Detecting and Ranking Recurrent Bottlenecks on Urban Expressways: A Speed-Only Severity Index from Floating Car Data

Accurate identification of traffic bottlenecks is a fundamental task in the operational analysis of urban expressways. Traditional bottleneck detection methods rely heavily on fixed-location sensors, which suffer from limited spatial coverage and high infrastructure costs. This study proposes a comprehensive Floating Car Data (FCD)-based framework for detecting, characterizing, and ranking recurrent bottlenecks on urban expressways using segment-level speed data alone. The methodology integrates normalized speed fields, a spatially aware moving window algorithm with adaptive downstream verification, and temporal persistence criteria to distinguish active bottlenecks from transient congestion events. To prioritize detected bottlenecks by their operational significance, a Speed-only Bottleneck Severity Index (S-BSI) is introduced. The S-BSI is a 0–100, five-component severity index that ranks active bottlenecks using segment-level speed data only, without requiring flow or density measurements; its components capture upstream spatial extent, temporal persistence, speed deficit magnitude, queue length, and queue growth rate. This research presents a structured methodology for identifying, characterizing, and ranking bottlenecks on urban expressways using Floating Car Data. Detection parameters are selected through a systematic grid search over 128 combinations. The framework is validated using one year of weekday commercial FCD (248 days) obtained from an urban expressway corridor. Empirical results demonstrate that the proposed algorithm reliably identifies spatially concentrated and temporally recurrent bottlenecks; in total, 32 distinct critical bottleneck locations (S-BSI ≥ 35) are detected, of which 31 (96.9%) coincide with an identifiable physical or operational congestion-generating feature of the corridor. By providing a scalable, cost-effective, and transferable detection-and-severity methodology, this research contributes to the growing body of literature on probe vehicle data applications for corridors where fixed-sensor infrastructure is limited or absent.