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Since nearly the beginning of the wide spread adoption of the automobile, motorized traffic data collection has occurred so that decision makers have information to plan the transportation system. Widespread motorized traffic data collection has allowed for estimating traffic volumes using developed extrapolation methods whereby short-term counts in sample locations can be expanded to longer periods. As states and local planning agencies make investments in bicycle infrastructure and count programs develop, similar extrapolation methods will be needed. The only available guidance on extrapolating bicycle counts comes from the National Bicycle and Pedestrian Documentation Project (NBPDP), yet no validation of these factors have been done to assess their usability in specific areas. Using bicycle traffic count data from the Central Lane Metropolitan Planning Organization Count Program in Oregon, this research demonstrates that using study area data to generate time-of-day factors. Factors are generated in two separate ways in order to reduce error from estimating daily bicycle volumes. Factors groups are developed using bicycle facility type where counts are collected. This research also seeks to add to the literature concerning bicycle travel patterns by using study area data to establish a university travel pattern exemplified by a flat hourly distribution from morning to evening.ii

Monitoring nonmotorized traffic is becoming increasingly common practice at local and state departments of transportation. These travel activity data are necessary to monitor the system and track progress toward active transportation policy and program goals. A common problem is that permanent count site data are often missing, making those sites less useful. Being able to accurately estimate those missing data records functionally increases the amount of data available to use by themselves as metrics for monitoring traffic but also makes available more data for factoring short-term sites. Using nonmotorized traffic counts from several cities in Oregon, this research compared the ability of day-of-year (DOY) factors, a statistical model, and machine learning algorithms to accurately impute daily traffic records for annual traffic estimation. Based on exhaustive cross-validation experiments using data not missing at random scenarios, this research concluded that random forest and DOY factor approaches could be used to impute daily counts for nonmotorized traffic but each approach comes with tradeoffs. Though for many missing data scenarios random forest performed best, this method is complicated to estimate and apply. DOY factor-based methods are simpler to create and apply, and though more accurate in scenarios with significant amounts of missing data, they were less flexible given the need for data from neighboring count sites. Negative binomial regression was also found to work well in scenarios with moderate to low amounts of missing data. This work can inform nonmotorized traffic count programs needing vetted solutions for traffic data imputation.

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