Call detail records (CDR)-Based road travel speed estimation: a location area code (LAC) sequence matching method

Although Call Detail Records (CDRs) provide a promising low-cost data source for large-scale traffic-state monitoring, their coarse spatial resolution and irregular temporal sampling make road-level speed estimation challenging. To address this problem, this study proposes a two-stage Location Area Code (LAC) sequence matching framework, which first identifies candidate road segments using LAC sequences and then refines route matching with cell-level information. The method is validated using mobile network data and GPS-based Floating Car Data (FCD) on the South Third Ring Road and the Jingcheng Expressway in Beijing. The results show that the optimal LAC sequence length is environment-dependent: longer sequences improve road-matching specificity in dense urban networks, whereas shorter sequences are sufficient on suburban expressways with lower base-station density. The analysis also reveals a precision-coverage trade-off, as stricter sequence matching improves accuracy but reduces the effective sample size. In addition, CDR-based estimates tend to be lower than FCD speeds on urban roads, which may be related to the difference between person-based CDR sampling and vehicle-based FCD sampling. These findings demonstrate the feasibility of LAC-sequence-based speed estimation and provide practical guidance for adaptive parameter selection in heterogeneous road environments.