Seyedbehzad Aghdashi

Deterministic Framework and Methodology for Evaluating Travel Time Reliability on Freeway Facilities

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 scenarios 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.

Deterministic Approach to Managed Lane Analysis on Freeways in Context of Highway Capacity Manual

The building of managed lanes parallel to general purpose lanes is an increasingly common approach to optimizing freeway capacity. Managed lanes allow agencies to classify customers and assign a portion of the freeway capacity to them. With no methodology in the Highway Capacity Manual (HCM) for analyzing these facilities, analysts rely on more time-consuming simulation analyses. A methodology is presented for estimating the performance of a parallel system of general purpose and managed lane facilities in an HCM context based on NCHRP Project 3–96. The methodology defines new managed lane segment types to use in an HCM analytical framework and is associated with a new set of speed–flow curves. It is sensitive to the number of lanes and the type of separation between managed lanes and general purpose lanes. The method introduces the concept of parallel lane groups of general purpose and managed lanes and thus can account for speed reduction in managed lanes caused by congestion in adjacent general purpose lanes. The method was implemented in a computational engine, FREEVAL-ML, which was built on the freeway facilities method in HCM 2010 but which was updated to incorporate inputs and outputs of the managed lane components. The geometry of two existing managed lane facilities in Washington State is used to illustrate the method, demonstrating the applicability of the analytical framework to real-world facilities.

Generic Speed–Flow Models for Basic Freeway Segments on General-Purpose and Managed Lanes in Undersaturated Flow Conditions

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.

Estimation of Incident Propensity for Reliability Analysis in the Highway Capacity Manual

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.

Enhanced Decision-Making Framework Using Reliability Concepts for Freeway Facilities

This paper presents a decision-making framework based on a travel time reliability methodology developed under the U.S. Strategic Highway Research Program. Existing methods consider a set of predefined prevailing conditions for the analysis of freeway facilities as the base case. However, a reliability analysis accounts for multiple recurring and nonrecurring congestion sources to estimate the travel time distribution over a long time horizon. This approach considers variations in traffic demand levels, inclement weather conditions, and incidents that occur stochastically on a freeway facility. Several performance measures are defined based on the travel time distribution, which comprehensively cover the full range of operational conditions on the system. Based on the proposed decision-making framework, mobility strategies can be identified, evaluated, and improved.

Generating Scenarios of Freeway Reliability Analysis: Hybrid Approach

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.

Method for Scenario Selection and Probability Adjustment for Reliability and Active Traffic Management Analysis in a Highway Capacity Manual Context

This paper presents an optimization-based probability adjustment approach that enables an analyst to minimize the error and bias in estimating freeway reliability performance measures by using a small sample of reliability scenarios. The freeway facilities travel time reliability methodology proposed for the Highway Capacity Manual 2010 can produce up to 24,000 scenarios to be evaluated for estimating the travel time distribution. The methodology uses a deterministic scenario generation approach to account for any possible operational condition of freeway facilities. In addition to processing time considerations, the large number of scenarios poses analytical challenges because (a) a detailed assessment and scrutiny of individual scenarios cannot be performed and (b) the customized selection of active traffic management is infeasible. The proposed method allows the analyst to adjust the selected scenario probabilities to estimate the real-world freeway performance measure better with a manual biased sample. The biased sampling probability adjustment method is applied to two real-world case study examples. These illustrate that the population travel time distribution can be approximated adequately through the probability adjusted sample. Findings from this research have implications for proposed reliability and active traffic management methodologies in the Highway Capacity Manual 2010. The number of scenarios can be greatly reduced from several thousand to less than 100 while maintaining the shape of the reliability distribution and key performance measures of the scenario population.

Validation and Calibration of Freeway Reliability Methodology in the Highway Capacity Manual: Method and Case Studies

Traffic congestion costs drivers an average of

Estimation of Saturation Headway in Work Zones on Urban Streets

1,200 a year in wasted fuel and time, with most travelers becoming less tolerant of unexpected delays. Substantial efforts have been made to account for the impact of non-recurring sources of congestion on travel time reliability. The 6th edition of the Highway Capacity Manual (HCM) provides a structured guidance on a step-by-step analysis to estimate reliability performance measures on freeway facilities. However, practical implementation of these methods poses its own challenges. Performing these analyses requires assimilation of data scattered in different platforms, and this assimilation is complicated further by the fact that data and data platforms differ from state to state. This paper focuses on practical calibration and validation methods of the core and reliability analyses described in the HCM. The main objective is to provide HCM users with guidance on collecting data for freeway reliability analysis as well as validating the reliability performance measures predictions of the HCM methodology. A real-world case study on three routes on Interstate 40 in the Raleigh-Durham area in North Carolina is used to describe the steps required for conducting this analysis. The travel time index (TTI) distribution, reported by the HCM models, was found to match those from probe-based travel time data closely up to the 80th percentile values. However, because of a mismatch between the actual and HCM estimated incident allocation patterns both spatially and temporally, and the fact that traffic demands in the HCM methods are by default insensitive to the occurrence of major incidents, the HCM approach tended to generate larger travel time values in the upper regions of the travel time distribution.

Application of High-Resolution Vehicle Data for Free-Flow Speed Estimation

Work zones and lane closures on urban arterials can cause significant disruptions to the traveling public, and methods are increasingly needed to estimate the reductions to saturation flow rates that result from work zones at signalized intersections. A set of statistical models that estimate saturation headways as a function of the presence and configuration of the work zone on signalized arterial streets is presented. More than 10,000 individual vehicular headway observations were collected from video observations in and after work zones at six study sites in North Carolina. Conventional multiple-regression and path-based-regression models (structural equation model) were used to develop the saturation headway models. Three models are provided at different aggregation levels of the collected data with identical work zone configurations. The models developed at cycle-length, 15-min, and full aggregation produced adjusted R-squared values of .3259, .7209, and .895, respectively. The proposed model incorporates the effects of lane configuration, pavement condition, turning percentage from shared lanes, work intensity, and number of closed exclusive turning lanes. Based on path analysis, the structural equation model satisfies all the rule-of-thumb criteria for goodness-of-fit indices. The model uses Highway Capacity Manual default values for turning-vehicle headway effect as its intercept coefficient value.