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This paper presents a methodology for incorporating freeway reliability analysis in the Highway Capacity Manual on the basis of Strategic Highway Research Program Project L08. The methodology uses a scenario-based approach in which each scenario represents a unique combination of traffic demands and facility (segment) capacities. Demand impacts consider variability in time-of-day, day-of-week, and month-of-year differences, and capacity impacts quantify effects of various nonrecurring congestion sources, such as weather, incidents, work zones, and special events. The method combines three key components: a data repository, a freeway scenario generator (FSG), and a computational software engine, FREEVAL-RL. The FSG determines the number of scenarios for the reliability analysis on the basis of the facility-specific combination of demand, incident, and weather events. For each scenario, it produces a set of inputs and adjustment factors that are passed on to the FREEVAL-RL engine, along with each scenario's probability. The engine is capable of batch-processing many scenarios and estimates the reliability distribution for the facility. At the core, the computational engine and underlying methodology are consistent with the freeway facilities method in the Highway Capacity Manual 2010, which is capable of estimating undersaturated and congested flow conditions in a multi-period analysis. The method is illustrated with a real-world case study freeway facility in North Carolina. The FSG for that facility resulted in 2,508 scenarios. The resulting travel time distribution is presented, and sensitivity analyses are presented to explore the contributing effects of weather and incidents on the overall reliability of the facility.

This paper presents the method used to generate the incident probabilities required by the freeway scenario generator for travel time reliability analysis in the Highway Capacity Manual. The freeway scenario generator requires the estimation of monthly probabilities of different levels of incident severity during specified study periods. Incident probability in this context is the fraction of time that an incident of a specific level of severity is active somewhere on the freeway facility during the study period for the month considered. The proposed method is designed to recognize and deal with the varying levels of incident and facility data availability at the implementing agencies. A queuing model is proposed for the conversion of incident frequencies into incident probabilities when agencies have access only to frequencies instead of probabilities.

The freeway reliability methodology proposed for the Highway Capacity Manual, which is based on SHRP2 L08 methodology, produces an approach to scenario generation that can result in several thousand scenarios to be evaluated to estimate travel time reliability. This large number of scenarios can result in cumbersome user input, a demanding computational burden, and more important, extensive challenges posed when trying to error-check and interpret individual scenarios or to calibrate the model on the basis of real-world observations. This paper presents a novel scenario-generating methodology that accounts for multiple operating conditions. The objective of the proposed approach is to increase the quality of each scenario to make it more representative of the expected congestion patterns on the freeway. This paper shows that the new approach estimates reliability performance measures more accurately than current methods, while reducing the number of scenarios significantly. Thus, the new approach results in a more direct interpretation of results, while simultaneously relaxing many assumptions in the present approach to scenario generation and decreasing biases and errors. The proposed approach uses three core mathematical schemes: (a) a deterministic mathematical model for demand generation and scheduled work zones, (b) a Monte Carlo simulation for incident and weather events, and (c) an optimization algorithm to maximize similarities between the generated set of scenarios and the population of all scenarios. A comparison of results between the proposed method and the SHRP 2 Project L08 approach confirms that the proposed approach yields a higher level of accuracy in matching observed freeway reliability performance measures.

This paper presents a generic set of undersaturated speed–flow models for basic freeway segments on general purpose and managed lanes (MLs) consistent with the Highway Capacity Manual 2010 (HCM 2010). The proposed models predict segment space mean speed under a wide set of freeway operational conditions that can affect its free-flow speed (FFS) and capacity. Furthermore, the proposed models allow quantifying the impacts of nonrecurring events, such as severe weather conditions, incidents, and work zones on the speed–flow relationship. In addition, the model allows calibration of real-world facilities through adjustments to FFS and capacity. The incorporation of analyses of reliability and active traffic and demand management in the HCM context requires a set of speed–flow models capable of accounting for the effect of nonrecurring sources of congestion. Currently, the HCM 2010 provides a set of speed–flow models to predict space mean speed and consequently other freeway performance measures. This family of equations provides a limited adjustment to FFS. With guidance from NCHRP Project 3-96, separate speed–flow models are proposed for MLs through use of a different form from that in the HCM. The proposed generic equations describing the speed–flow relationship provide consistency between speed–flow relationships of managed and general purpose lanes and can incorporate any capacity or FFS adjustments to predict segment speed under different circumstances. The proposed generic equations are wholly consistent with the speed–flow models in the HCM 2010 and predict the same speed under any flow rate.