Common Measures and Metrics for Work Zone ITS Evaluations
The initial project plan for this study included a long list of potential measures and metrics for use at each site. Early evaluations included several metrics, but some proved difficult to quantify due to issues such as limited or incomplete data and difficulties determining whether the metric was positively influenced by the ITS deployment. Latter site evaluations included a smaller set of the most promising metrics. For example, rate of diversion was the primary metric for the Texas site, where the main objective of using the system was to divert traffic around the work zone during congested periods.
The study team sought to quantify benefits at a number of sites for comparative results. The study team designed this approach to avoid drawing conclusions from one site or one system. This approach limited the resources that went toward any one site but allowed the study team to focus on the most logical metrics that directly related to the goals and objectives for the deployment. The study team focused most resources per site on the primary goals and objectives.
The following paragraphs outline common measures and metrics and their application across some of the sites.
Deploying agencies cited improved mobility as a main goal for the ITS deployment at each site. Within mobility, more specific measures included delay, travel time, and reduced demand. However, many of the systems had limited detector coverage and none of the deployments had enhanced travel time data such as through license plate recognition technology. While improved mobility was a key goal cited at all of the sites, system design and layout often made it difficult to specifically measure.
For the Arkansas site, the study team measured mobility impacts through user surveys. For the Texas and DC sites, the study team measured improvements in mobility by collecting data on diversion rates that would reduce demand on the mainline and thereby improve mainline mobility. One limitation of this approach involved data collection on the alternate routes, in that it was too costly to collect alternate route data and difficult to design data collection plans since it was impossible to know in advance when the traffic conditions would trigger the systems to recommend alternate routes.
Agencies also cited safety benefits as a main goal for several of the deployments, although agencies were reluctant to cite specific safety measures such as reduced crash rates when referring to goals and objectives for the system. Additionally, it is very difficult to draw conclusive findings from safety performance measures such as number of crashes and crash rates since data quality and quantity are often limited, and researchers face a significant challenge in determining whether a crash was specifically caused by the work zone (i.e., whether or not it would have still happened had the work zone not been in place).
For the Michigan site, the main objective was to reduce aggressive maneuvers at the lane drop. Therefore, safety was a primary metric, and the study team quantified the reduction in observed aggressive maneuvers and forced merges as a surrogate measure for safety performance. At the early sites, the study team investigated crash analysis but it was difficult to draw a meaningful conclusion due to the limited timeframe for collecting data and the several month long lags in reporting crash data. Crash and crash rate analyses did not produce significant findings at the sites due to common reporting issues and the need for long periods of time during which to collect data.
The objective of ITS can often be to provide real-time information on work zone conditions in the field. Deploying agencies often take this idea one step further and develop a plan for ensuring the information was not only real-time but was also useful to motorists in helping them plan their trip. For example, posting "work zone ahead expect delays" is not as specific as "30 minute delays ahead." The more specific the message, the better motorists can make information decisions about which route to take.
At the Arkansas site, the study team used two metrics to determine the effectiveness of the system. These metrics included "Travelers will use the work zone ITS" and "The use of ITS in the work zone will improve trip planning." While the main focus of this study was to quantify the benefits of each system, the study team analyzed the effectiveness of the information where practical. For the DC and Texas sites, travelers avoided mainline congestion but may have experienced some congestion in some cases on the alternate routes.
This metric is less about quantifying the benefits of a system, and more about evaluating how well a system performed in the field. Data analysis at each site proved useful in helping the study team determine how well the system performed. Additionally, error logs and missing data often gave indications of the overall performance. System algorithms are often proprietary, making it challenging to determine with confidence how well the system performed based on its design.
Other metrics such as productivity, worker exposure to hazards, and construction efficiency proved more difficult to measure and were not analyzed at most sites (except Arkansas) due to burdensome data collection needs and challenges with determining the specific cause of a benefit. Additionally, the systems may have indirectly affected measures such as productivity because measuring the direct, quantified impact proved difficult. The other work zone ITS studies analyzed as part of this project focused mainly on direct traffic impacts. For the Arkansas site, the study team evaluated user perspectives on system performance, functionality, and benefits to motorists.
The following sections highlight in detail the measures and metrics analyzed at each site, along with the results of each site evaluation. The sites are presented in reverse chronological order with the most recent site first.Previous | Next