Examining the influence of network, land use, and demographic characteristics to estimate the number of bicycle-vehicle crashes on urban roads

The focus of this paper is to examine the influence of network, land use, and demographic characteristics on the number of bicycle-vehicle crashes, and to develop area-level bicycle-vehicle crash estimation models (safety performance functions) for urban roads. Mecklenburg County in the State of North Carolina was considered as the study area. The reported bicycle-vehicle crash data, from 2010 to 2015, along with the network, land use, and demographic characteristics data were obtained from the local agencies. Data within a one-mile buffer of 119 selected locations was then captured. Data for 99 selected locations were used for the modeling purpose, while data for the remaining 20 selected locations were used for validating the models. Six alternate models were developed, considering various combinations of explanatory variables that are not correlated with each other. As the bicycle-vehicle crash dataset used in this research was observed to be over-dispersed (variance greater than the mean), Negative Binomial log-link distribution-based models were developed. The validation dataset was used to compare the estimated number of bicycle-vehicle crashes from each model with the actual number of bicycle-vehicle crashes. The results obtained from the analysis and modeling suggest that bicyclists are more often involved in crashes while traveling on segments with no bicycle lane, the traffic light, 45 mph as the speed limit, and in commercial activity, research activity, institutional, multi-family residential (densely populated), and heavy industrial areas. The computed Moran's Index values indicate weak to no spatial correlation between the residuals of each model. However, the residuals seem to depend on the area type and the number of bicycle-vehicle crashes.

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  • English

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  • Accession Number: 01709422
  • Record Type: Publication
  • Files: TRIS
  • Created Date: May 2 2019 3:06PM