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This study aims to develop a method for estimating the traffic volume at locations and time periods where past data are unavailable. To achieve this, we constructed a model based on seven years of traffic volume observations on 22 main roads in Tokyo. The estimation method comprises two components: a traffic volume fluctuation pattern model that explains pre-clustered patterns and an annual average daily traffic model. Through our analysis, we made three key observations. First, the distance to the city center is a significant explanatory factor for monthly fluctuations, the month number for weekly fluctuations, the day-to-night population ratio for daily fluctuations, and the city center direction angle for hourly fluctuations. These calendar and road geometry variables account for 70-90% of the traffic volume fluctuation. Second, when estimating traffic volume for specific time periods using the traffic volume fluctuation pattern model, we explained approximately 60% of routes where no traffic volume observations were conducted and approximately 80% of routes where observations were made infrequently. Finally, the optimal number of clusters for the traffic volume fluctuation pattern, which maximized the coefficient of determination of the traffic volume model, was 8 for monthly fluctuations, 4 for weekly fluctuations, 6 for daily fluctuations, and 11 for hourly fluctuations. These findings made it possible to estimate traffic volumes on routes where traffic volume observations are not conducted and to interpolate traffic volumes for any given month or date using the results of periodic surveys conducted every few years.

Development of a Method for Estimating Traffic Volume Fluctuations that Considers Calendar Information and Road Network Geometry