Traffic Flow Forecasting for Urban Work Zones
None of numerous existing traffic flow forecasting models focus on work zones. Work zone events create conditions that are different from both normal operating conditions and incident conditions. In this paper, four models were developed for forecasting traffic flow for planned work zone events. The four models are random forest, regression tree, multilayer feedforward neural network, and nonparametric regression. Both long term and short term traffic flow forecasting applications were investigated. Long term forecast involves forecasting 24 h in advance using historical traffic data, and short term forecasts involves forecasting 1 h and 45, 30, and 15 min in advance using real time temporal and spatial traffic data. Models were evaluated using data from work zone events on two types of roadways, a freeway, i.e. I 270, and a signalized arterial, i.e. MO 141, in St. Louis, MO, USA. The results showed that the random forest model yielded the most accurate long term and short term work zone traffic flow forecasts. For freeway data, the most influential variables were the latest interval's look back traffic flows at the upstream, downstream, and current locations. For arterial data, the most influential variables were the traffic flows from the three look back intervals at the current location only.
IEEE Transactions on Intelligent Transportation Systems