This study aims to develop a set of performance metrics and computational methodologies that can be incorporated into the operational management and planning process for investment decision making. This report details the work performed in the research project. The objectives of this project are to (a) quantify the impact of nonrecurring congestions, including incidents and weather; and (b) provide linkage between performance measures and decision making by using interpretative indicators to inform decisions. Freeway performance measures often are considered in three dimensions: temporal aspect, spatial details, and source of congestion. This study studies these dimensions from a holistic view and strives to describe the roadway conditions in support of investment decisions. The study examines the freeway network as a whole, and determines its overall condition. Questions posed included where are the unreliable locations along a freeway corridor and how is reliability/unreliability determined? To answer these questions, a measure called congestion frequency was developed. It is intuitive and consistent with the speed reliability measurement currently used by UDOT. Congestion frequency is defined as the percentage of time that speed drops below a certain threshold. By extracting traffic information from historical archived data in PeMS, this indicator can be calculated and sensitivity analysis conducted to choose the proper threshold. A methodological framework is developed to quantify the incident-induced delay and identify secondary incidents based on the empirical data collected. The framework acknowledges that each individual incident has a different impact on the roadway spatially and temporally due to varying traffic conditions, roadway geometries, and crash characteristics. Thus, a data-driven algorithm was developed to determine the impact region for each incident. By heuristically searching the historical database and performing pattern matching to find the historical traffic condition that matches the incident scenario, the incidentinduced delay was calculated and secondary incidents were identified. There were 109 primary incidents and 240 secondary incidents identified on the selected I-15 Northbound corridor in 2013. From the distribution of secondary incidents, it was found that the occurrence of secondary incidents was highly related to weather condition. The incident-induced delay was influenced by severity, location, and timeof-day. A statistical mechanism was developed to determine adverse weather impact on travel. Utilizing the weather/roadway information provided by Traveler Advisory Telephone System (TATS) and PeMS, an algorithm was developed to map traffic data with the weather database. It was concluded that during adverse weather, especially when the road is snow-covered, lower flow is associated with high delay during the peak period, indicating a reduction in speed. Also, the non-peak period had a significant reduction in delay compared with the historical travel pattern, which implies a reduction in demand. (Author/publisher)
Samenvatting