5.0 Challenges and Limitations in the Use of Traffic Analysis Tools

As discussed in sections 2.0 and 3.0, traffic analysis tools are useful and effective in helping transportation professionals best address their transportation needs (as long as they are used correctly). Each tool and tool category is designed to perform a different traffic analysis function, and there is no one analytical tool that can do everything or solve every problem. This section addresses some of the challenges and limitations that should be considered when selecting traffic analysis tools:

• Availability of quality data. If good data are not available, the user should consider a less data-intensive tool category, such as a sketch-planning tool rather than microsimulation. However, the results of the simpler tool categories are usually more generalized, so the user should carefully balance the needs of a more detailed analysis with the amount of data required.
• Limited empirical data. Data collection can often be the most costly component of a study. The best approach is to look at the ultimate goals and objectives of the task and focus data collection on the data that are crucial to the study.
• Limited funding. Limited funding for conducting the study, purchasing tools, running analytical scenarios, and training the users is often a consideration in transportation studies. Traffic analysis tools can require a significant capital investment. Software licensing and training fees can make up a large portion of the budget. Also, the analysis of more scenarios costs money. When faced with funding limitations, focus on the project’s goals and objectives, and try to identify the point of diminishing returns for the investment.
• Training limitations. Traffic simulation tools usually have steep learning curves and, as a result, some transportation professionals do not receive adequate modeling and simulation training.
• Limited resources. Limitations in staffing, capabilities, and funding for building the network and conducting the analysis should be considered. The implementation of most traffic analysis tools is a resource-intensive process, especially in the model construction and calibration (front-end) phases for simulation analyses. Careful scheduling and pre-agreed-upon acceptance criteria are necessary to keep the project focused and on target.
• Data input and the diversity and inconsistency of data. Each tool uses unique analytical methodologies, so the data requirements for analysis can vary greatly from tool to tool and by tool category. In many cases, data from previous projects contribute very little to a new analytical effort. Adequate resources must be budgeted for data collection.
• Lack of understanding of the limitations of analytical tools. Often, limitations and “bugs” are not discovered until the project is underway. It is important to learn from experiences with past projects or to communicate with fellow users of a particular tool or tool category in order to assess the tool’s capabilities and limitations. By researching the experiences of others, users can gain a better understanding of what they may encounter as the project progresses.
• Tool may not be designed to evaluate all types of impacts produced by transportation strategies/applications. The output measures produced by each tool vary, so the process of matching the project’s desired performance measures with the tool’s output is important. In addition, there are very few tools that directly analyze ITS strategies and the impacts associated with them (e.g., reduction in incident duration, agency cost savings, etc.).
• Lack of features. Some analytical tools are not designed to evaluate the specific strategies that the users would like to implement. This is more prevalent in modeling ITS strategies or other advanced traffic operations strategies. Often, “tricking” the tool into mimicking a certain strategy is a short-term solution; however, there should be flexibility so that advanced users may customize the tools.
• Desire to run real-time solutions. Many tools require a significant amount of time for setup, modeling, and analysis. There is hope that future tools would be able to be linked to Traffic Management Centers and detectors, so that the analysis can be implemented directly and in real time. This would allow transportation professionals to respond to recurring and nonrecurring congestion using real-time solutions.
• Tendency to use simpler analytical tools and those available in house, although they might not be the best tools for the job. Because of lack of resources, past experiences, or lack of familiarity with other available tools, many agencies prefer to use a tool currently in their possession, even if it is not the most appropriate tool for the project.
• Biases against models and traffic analysis tools. These biases are not only because of the challenges listed above, but also because models are not always reliable and are often considered to be “black boxes.” Some transportation professionals prefer to use back-of-the-envelope calculations, charts, or nomographs to estimate the results. This may be adequate for simpler tasks; however, more complex projects require more advanced tools.
• Long computer run times. Depending on the computer hardware and the scope of the study (i.e., area size, data requirements, duration, analysis time periods, etc.), an analytical model run may range from a few seconds to several hours. The most effective approaches to addressing this issue involve using the most robust computer equipment available and/or carefully limiting the study scope to conform to the analytical needs.

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