Evaluating freeway traffic conditions by Data Envelopment Analysis using loop data

Effective evaluation of traffic conditions is a key issue involved in alleviating freeway congestion, improving operations and estimating travel time. Loop detectors can provide reliable traffic data sources for traffic conditions measurement and monitoring, however, the multiple influencing factors derived from loop data lead to a combined effect which complicates the measurement. Therefore, a novel traffic conditions evaluation method by utilizing Data Envelopment Analysis (DEA) is proposed. The method can devise an overall traffic conditions evaluation based on the multiple performance measures. To illustrate our method, an experimental study was undertaken with dual-loop-detector data from 6 freeway sections for the year 2006, and 5 measures were selected for inclusion in this multivariate analysis to evaluate the traffic conditions. The conclusions indicate the stakeholders can gain new insight into the overall traffic conditions behind multiple performance measures with our method, and the evaluation results is helpful in identifying transportation investment priorities for specific regions and improving resource utilization among competing sectors.