## 1 Introduction
### Literature Review  
Accurately and timely obtaining microscopic and macroscopic traffic flow characteristic parameters and evolution trends forms the foundation for traffic control strategy optimization, evaluation, and feedback. While this study focuses primarily on vehicle trajectory data for signal control optimization, it is important to acknowledge the broader multi-modal transportation context. Urban traffic systems encompass various transportation modes, including pedestrians, cyclists, public transit, and emerging mobility services. Pedestrian and cyclist movements at signalized intersections significantly influence signal timing requirements, particularly for crossing phases and all-red clearance intervals. However, the current research scope is specifically designed for vehicle-based signal control optimization, as vehicle trajectories provide the most direct input for traditional traffic signal timing parameters such as flows, speeds, and coordination offsets. Future extensions of this methodology could incorporate pedestrian detection data and bicycle trajectory information to support comprehensive multi-modal signal control strategies. Currently, research on traffic parameter extraction based on vehicle trajectory data mainly focuses on the following aspects:  
Regarding link speed estimation, researchers have proposed various speed estimation methods based on trajectory data. [PERSON] and [PERSON] developed a trajectory reconstruction model using smoothing schemes to construct speed surfaces, achieving 6.3% mean absolute percentage error compared to ground truth [1]. [PERSON] et al. proposed real-time vehicle trajectory prediction using deep neural networks for traffic conflict detection at unsignalized intersections [2]. [PERSON] et al. demonstrated that multisource data-based dynamic methods significantly outperformed static approaches in freeway traffic state estimation [3]. [PERSON] et al. achieved 95.72% accuracy in vehicle speed forecasting using cellular floating vehicle data combined with neural network algorithms [4]. Early methods primarily involved simple average speed calculations, later evolving to include estimation methods considering spatiotemporal correlations and machine learning-based prediction models. [PERSON] et al. developed post-earthquake traffic travel time distribution estimation methods based on floating car data with reliability analysis [5]. These methods have achieved remarkable results in single-indicator speed and travel time estimation, providing a solid technical foundation for further construction of network-wide state assessment systems. In travel mode identification, research mainly focuses on identifying different transportation modes from trajectory data (such as walking, cycling, driving, and public transit) and trip purposes (such as commuting, shopping, and leisure). [PERSON] et al. pioneered supervised learning approaches for transportation mode learning from raw GPS data using change point-based segmentation [6]. [PERSON] et al. enhanced transportation mode identification to 91.1% accuracy by incorporating GIS information with GPS trajectory data [7]. [PERSON] et al. provided a comprehensive review and evaluation of GPS-based transport mode detection methods [8]. [PERSON] et al. proposed a novel trajectory-as-a-sequence framework for travel mode identification using sequence-to-sequence models [9]. [PERSON] achieved superior performance using wavelet transform and deep learning for GPS trajectory-based travel mode identification [10]. [PERSON] et al. developed deep semi-supervised machine learning algorithms, achieving 93.94% accuracy with minimal labeled data [11]. [PERSON] et al. introduced multi-stage fusion networks with varied scale representation for transportation mode identification [12]. These studies provide important information for understanding urban traffic demand while offering rich user behavior data support for intelligent signal control systems, facilitating more precise traffic management decisions.