Multiscale Crash Analysis: A Case Study of Integrating FARS, Maryland's Crash Data, and Montgomery County's Traffic Violation Data

Road safety is a serious issue raising increased public concerns. In this paper, the authors analyze road safety with an integration of multiple data sources on multiple scales. As a case study, the authors consider three datasets, including the nationwide Fatality Analysis Reporting System (FARS), the statewide traffic crashes in Maryland (MDCrash), and the countywide traffic violations in Montgomery County, MD (MoCoVio). For data integration, the authors first exploit basic common characteristics among all the datasets. The time interval statistics of the datasets are found stable and can be modeled into parametric statistical distributions. The authors then check essential features of the datasets corresponding to road safety and the relationship among them. The authors also compare the patterns of six common risk factors across all the three datasets. It is found that despite the difference in the features of the datasets, the pattern s of DUI/DWI are very similar. Next, the authors explore practical values of the multiple data integration on road crash analysis. The crash risk patterns extracted from data fusion is shown to be rather valuable. By identifying determinant risk factors in the patterns, the authors can better understand the effects of other risk factors. In addition, conditional risk matrix can be computed from data integration to measure the probability of the injury levels and to evaluate the impact of each individual risk factor on injuries. Finally, the authors conduct a multi-source data integration to discover the safety factors for pedestrians, where the authors obtain temporal patterns from FARS but acquire spatial patterns from the traffic crash and violation data. The results indicate that, in comparison with only using FARS, integrating multiple data has the power of showing more in- sights of the patterns on risk factors for traffic crashes, which allows us to not only better optimize limited resources but also realize more effective countermeasures for enhancing road safety.

Language

  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 19p

Subject/Index Terms

Filing Info

  • Accession Number: 01658963
  • Record Type: Publication
  • Report/Paper Numbers: 18-02283
  • Files: TRIS, TRB, ATRI
  • Created Date: Jan 8 2018 10:33AM