Development of a Statistical Model to Predict Traffic Congestion in Winter Seasons in Nagaoka, Japan Using Publicly Available Data

Abstract

Since the weather condition can be a cause of serious traffic congestion, it is necessary to establish a methodology to forecast future traffic congestions caused by rainfall and snowfall. However, there are few studies with simple methods that are applicable for practitioners such as road administrators. Therefore, in this paper we challenged to construct a statistical model to predict locations and levels of traffic congestion in a city, using only existing data that is open to the public. We collected hourly precipitation amount, hourly snowfall amount and cumulative snowfall amount from the Japan Meteorological Agency as weather observation data and images of Google Maps as traffic congestion data. As a result of the correlation analysis, we found that the hourly precipitation amount and the hourly snowfall amount did not correlate much with the relative congestion level whereas the correlation between the cumulative snowfall amount and 18-hour snowfall amount was found to be high. Consequently, a logistic regression analysis was conducted to explain the relative congestion level at various points on the roads using the 18-h snowfall amount and the cumulative snowfall amount. As a result, the model demonstrated good performance to reproduce the occurrence of increase in traffic congestion levels with >80% hit rates. In future, we would like to improve the present model to forecast potential road congestion based on weather forecast by using highly accurate weather information and longer term data.