Optimize travel time messaging operations by improving the way in which data is collected, analyzed, and displayed.
Four American Cities' experiences with Travel Time Messaging on Dynamic Message Signs.
Made Public Date

Travel Time Messaging on Dynamic Message Signs – Chicago, IL; Houston, TX, Nashville, TN, and Portland, OR


The Federal Highway Administration's Office of Operations activities involving travel times on Dynamic Message Signs (DMS) encourage and assist states and metropolitan areas in posting travel time messages. In May 2005 case studies were done on four cities (Chicago, Houston, Nashville and Portland, Oregon) that currently post travel time messages via DMS. The four case studies document the planning, deployment, operations, public outreach, and lessons learned of each cities travel time messaging on DMS system.

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Lessons Learned

For travel time messaging systems to be effective, data needs to be accurate and reflect real time conditions. Data collection and analysis must be done in a manner that is both quick and efficient, while at the same time preserving accuracy. Agencies seeking to implement travel time messaging on DMS will find the following lessons helpful in planning and designing methods for data collection, analysis, and presentation.

  • Draw from multiple data sources when calculating travel times. The availability of more than one resource for estimating travel times can lead to more accurate travel times being calculated. Multiple data sources provide an element of redundancy that strengthens the accuracy of travel times displayed on DMS.
  • The Illinois State Toll Highway Authority (ISTHA) has the ability to calculate travel time from three sources: loop detectors, radar sensors, and toll transponder data. ISTHA’s ability to estimate accurate travel time came as a result of electronic toll collection (ETC) using the IPASS transponder, which provided near real-time data. However, it was soon noted that travel time estimates lagged when traffic was in a transition stage (e.g. free flowing to congested). To mitigate this problem ISTHA deployed RTMS (radar) sensors. Along certain segments IPASS and RTMS data overlap, providing a level of redundancy.
  • Regularly assess travel time data accuracy during initial implementation. The greatest risk for inaccuracy in estimating travel time occurs during initial deployment. Conducting regular data accuracy tests will allow agency staff to work out any bugs in the system until stability is established.
    • When Houston TranStar’s travel time messaging system began, operators used the CCTV network to conduct frequent data accuracy test by comparing travel times calculated by the TranStar system against the time it took individual vehicles to drive a given segment of roadway. Once the system's stability was established, it was determined that such verification efforts would not need to be conducted as often.
    • To provide assurance that travel time data was accurate, ISTHA compared video observations of traffic flow with the travel time estimates of the traffic and incident management system (TIMS) software. ISTHA also directed their on-call consultant to conduct a probe-vehicle travel time test to measure the accuracy of the travel times provided. The results of this test indicated that the travel times produced by the system were sufficiently accurate for release to the public. Currently data quality is checked annually using probe vehicles.
    • Like Houston TranStar, the Tennessee Department of Transportation (TDOT) uses CCTV to verify travel times in Nashville.
  • Automate system operations to create smoother data management processes. Although more expensive initially, automated data collection, analysis, and presentation on DMS provides significant long-term benefits. Automation allows for problems to be immediately identified and addressed from a transportation management center).
    • Portland’s travel time messaging system is largely automated and operators generally rely on the software's failure management subsystem to notify them if a problem occurs (e.g., a drop in data accuracy). ODOT is currently planning modifications to its travel time software to ensure that it accurately calculates travel times when an entire data collection station ceases to function or loses the ability to transmit its data to the travel time server.
    • Houston TranStar's travel time messaging system was initially manual in nature. One operator posted travel times to about 40 DMS every 15-20 minutes during peak travel times. TranStar worked with the Texas Transportation Institute (TTI) to develop an automated travel time processor and the Southwest Research Institute (SwRI) to develop an automated DMS interface for posting messages. This fully automated system currently posts travel times to 81 signs every 10 minutes (5:30 am-7:30 pm), with some DMS updated more frequently depending on need, for an average of 4900 messages posted per day. If this level of service were to be provided via the manual calculation of travel times/posting of DMS messages, at least four operators would be needed per shift.

A common factor behind successful travel time messaging systems is an effective data management operation. When displaying travel times on DMS, accuracy is of the utmost importance, making it necessary to conduct regular tests during the initial implementation and to draw from multiple sources, should they exist. To provide travel times in a way that is both useful to drivers and efficient for the agency, data collection and analysis should be automated to reduce error and increase the speed at which data is collected, analyzed, and displayed. This experience and guidance helps to address the ITS goals of mobility and efficiency.