Measuring Variability in Freeway Traffic States Using Real-time Loop Data in Jilin
Understanding the evolution of traffic states in both time and space is a critical step toward improving freeway modeling and operations. Dual-loop detectors can a foundation for a uniform and comprehensive evaluation of traffic states, however, the multiple influencing factors derived from loop data lead to a combined effect which complicates the measurement. Furthermore, the goals and objectives of evaluation are inherently an expression of the various stakeholders affected by the traffic conditions, so the evaluation process and result must address the interests of all stakeholders. Therefore, this paper introduces a novel hybrid method based on Data Envelopment Analysis (DEA) and Analytical Hierarchy Process (AHP) using loop data. This method can evaluate traffic conditions of each freeway section relative to others by considering various stakeholders’ preferences in multiple performance measures. In particular, this research evaluates the traffic conditions of 6 freeway sections in Seattle by incorporating two types of stakeholders’ preferences, and 5 measures are established on the basis of the 12-month loop data for the year 2010. As a result, the best sections and others’ performance gaps can be identified. The conclusions indicate the stakeholders can gain new insight into the overall traffic conditions behind multiple performance measures with our method, and the analysis of returns to scale can generate references for infrastructure investment and facilitate optimal allocation of resources.