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

Transportation Research Record: Journal of the Transportation R

Rouphail

2020

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This paper investigates the effect of incidents on freeway segment capacity. Currently, the Highway Capacity Manual (HCM) provides a look-up table linking the remaining segment capacity fraction during an incident to the total and closed number of lanes on the segment. In reality, segment capacity during an incident will tend to vary over time, with the most severe effects felt early on before any type of response is initiated, with congestion progressively improving as the appropriate incident management actions are implemented. By applying a genetic algorithm calibration method on each incident day and calibrating the incident capacity adjustment factors (CAFs), optimal time-dependent CAFs were derived that best represented the effect of incidents on the freeway segment capacity. By analyzing the optimal CAFs, the strongest relationship was revealed to be between the optimal time-dependent CAF and the temporal progression of the incident. A regression model was developed to represent this behavior. This was formulated in a manner that can directly adjust the current HCM’s fixed CAF values (for a specific lane closure configuration) for modeling incidents both in single day, seed file application, or for an entire year reliability analysis. A portion of WB I-540 in Raleigh, NC was selected as the study area in which the proposed method was tested. Between January 2014 and December 2018, the team identified 22 isolated incidents (away from the recurring congestion period) that closed one or two lanes of traffic, creating a distinct congestion pattern.

Section: Literature Reviewmentioning

confidence: 99%

Evaluating the Effect of Incidents on Freeway Facilities and Updating the Incident Capacity Adjustments for HCM

Addison

Transportation Research Record: Journal of the Transportation R

Rouphail

2020

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“…The occurrence of incidents is stochastic in nature and can result in a reduction in the capacity and/or speeds ( 4 , 13 , 24 ). Moreover, the location, severity, and duration of an incident can altogether influence its impact on the facility travel time.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Karmakar

Transportation Research Record: Journal of the Transportation R

Rouphail

et al. 2018

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Traffic congestion costs drivers an average of $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.

“…The number of incidents in any given study period follows a Poisson distribution (4,5). The SHRP 2 Project L08 method models only one incident in any given scenario, and to account for more incidents per scenario, it essentially prolongs the duration of that single incident.…”

Section: Comparison With Generation Of Freeway Scenarios From Shrp 2 Project Lo8mentioning

confidence: 99%

“…Travel time variability on freeway facilities is affected by a variety of probabilistic factors such as inclement weather conditions, traffic demand changes, and incidents (3). These factors are stochastic in that their occurrence, location, and duration need to be considered as random variables (4,5). Reliability analysis aims to quantify these factors and their operational effects in a whole-year analysis.…”

mentioning

confidence: 99%

Generating Scenarios of Freeway Reliability Analysis

Transportation Research Record: Journal of the Transportation R

Hajbabaie

Schroeder³

et al. 2015

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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.