The specific requirements of signal system asset management include a mix of the capabilities covered by infrastructure and IT asset management. Similarities between signal systems and IT assets exist most obviously because signal systems include IT assets – computer hardware, software, and communications equipment, and other signal system components (e.g., controllers) have similar management requirements. The specific data content of IT asset management systems can therefore be used to further define data requirements for the IT assets of signal systems. The real-time monitoring and discovery functions of IT asset management systems are beginning to appear in signal system management software. Signal system asset management can also benefit from the kinds of quick-response what-if capabilities of IT asset management – for understanding the implications of equipment replacement and upgrades, and for understanding the relationships among hardware, software, and personnel requirements (analogous to the physical, system, and personnel elements of signal systems).

There are many aspects of infrastructure asset management that address requirements of a SSAMS. First, there are certain components of a signal system (e.g., structures/poles) that can be handled with deterioration modeling and life-cycle costing analyses that have been developed for the infrastructure domain. More broadly, the practice of tracking performance not only from a business/resource utilization perspective, but from a user or customer point of view is ingrained in infrastructure management, and is needed for signal systems asset management (and for all public sector transportation assets) as well. Calculation of the impacts of signal system improvements on users requires many of the same methods used in infrastructure asset management, and in fact some of the performance measures are identical (safety, travel time). Finally, both signal systems and infrastructure require significant taxpayer dollars to preserve and improve, and asset management systems must provide accountability for resource allocation decisions. Therefore, the discipline and methods used in infrastructure asset management for systematically identifying deficiencies, evaluating alternatives, and using well-defined prioritization methods for allocating resources are needed for signal systems as well.

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