Estimation of Speed Differentials on Rural Highways using Multilevel Models

Large speed differentials between highway segments are associated with an increase in accidents. Traditional speed differential measures, derived from single level linear regression models, suffer from serious deficiencies: underestimating the speed differential (due to intra-correlated data), and inflating the adequacy of the model’s explanation (due to aggregate data). High quality speed differential predictions are highly desirable right from the initial design phase, but the estimation process is not straightforward, and decision makers must recognize that speed differential predictions are subject to considerable uncertainty. This paper compares four models: two single level models, a conventional multilevel model, and a Bayes multilevel model. The results show empirically that multilevel models increase the accuracy and precision of speed differentials estimates, possibly with less data. The final part of this paper introduces a new, easy to interpret speed consistency measure that simply represents the probability that a vehicle exceeds a certain speed differential. This measure is calculated using a multilevel model and takes into account the uncertainty in the estimates of speed differentials. Overall, the authors show that a multilevel modeling approach can improve the quality of decision making that makes use of speed differential information in road design and road safety.


  • English

Media Info

  • Media Type: DVD
  • Features: Figures; References; Tables;
  • Pagination: 24p
  • Monograph Title: TRB 89th Annual Meeting Compendium of Papers DVD

Subject/Index Terms

Filing Info

  • Accession Number: 01152391
  • Record Type: Publication
  • Report/Paper Numbers: 10-1223
  • Files: TRIS, TRB
  • Created Date: Jan 25 2010 10:32AM