Optimal Deployment of Highway Traffic Detectors

Various types of traffic sensors, including loop detectors, radar, and video cameras, are widely deployed on highways to provide data for traffic management applications, such as ramp metering control, incident detection, and travel time estimation.

However, a systematic analysis of the data requirements of those applications has rarely been conducted, and sensors are often installed on a case-by-case basis without knowing whether the predicted benefits are fully realized.
To provide fresh answers to practitioners, CCIT assembled an impressive line-up of traffic management and modeling experts, under the leadership of Civil Engineering Systems Professor Alexandre M. Bayen.

CCIT will develop a decision-support tool that recommends an optimal, traffic sensor deployment strategy on a given freeway corridor. Taking into account corridor characteristics such as its setting (i.e. rural/mid-size/urban), ramp locations, number of lanes, and existing sensors, the tool will suggest the locations and types of additional traffic detectors needed to yield a set level of information.

An innovation stemming from this project is the design of quality measures to
quantify information from a network of traffic detectors, which is relevant to key traffic applications. The measures are used to express application requirements, and to establish the relationship between sensor deployment strategies and data quality.

For instance, despite radical technical improvements in data collection techniques over the past decade, accurate and timely travel-time estimates remain rare, and systematic studies of their quality are surprisingly sparse. One reason may be that the industry has not developed widely accepted metrics and methods to measure the accuracy of travel time estimates.

Measuring the quality of travel-time estimates is important for the following reasons:

• The margin of error in travel time estimates should be better understood so that drivers and operators can develop adequate expectations.

• Robust validation and monitoring practices for travel-time estimates can point
to needed improvements in traffic-data collection and ultimately build up the confidence of network operators in the information.

• In the context of public-private partnerships for data collection, aggregation and dissemination, quality metrics are needed to enable government agencies and technology providers to reach business agreements.

Leveraging those observations, the project team is conducting an extensive benchmark evaluation of travel-time estimates. The methodology and proposed quality measures are intended to set a standard that can be universally applied.
Similar efforts are underway to analyze the quality of ramp metering and freeway performance monitoring tools.