Abstract: Urban big data provides data support for exploring the behavior characteristics of urban residents’ travel. Combining data mining with visualization technology, the resident travel law and urban space interaction characteristics of Lanzhou are studied based on the taxi GPS trajectory. Firstly, the travel characteristics and the inter-district spatial interaction characteristics of the four districts are analyzed. Then, the traffic trips between the urban grid and urban traffic hotspots of weekday and weekend are studied by the CLARA clustering algorithm. Finally, a directed-weighted complex network model is established to analyze the space interaction characteristics between urban traffic hotspots. The results show that there are significant differences in the spatial and temporal characteristics of urban travel behaviors and urban space interaction characteristics in the weekday and weekend. Compared with weekend, urban travel in the weekday are more compact and purposeful. The cluster structure of travel topology presents a "dumbbell" distribution shape matching the valley topology of Lanzhou. The spatial interaction between adjacent clustering areas close to the city center is strong. The results of this study can provide decision-making services for urban traffic management and residents’ travel.

Key words: resident travel, interaction characteristics, traffic hotspots, CLARA clustering algorithm, GPS trajectory