Potential trend discovery for highway drivers on spatio‐temporal data

Inter-city transportation plays an important role in modern cities, and has accumulated massive spatio-temporal data from various sensors by IoT (Internet of things) technologies. Travel characteristics future trends highway behind are valuable for traffic guidance personalized service. As a routine domain analysis, trend discovery drivers faces challenges processing efficiency predictive accuracy. Insufficient profiles those available directly, sensible executive latency on huge is hard to guarantee, inadequate features among correlations hinder the analytical In this paper, travel-characteristic based method proposed discover potential payment identity drivers. Considering time, space, subjective preference objective property, travel modeled monthly toll stations, through which errors can be reduced gradient boosting classification. With real-world one Chinese provincial network, extensive experiments case studies show that our second-level with more than 85% F1-score discovery.