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Limited field investigations have evaluated the impact of traffic on the snow melting capacity of salts. This paper will describe an extensive field study that includes 1,200 tests that are conducted during 100 snow events over three winter seasons (2012, 2013, and 2014) to quantify the traffic impact on snow melting capacity. The tests will be conducted in an active parking lot, and on parking stalls and driveways, which could increase the applicability of the results to real-world scenarios. The deicing performance of salt will be compared between both areas with different traffic patterns for bare pavement regain time (BPRT). The developed model quantified the reduction in BPRT from 21% for a low traffic volume of 5 vehicles/h to an 86% reduction for a high traffic volume of 50 vehicles/h. The deicing performance models that were calibrated for parking stalls and driveways could be directly used to determine the most cost-effective application rates to treat parking lots and sidewalks based on specific weather, facilities, and traffic conditions. [ABSTRACT FROM AUTHOR]

Accurate forecasts of public transportation ridership are key components to a transit agency's management program. These forecasts are used to develop revenue projections that are then imputed into short-term and long-term maintenance, rehabilitation, and capital investment programming. The forecasts are complicated by the fact that many of the causes of the underlying variability, such as socioeconomic and land-use factors, are not constant over the network. Failure to account for these spatial processes will yield biased, inefficient, and inconsistent demand estimates. To overcome these challenges, the current paper presents a spatial Durbin model for analyzing the change in the New York City subway ridership between 2011 and 2016. The results indicate that subway stations experienced a greater increase in ridership over the study period when the stations served more train lines, were located in areas comprised of census tracts with a greater number of tax units (residential, commercial, etc.), or served lower mean household incomes. Furthermore, the subway stations located in areas surrounded by census tracts with more commercial property or higher median family income are also expected to have a greater increase in ridership. Lastly, ridership at a given station decreases due to an increase in ridership at neighboring stations. This may indicate that a change in ridership at a station is due, in part, to riders in a region changing which station they use instead of riders shifting from alternative modes of transportation. [ABSTRACT FROM AUTHOR]