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Evaluation of Variable Advisory Speed Limits in Work Zones

Status: Complete

Description: Variable advisory speed limit (VASL) systems could be effective at both urban and rural work zones, at both uncongested and congested sites. At uncongested urban work zones, the average speeds with VASL were lower than without VASL. But the standard deviation of speeds with VASL was higher. The increase in standard deviation may be due to the advisory nature of VASL. The speed limit compliance with VASL was about eight times greater than without VASL. At the congested sites, the VASL were effective in making drivers slow down gradually as they approached the work zone, reducing any sudden changes in speeds. Mobility-wise the use of VASL resulted in a decrease in average queue length, throughput, number of stops, and an increase in travel time. Several surrogate safety measures also demonstrated the benefits of VASL in congested work zones. VASL deployments in rural work zones resulted in reductions in mean speed, speed variance, and 85th percentile speeds downstream of the VASL sign. The study makes the following recommendations based on the case studies investigated: 1. The use of VASL is recommended for uncongested work zones to achieve better speed compliance and lower speeds. Greater enforcement of regulatory speed limits could help to decrease the standard deviation in speeds. 2. The use of VASL to complement the static speed limits in rural work zones is beneficial even if the VASL is only used to display the static speed limits. It leads to safer traffic conditions by encouraging traffic to slow down gradually and by reminding traffic of the reduced speed limit. A well-designed VASL algorithm, like the P5 algorithm developed in this study, can significantly improve the mobility and safety conditions in congested work zones. The use of simulation is recommended for optimizing the VASL algorithms before field deployment.

Report: variable_advisory_speeds_w_cvr.pdf 3.25 mb (published 2013)

State conducting research: Missouri


  • Sun, Carlos (University of Missouri, Columbia)
  • Edara, Praveen (University of Missouri)
  • Hou, Yi (University of Missouri)
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