Vehicle trajectory collection is critical for intelligent transportation systems and tasks such as driving behavior analysis, travel time measurement, and traffic planning. Object tracking through computer vision can be used to obtain vehicle trajectories; however, trajectory information collected from a single camera is limited because of the camera's limited field of view. In multi-target multi-camera (MTMC) tracking, multiple camera views are integrated to associate various trajectories to a single vehicle by matching the vehicle's appearance with the trajectories. These cameras might have overlapping or nonoverlapping fields of view. Trajectory information from MTMC tracking can be used for driving behavior analysis, traffic congestion estimation, and route planning. However, color tones and angles differ between cameras; thus, trajectory association is challenging. In MTMC tracking, appearance, spatial, and temporal information can be integrated to reduce identification failures. This paper proposes a trajectory association framework for travel time and traffic flow estimation. The effects of spatial and temporal information on performance were evaluated for the AI City Challenge dataset and a self-collected dataset. The F1 scores obtained for these two datasets were 0.917 and 0.897, respectively. The inclusion of spatial and temporal information improved the F1 scores by approximately 0.06-0.72. The errors for estimating travel time and vehicle behavior (turning or straight movement) were approximately 3 sand 15 %, respectively, for various camera angles.