Assess and Quantify Performance of Ramp Metering Systems by Periodically Checking for Data Consistency in Reported Traffic Speeds, Flows, and Occupancies.
System Performance of Ramp Meters in Ohio Assessed for Effectiveness in Traffic Management.
Made Public Date

Ohio, United States

United States

Investigating the Feasibility of Coordinated Ramp Metering Along Freeway Corridors in Ohio


Ramp metering is an important tool for controlling the flow into the freeway to mitigate or prevent the freeway congestion. Ohio Department of Transportation (ODOT) has ramp metering systems operating in two urban freeway corridors. These meters run on a daily schedule or under local traffic responsive plans; remote traffic microwave sensor (RTMS) units were placed upstream and utilized to sense vehicle occupancy. This study assessed and quantified ramp meter performance to determine how ODOT could improve the performance of the existing system. These results were then used to determine practices that best ensure a well-performing traffic responsive metering system. Researchers also investigated coordinated ramp metering systems, their overall benefits to traffic flow, and the feasibility of their application statewide.

Lessons Learned

  • Periodically check the traffic data obtained by ramp metering systems (e.g., traffic speeds, flows, and occupancies) to assess and quantify system performance. The system cannot respond to prevailing traffic conditions if a sensor is not operational. Data from all sensors and radars, and real-time data should be periodically reviewed for consistency.
  • Quantify ramp metering system performance by programming metering algorithms to respond differently to recurring and non-recurring congestion. Algorithms typically assign greatest weight to the sensors closer to the ramp and decay the weight as sensor distance increase. Though this approach is best suited for recurring congestion, if possible, run two or more metering algorithms for a given meter which gives higher weights to sensors further downstream that can detect non-recurrent congestion, and choose the most restrictive output.
  • Plan for sensor malfunctions.  It is important to plan how to adapt the ramp metering algorithm when one or more sensors are not operational. The best solutions include either adapting the time of day metering or have the traffic management center staff regularly check the location and manually adjust the metering rate, while the non-operational sensors are being restored to operational status.
  • Consider data smoothing to reduce volatility in the metering rates. When the sensors are polled within a short time period, e.g. 20 seconds, the metering rate can exhibit large jumps from one cycle to the next. This study showed that low pass filters, such as a moving average and an exponential filter can be used to smooth the sensor data to reduce volatility in the metering rates.
  • Consider the feasibility of using other sources of traffic data for ramp metering. In order to account for sensor malfunction in current ramp metering implementations or for corridors without sensors, other sources of traffic data can be invaluable. Real-time speed data are now available through various data vendors. However, these data sources may have their idiosyncrasies such as time lag, imputed data and the fact that they provide speed instead of occupancy, the main input used by ramp metering algorithms. This study considered the feasibility of using available real-time speed data, and presented a simple approach to convert speed to occupancy.
  • Investigate the limitations of the existing metering system and explore the benefits of a coordinated ramp metering system. Limitations must be understood to successfully improve system performance and data availability and determine which coordinated ramp metering systems best address the current limitations. This understanding ensures a well-performing system that can then be expanded to accurately obtain traffic data and optimize traffic flow.