5. Recommendations

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Promote Local Use of Archived Operations Data and Performance Measures

In the long run — especially as many more cities deploy TMCs and coverage for existing systems expand — a national performance measurement program will benefit from increased local involvement in archiving and use of operations data. As more local applications are developed, there will be increased pressure to ensure high quality data. Also, much of the initial data processing (quality control and aggregation) can be achieved at the local level, making the national process more efficient.

Recommendations: (1) Make archiving and performance measurement a major focus of the Planning for Operations effort. Operations data provide local planners a rich source of information for both traditional and innovative applications, and gives them “something in return” for the extra effort required to integrate operations into planning. (2) Develop 1-2 case studies highlighting how operations and planning agencies work together to achieve performance measurement from their agency perspectives. If appropriate, extend the case studies to encompass how Planning for Operations is achieved. (3) Promote standards and guidelines for data collection, data quality maintenance, performance metrics, and analysis procedures for archived data. (4) Document the differences in methods for developing performance measures (modeling, floating cars, roadway detectors, probe vehicles, cell-phone tracking); identify adjustment techniques to make them compatible.

Presentation and Use of Performance Measures in Decision-Making

As discussed earlier, identification of which performance metrics (measures) should be used in a congestion monitoring program has received a good deal of attention over the past few years. The remaining three pieces of the performance measurement process are: What data are needed? How should the measures be presented? And, how should the measures be used in the decision-making process? This next step deals with the second and third of these issues. The toughest of these two issues is how performance measures influence investment and policy decisions.

Recommendations: (1) A scan should be conducted of different methods being used by transportation agencies to present congestion performance measures to the public and decision-makers. From this, a compendium highlighting the most effective presentation methods should be compiled. (2) Case studies of two or three transportation agencies that have aggressive congestion performance monitoring programs in place should be conducted to document how the measures have influenced investment and policy decisions.

Improve Traffic Detector Data Quality at Its Source

A high level of data quality is absolutely essential for an archive to be useful to a wide variety of interests (including performance measurement). If users perceive that the data are not of sufficient quality, the archive will not get used and interest will wane. The best way to ensure quality data is to have the original collectors (owners) use it for their own benefit. For example, the traffic management centers should use this information for planning their activities, deploying operational forces, programming maintenance efforts, etc. Improving data quality also includes developing formal review procedures (which can be automated through software), routinely publishing data quality statistics, and establishing a feedback process whereby users can alert collectors/owners of quality problems not originally detected. However, that is the easy part. A much more difficult part of maintaining a high level of data quality is ensuring that field devices are properly installed, calibrated, and maintained. These activities require significant investment by data collectors/owners.

Recommendations: Document the costs of proper detector installation, calibration, and maintenance activities, especially with regard to type of equipment and the level of data quality (accuracy in the field measurements) achieved. Identifying best practices for each of these activities would also foster archive development and use. Promoting the use of quality control software by data collectors/owners (i.e., traffic management centers) would also support maintenance of quality data. Document the implications on performance measures of sparse detector coverage as well as poor data quality.

Integrate Event Data at the Local Archive Level

The congestion performance measures developed so far focus mainly on an overall picture of congestion using traffic detector, probe, or modeled data. However, to be more useful for implementing operations strategies, the causes of congestion should be tracked at a detailed level. In other words, what factors (“events”) have contributed to overall mobility and what are their magnitude; factors include traffic incidents, weather, work zones, changes in traffic demand, special events, and recurring bottlenecks. If the share of total congestion attributable to these sources can be produced, strategies targeted at the root causes can be developed. Identifying the events that are restricting mobility is important at both the national level (development of overall programs) and the local level (development of specific actions). Key in this effort is the capture of roadway event-related data in a consistent manner. These data must be fully integrated with traffic detector and other forms of traffic data so that the events’ influence on congestion patterns can be ascertained.

Recommendations: (1) An effort should be undertaken to harmonize the data requirements required for documenting roadway events from performance measurement and archive perspectives as opposed to purely an operational perspective. This involves review of and potential modification to existing ITS standards and standards used in the data systems of non-transportation agencies (especially police computer-aided dispatch systems). (2) A scan of current event/traffic data integration practices among ITS data archives would reveal best practices and potential pitfalls.