Travel time prediction of expressway based on multi-dimensional data and the particle swarm optimization–autoregressive moving average with exogenous input model

In order to meet the fine demand of different travelers, a multi-dimensional prediction method of travel time is proposed combining the toll collection data and meteorological data of highway. First, a logical model of multi-dimensional database is designed including vehicles’ dimension, meteorological dimension, and time dimension. Second, aiming at integration of the toll collection data and meteorological data, a matching method is presented within the space and time scale. Then, the multi-dimensional database is constructed. Next, an autoregressive moving average with exogenous input model is constructed using the travel time series and traffic flow series. The maximum likelihood estimation method is used to solve the parameters of the autoregressive moving average with exogenous input model. Considering the complexity and solving difficulty of the maximum likelihood equation, the particle swarm optimization algorithm is used to optimize the solution process. Finally, the toll collection data of two road links on Shenyang–Haikou expressway (G15) and the corresponding meteorological monitoring data are used to validate the algorithm. The results show that the prediction accuracy of the particle swarm optimization–autoregressive moving average with exogenous input model under normal and special conditions can be accepted and the absolute percentage error of road section between two neighboring toll stations is reduced by almost 5% after optimization.