The latest edition of the Highway Capacity Manual (HCM-6) includes, for the first time, a methodology for estimating and predicting the average travel time distribution (TTD) of urban streets. Travel time reliability (TTR) metrics can then be estimated from the TTD. The HCM-6 explicitly considers five key sources of travel time variability. A literature search showed no evidence that the HCM-6 TTR model has ever been calibrated with empirical travel time data. More importantly, previous research showed that the HCM-6 underestimated the empirical TTD variability by 70% on a testbed in Lincoln, Nebraska. In other words, the HCM-6 TTR metrics reflected a more reliable roadway than would be supported by field measurements. This paper proposes a methodology for calibrating the HCM-6 TTR model so that it better estimates the empirical TTD. This calibration approach was used on an arterial roadway in Lincoln, Nebraska, and no statistically significant differences were found between the calibrated HCM-6 TTD and the empirical TTD at the 5% significance level.
The 6th edition of the Highway Capacity Manual (HCM-6) includes the concept of travel time reliability (TTR), which attempts to determine the distribution of average trip travel times over an extended period. TTR is an inherent part of travelers’ route choice decisions and is used by traffic managers to better quantify operations rather than simply using average travel times. The focus of this paper is on the HCM-6 urban street TTR methodology contained in Chapter 17. The approach uses historical data (e.g., weather and volume fluctuations) and simple empirical data (e.g., 1-day volume count) to provide the user with average travel time and reliability predictions for a traffic facility over an extended period (e.g., a year). To the best of the authors’ knowledge, there is no existing literature on validating the HCM-6 methodology with empirical data. The goals of this paper were to validate the HCM-6 urban street reliability methodology by comparing the empirical Bluetooth (BT) travel time distributions with the estimated HCM-6 distribution, and to propose potential HCM-6 augmentation strategies. Archived BT data from a 0.5-mi urban arterial in Lincoln, Nebraska was used for comparison. It was found that there were statistically significant differences, but minimal practical differences, between the mean of the predicted HCM-6 travel time distribution and the mean of the empirical distribution. However, the HCM-6 distribution had a lower variance than the empirical distribution. Not surprisingly, the HCM-6 model underestimated the TTR metrics (buffer index and the planning time index) by approximately 62% and 9%, respectively.
The Highway Capacity Manual 6th edition (HCM6) includes a new methodology to estimate and predict the distribution of average travel times (TTD) for urban streets. The TTD can then be used to estimate travel time reliability (TTR) metrics. Previous research on a 0.5-mi testbed showed statistically significant differences between the HCM6 estimated TTD and the corresponding empirical TTD. The difference in average travel time was 4 s that, while statistically significant, is not important from a practical perspective. More importantly, the TTD variance was underestimated by 70%. In other words, the HCM6 results reflected a more reliable testbed than field measurement. This paper expands the analysis on a longer testbed. It identifies the sources and magnitude of travel time variability that contribute to the HCM6 error. Understanding the potential sources of error, and their quantitative values, are the first steps in improving the HCM6 model to better reflect actual conditions. Empirical Bluetooth travel times were collected on a 1.16-mi testbed in Lincoln, Nebraska. The HCM6 methodology was used to model the testbed, and the estimated TTD by source of travel time variability was compared statistically to the corresponding empirical TTD. It was found that the HCM6 underestimated the TTD variability on the longer testbed by 67%. The demand component, missing variable(s), or both, which were not explicitly considered in the HCM6, were found to be the main source of the error in the HCM6 TTD. A focus on the demand estimators as the first step in improving the HCM6 TTR model was recommended.
This paper evaluated the effect of the COVID-19 preventive orders on arterial roadway travel time reliability (TTR). A comparative analysis was conducted to examine average travel time distributions (TTD), and their associated TTR metrics, before and during the COVID-19 pandemic. Travel time data for four urban arterial corridors in Nebraska, disaggregated by peak period and direction, were analyzed. It was found that in 2020, the average TTD mean and standard deviation values for all 16 scenarios were reduced by an average of 14.0% and 43.4%, respectively. The travel time index, the planning time index, the level of travel time reliability (LOTTR), and the buffer index metrics associated with these TTDs were reduced, on average, by 14.0%, 19.7%, 3.5%, and 35.0%, respectively. In other words, whether the test corridors were more reliable during the pandemic was a function of which TTR metric was used. The paper concludes by arguing for a fundamental change in how arterial TTR is measured and reported to different user groups.
Platoon dispersion (PD) is the foundation of traffic signal coordination in an urban traffic network. PD describes the phenomenon by which vehicles depart from an upstream intersection as a platoon and begin to disperse before they arrive at the downstream intersection. Recently, advance warning flashers (AWFs) have been applied in many high-speed corridors. There is a need to update the traditional PD model to include the effect of AWFs. This paper examines the traffic flow dispersion patterns when an AWF is present and tests the hypothesis that the AWF will affect PD on a coordinated signal corridor. Platoon vehicles, which are not affected by the operation of the AWF, are used for comparison. Results show that when the AWF effect is included in the PD model, the smoothing factor F of the Robertson's PD model ranges from 0.11 to 0.13. This range is smaller than the smoothing factor without the AWF effect. The platoon
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