Population‐Demand‐Congestion Prediction Framework: Traffic Dispersion Through Traffic Prediction Using Population Distribution Based on Mobile Big Data

Traffic congestion mitigation is essential for reducing environmental impact and improving quality of life. As constructing new infrastructure is costly, enhancing the operational efficiency of existing road networks is crucial. One promising strategy is to influence driver behaviour by providing congestion predictions before route decisions are made. However, accurate long‐term prediction several hours ahead remains challenging when relying solely on conventional traffic data. This study proposes a population‐demand‐congestion prediction framework that leverages real‐time population data derived from cellular networks to capture latent travel demand. Directly predicting travel time from high‐dimensional and sparse population data results in a nonlinear high‐dimension, low‐sample‐size (HDLSS) problem. To address this, the proposed framework decomposes the task into two stages: estimating traffic demand from population distribution using linear regression under an HDLSS setting, and predicting travel time from the estimated demand using a nonlinear model in a lower‐dimensional space. This decomposition aligns the statistical model with the physical causality of traffic formation and improves estimation stability. Empirical evaluation demonstrates accurate congestion prediction several hours ahead. Furthermore, field experiments on major expressways show that providing predictions influences driver behaviour in up to 89.0% of users, demonstrating the framework's practical potential for real‐world ITS applications.