LAUSR

Predicting urban travel demand is important in perceiving the future state of a city, deploying public transportation resources, and building intelligent cities. Influenced by multifarious factors, urban travel demand data have high-frequency noise and complex fluctuation patterns. Current studies have focused on predicting urban travel demand via various models. However, there is little work that comprehensively considers natural environmental factors and socioeconomic factors affecting urban travel demand. Some improvements are made in this work. First, multifarious influencing factors are taken into consideration. Second, a novel random forest-based method for influencing factor data preprocessing is introduced. Finally, this work proposes an urban travel demand prediction model considering influencing factors (UTDP-IF). A principal component analysis algorithm is used to extract the principal components of different influencing factors for avoiding multicollinearity. Based on four data sets, this work evaluates a UTDP-IF and compares it with some typical models. Compared with baselines, the root-mean-square error of the UTDF-IF is reduced by approximately 29.44% on average, which can perfectly predict urban travel demand.