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Abstract

This report describes methods that potentially can be incorporated into the performance monitoring and planning processes for freeway performance evaluation and decision making. Reliability analysis was conducted on the selected I-15 corridor by employing congestion frequency as the performance measure. Hot spots during peak hours were identified through sensitivity analysis. A data-driven algorithm combining spatiotemporal analysis and shockwave theory was developed to determine secondary incidents. Incident-induced delay was further quantified through spatiotemporal pattern recognition. The average delay induced by incidents aligns well with the incidents' severity and impact. Several hot spots suffered from higher delays and were explored in further detail. A statistical mechanism was developed to determine adverse weather impact on travel. Using the weather records in 2013 and mapping with the PeMS traffic database, volume and delay were estimated under normal conditions and compared with adverse weather conditions. The analysis of different roadway conditions reveals that the general parabolic pattern of speed and volume disappear under severe adverse weather condition. The mechanism was able to identify the causes for reduced volume under a variety of scenarios through empirical data, either due to roadway capacity reduction or travel demand reduction.

How to Cite

Liu, Xiaoyue Cathy, and Zhuo Chen. Data-Driven Freeway Performance Evaluation Framework for Project Prioritization and Decision Making, MPC-17-316. North Dakota State University - Upper Great Plains Transportation Institute, Fargo: Mountain-Plains Consortium, 2017.