Development of an Oversaturated Speed–Flow Model Based on the Highway Capacity Manual

Abstract

Oversaturated speed, flow, and density relationships are of key importance to studies of freeway operations. The Highway Capacity Manual (HCM) oversaturated model, which is defined by a linear transition from the flow and the density at capacity to a zero flow at jam density in the flow–density space, provides a reasonable representation of this relationship but does not provide an unbiased representation for all freeway facilities with different road conditions or driver behavior. This study proposes a method for fitting the HCM model to oversaturated flow and density. Fifteen-minute aggregated flow rate and speed data were collected in 2010 from Traffic.com fixed-location sensors at three sites on North Carolina urban freeways. Density was calculated as the flow rate divided by the speed. The fitted models for these sites were compared with the default HCM model. A set of thresholds was defined to identify eligible sensor observations that represented the steady-state congested traffic conditions. The results revealed that data observations during inclement weather, lane closures, or incidents biased the model-fitting results and therefore needed to be filtered out. The steady-state congestion data identified in the manner proposed in this study fit well with the HCM-based linear flow–density oversaturated model. This method avoids possible bias caused by capacity and jam density differences between the default HCM model and the site-specific models; therefore, the fitted models represented the actual traffic characteristics relationships better than the default HCM models did. Fitting a site-specific HCM-based model is recommended for sites with sufficient speed and flow data.

Application of a Discontinuous Form of Macroscopic Gazis–Herman–Rothery Model to Steady-State Freeway Traffic Stream Observations

“…As the model development is data driven, inconsistent and mixed-state data were removed in two stages of the process. This section presents the first stage, which involves applying three thresholds that are introduced in an earlier study ( 13 ). In this study, the thresholds are modified and applied in a slightly different manner to conform to the study objectives.…”

“…Xu et al ( 13 ) developed a method for fitting the HCM oversaturated model to fixed sensor data by filtering steady-state observations only. Mixed-state and inconsistent data points were removed by imposing three thresholds.…”

“…One of the practical challenges that lies behind empirically fitting a steady-state macroscopic model is identifying non-stationary and mixed-state observations in addition to identifying outliers ( 13 ). If not removed before or during model fitting, such observations will introduce bias in the model parameters and resulting estimates.…”

“…If not removed before or during model fitting, such observations will introduce bias in the model parameters and resulting estimates. Although many outlier-detection algorithms are available in the literature ( 13 , 14 ), the selection of algorithm should be specific to the modeling objective and data characteristics. In this study, a two-regime, discontinuous, steady-state macroscopic model is developed for basic freeway segments ( 12 ) with explicit identification and removal of non-stationary and outlying observations.…”