Impact of Data Gaps on the Accuracy of Annual and Monthly Average Daily Bicycle Volume Calculation at Permanent Count Stations

Abstract

This research tackles the problem of missing cycling count data at permanent count stations that may experience frequent sensor breakdowns along the year. The purpose is to study the impact of missing rate (i.e. missing amount of data) on the accuracy of monthly and annual average daily bicycle volume estimates (MADB and AADB) as well as monthly adjustment factors (MF). The study made use of a full year of daily bicycle counts at six count stations in the City of Vancouver, Canada. Repeated random samples of daily bicycle count of different missing rates were drawn from the full data set and used to calculate MADBs and AADB at each count station. The sample estimates were compared to the actual estimates and the minimum, maximum, and average estimation errors for each sampling scenario were calculated. The results showed that the impact of missing counts on the estimation accuracy of the AADB is minimal where the average errors did not exceed 5%, even for high missing rates. This is conditional that the data are missing randomly and there are a few samples that cover each month of the year. On the other hand, the estimation errors or MADBs were found to be relatively high when the missing rates are high. The maximum estimation errors of MADBs were 39%, 23%, 14%, 8%, and 6% for missing rates of 90%, 70%, 50%, 30%, and 10%, respectively. These results indicate that even if half of the permanent counter data is missing, the maximum error would not exceed 14%. The combined impact of AADB and MADB estimation was captured by comparing the MFs calculated using full data versus those calculated for incomplete data. The results showed maximum errors of 94% and 34% for missing rates of 90% and 70%. Finally, a Multiple Imputation (MI) method was applied to fill in data gaps for high missing rates. The estimation errors of AADBs were found to be almost the same while the estimation errors of MADBs and MFs were reduced significantly.