Toward Understanding and Reducing Errors in Real-Time Estimation of Travel Times

Abstract

In recent years, the increased deployment of the infrastructure of intelligent transportation systems has enabled the provision of real-time traveler information to the public. Many states as well as private contractors are providing real-time travel-time estimates to commuters to help improve the quality and efficiency of their trips. Accuracy of travel-time estimates is important: inaccurate estimates can be detrimental to travelers, particularly when such estimates are less accurate than a person's ability to predict traffic on the basis of experience. Improving the accuracy of real-time estimates involves identifying and understanding the sources of error. The errors found during the evaluation of real-time travel-time estimates in Portland, Oregon, were explored and solutions are provided for reducing estimation error. The midpoint algorithm used by the Oregon Department of Transportation was used to estimate travel times from speeds obtained from loop detectors. The estimates were assessed for accuracy by comparisons with ground truth probe vehicle runs. The findings from the study indicate that 85% of the travel-time runs had errors less than 20% and, further, that accuracy varied widely between segments. The evaluation of high-error runs revealed the main causes of errors as transition traffic conditions, failure of detectors, and detector spacing. Potential solutions were identified for each source of error. In addition, a method was tested for evaluating the benefits of additional detectors by simulation of virtual detectors. The results indicated that additional detection helps in reducing the mean average percentage error in most cases, but the location of detectors is critical to error reduction.