Traffic surveillance using computer vision techniques is an emerging research area. Many algorithms are being developed to detect and track moving vehicles in daytime in effective manner. However, little work is done for nighttime traffic scenes. For nighttime, vehicles are identified by detecting and locating vehicle headlights and rear lights. In this paper, an effective method for detecting and tracking moving vehicles in nighttime is proposed. The proposed method identifies vehicles by detecting and locating vehicle lights using automatic thresholding and connected components extraction. Detected lamps are then paired using rule based component analysis approach and tracked using Kalman Filter (KF). The automatic thresholding approach provides a robust and adaptable detection process that operates well under various nighttime illumination conditions. Moreover, most nighttime tracking algorithms detect vehicles by locating either headlights or rear lights while the proposed method has the ability to track vehicles through detecting vehicle headlights and/or rear lights. Experimental results demonstrate that the proposed method is feasible and effective for vehicle detection and identification in various nighttime environments.