Updating Annual Average Daily Traffic Estimates at Highway-Rail Grade Crossings with Geographically Weighted Poisson Regression

Huiyuan Liu, Myungwoo Lee, Aemal J. Khattak

Research output: Contribution to journalArticle


Highway-rail grade crossings (HRGCs) are unique nodes in the transportation system that facilitate the movement of rail and highway traffic. Various mathematical models are available that provide safety assessments of HRGCs. A chief ingredient of these models is the annual average daily traffic (AADT). One of the main sources of data for such models is the Federal Railroad Administration (FRA)’s Grade Crossing Inventory. A substantial portion of the AADT data in the inventory is outdated. This paper investigates the effects of using out-of-date rather than up-to-date AADT values, using two safety assessment models to isolate the differences. Results show that the use of out-of-date AADT data generates biased rankings of HRGCs based on safety considerations. Since collection of AADT data is resource-intense, a methodology based on a geographic information system for estimating updated AADT is presented. This methodology utilizes limited traffic counts that are supplemented with additional publicly available data. An application using a geographically weighted Poisson regression model for 14 HRGCs gave results that closely matched AADT values based on 2018 field traffic counts at those HRGCs. This method provides an alternative to costly field-data-based updating of AADT in the relatively extensive Grade Crossing Inventory database. Limitations of the research and suggestions for future research complete this paper.

Original languageEnglish (US)
JournalTransportation Research Record
Publication statusPublished - Jan 1 2019


ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Mechanical Engineering

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