Proposed Chapters for Incorporating Travel Time Reliability into the Highway Capacity Manual

2013 ◽  
Author(s):  
Paul Ryus ◽  
James Bonneson ◽  
Richard Dowling ◽  
John Zegeer ◽  
Mark Vandehey ◽  
...  
Keyword(s):  
Travel Time ◽  
Travel Time Reliability ◽  
Highway Capacity ◽  
Highway Capacity Manual

scholarly journals Analysis of Component Errors in the Highway Capacity Manual Travel Time Reliability Estimations for Urban Streets

2020 ◽  
Vol 2674 (6) ◽  
pp. 85-97 ◽  
Author(s):  
Ernest O. A. Tufuor ◽  
Laurence R. Rilett
Keyword(s):  
Travel Time ◽  
Time Variability ◽  
Travel Time Reliability ◽  
Travel Times ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Urban Streets ◽  
Estimate Travel Time ◽  
The Difference ◽  
Travel Time Variability

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.

Freeway Network Travel Time Reliability Analysis Methodology and Software Tool Development

2021 ◽  
pp. 036119812110184
Author(s):  
Wei Sun ◽  
Scott S. Washburn
Keyword(s):  
Travel Time ◽  
Performance Measures ◽  
Traffic Assignment ◽  
Software Tool ◽  
User Equilibrium ◽  
Travel Time Reliability ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Analysis Methodology ◽  
Weather Events

Applications in the field often require analysis of freeway travel time reliability (TTR) at the network level. While micro-simulation is suitable for performing network-level analysis, the computational burden can become unreasonable when TTR analysis is factored in. As for macro-simulation, most network analysis studies often use simplified link performance functions to represent the travel time and flow relationship. Such functions are not generally sensitive to the range of geometric and traffic conditions that can influence freeway facility operations. This research extends the Highway Capacity Manual (HCM) freeway TTR analysis methodology to the network level. The proposed freeway network TTR analysis methodology generates scenarios that represent the impacts of origin–destination (OD) demand variations, weather events, incident events, and work zone events on freeway network travel time. For each scenario, the methodology performs user equilibrium (UE) traffic assignment and the HCM freeway facility core methodology is applied to represent the travel time and flow relationship. The method of successive average approach is applied to solve the UE traffic assignment. Finally, scenario travel times (and/or other performance measures) are aggregated into various distributions of interest, such as the network-, facility-, and OD-level distributions, and TTR performance measures are calculated at the three different levels. A software tool is developed using C# language on the .NET Framework. The software tool provides a convenient and efficient approach for transportation planners and researchers to conduct the freeway network TTR analysis, which helps to bridge the gap between research and practice.

scholarly journals Validation of the Highway Capacity Manual Urban Street Travel Time Reliability Methodology using Empirical Data

2019 ◽  
Vol 2673 (4) ◽  
pp. 415-426 ◽  
Author(s):  
Ernest O. A. Tufuor ◽  
Laurence R. Rilett
Keyword(s):  
Travel Time ◽  
Empirical Data ◽  
Empirical Distribution ◽  
Extended Period ◽  
Travel Time Reliability ◽  
Travel Times ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Urban Street ◽  
The Mean

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.

Validating and Calibrating the Highway Capacity Manual Arterial Travel Time Reliability Methodology

2021 ◽  
pp. 036119812110266
Author(s):  
Ernest Tufuor ◽  
Laurence Rilett ◽  
Sean Murphy
Keyword(s):  
Performance Management ◽  
Travel Time ◽  
Empirical Data ◽  
Research Question ◽  
Ground Truth ◽  
Travel Time Reliability ◽  
Highway Capacity ◽  
Data Set ◽  
Highway Capacity Manual ◽  
National Performance

The 6th edition of the Highway Capacity Manual (HCM6) introduced a methodology for estimating and forecasting arterial travel time (TT) distributions (TTD) and their associated travel time reliability (TTR) metrics. Recently, it was shown that the HCM6 severely underestimated both the TTD and the TTR metrics for a test network in Lincoln, NE, U.S. Subsequently, it was shown that the underestimation issue could be eliminated through a proposed calibration methodology. Because this validation and calibration work was done on a single, relatively short section of arterial roadway there is an open research question on whether this finding applies to longer and more congested arterial roadways. The goal of this paper is to validate and calibrate the HCM6 TTR methodology on five arterial roadway testbeds that are longer and more congested than the original testbed. Empirical data from the National Performance Management Research Data Set (NPMRDS) which is managed by INRIX was used to represent the ground truth. Similar to the original study, it was found that the HCM6 TTR methodology severely underestimated the TTDs, and their respective TTR metrics, on all five testbeds. This is problematic, because the HCM6 methodology indicates that the corridors had more reliable TT than the empirical data would suggest. It was also shown that the calibration methodology eliminated this underestimation. It is recommended that users of the HCM6 TTR methodology validate and, if necessary, calibrate the model using local empirical travel data.

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

2018 ◽  
Vol 2672 (15) ◽  
pp. 93-104 ◽  
Author(s):  
Nabaruna Karmakar ◽  
Seyedbehzad Aghdashi ◽  
Nagui M. Rouphail ◽  
Billy M. Williams
Keyword(s):  
Travel Time ◽  
Performance Measures ◽  
Traffic Congestion ◽  
Practical Implementation ◽  
Travel Time Reliability ◽  
Time Data ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Reliability Performance ◽  
The Impact

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.

scholarly journals Incorporating Travel Time Reliability into the Highway Capacity Manual

2014 ◽  
Author(s):  
Wayne Kittelson ◽  
Mark Vandehey ◽  
◽  
◽  
◽  
...  
Keyword(s):  
Travel Time ◽  
Travel Time Reliability ◽  
Highway Capacity ◽  
Highway Capacity Manual

Travel-Time Reliability as a Measure of Service

2003 ◽  
Vol 1855 (1) ◽  
pp. 74-79 ◽  
Author(s):  
Chao Chen ◽  
Alexander Skabardonis ◽  
Pravin Varaiya
Keyword(s):  
Los Angeles ◽  
Travel Time ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Time Variability ◽  
Travel Time Reliability ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Average Travel Time ◽  
Travel Time Variability

Statistics from a corridor along Interstate 5 in Los Angeles show that average travel time and travel-time variability are meaningful measures of freeway performance. Variability of travel time is an important measure of service quality for travelers. Travel time can be used to quantify the effect of incidents, and incident information can help reduce travel-time uncertainty. Predictability of travel time is a measure of the benefits of intelligent transportation systems. These measures differ from those defined in the Highway Capacity Manual and other aggregate measures of delay.

scholarly journals New Travel Time Reliability Methodology for Urban Arterial Corridors

2021 ◽  
pp. 036119812110061
Author(s):  
Ernest O. A. Tufuor ◽  
Laurence R. Rilett
Keyword(s):  
Travel Time ◽  
Traffic Congestion ◽  
New Technologies ◽  
Travel Time Reliability ◽  
Highway Capacity ◽  
Significance Level ◽  
Highway Capacity Manual ◽  
Travel Time Distribution ◽  
Reliable Performance ◽  
Urban Arterial

The need for reliable performance measures of urban arterial corridors is increasing because of the rise in traffic congestion and the high value of users’ travel time. Consequently, travel time reliability (TTR), which attempts to capture the day-to-day variability in travel times, has recently received considerable research interest. The basis of all TTR metrics is the underlying travel time distribution (TTD) along the given link or corridor. Estimating and forecasting arterial corridor TTDs for TTR analysis is the focus of this paper. This paper proposes a TTR methodology that addresses some of the limitations of the current U.S. state-of-the-art methodology which was published in the 6th edition of the Highway Capacity Manual (HCM6). Specifically, HCM6 can only estimate average TTD and not the population TTD. However, the population TTD is needed for accurate trip decision-making by individual drivers and logistics companies. In addition, HCM6 cannot be used to analyze the effect of new technologies, such as connected and automated vehicles, nor can it be used easily for long corridors or networks. The proposed TTR methodology, which is traffic-microsimulation based, was applied on a 1.16 mi arterial testbed in Lincoln, Nebraska, U.S. It was shown that the proposed TTR methodology, when calibrated, could replicate the empirical population TTD at a 5% significance level. The population TTD could also be transformed into an average TTD that also replicated the corresponding empirical average TTD at a 5% significance level.

Assessment of Arterial Signal Timings Based on Various Operational Policies and Optimization Tools

2021 ◽  
pp. 036119812110111
Author(s):  
Suhaib Al Shayeb ◽  
Nemanja Dobrota ◽  
Aleksandar Stevanovic ◽  
Nikola Mitrovic
Keyword(s):  
Travel Time ◽  
Heavy Traffic ◽  
Future Research ◽  
Signal Timing ◽  
Highway Capacity ◽  
Average Delay ◽  
Highway Capacity Manual ◽  
Simulation And Optimization ◽  
Fort Lauderdale ◽  
Design Engineers

Traffic simulation and optimization tools are classified, according to their practical applicability, into two main categories: theoretical and practical. The performance of the optimized signal timing derived by any tool is influenced by how calculations are executed in the particular tool. Highway Capacity Software (HCS) and Vistro implement the procedures defined in the Highway Capacity Manual, thus they are essentially utilized by traffic operations and design engineers. Considering its capability of timing diagram drafting and travel time collection studies, Tru-Traffic is more commonly used by practitioners. All these programs have different built-in objective function(s) to develop optimized signal plans for intersections. In this study, the performance of the optimal signal timing plans developed by HCS, Tru-Traffic, and Vistro are evaluated and compared by using the microsimulation software Vissim. A real-world urban arterial with 20 intersections and heavy traffic in Fort Lauderdale, Florida served as the testbed. To eliminate any bias in the comparisons, all experiments were performed under identical geometric and traffic conditions, coded in each tool. The evaluation of the optimized plans was conducted based on average delay, number of stops, performance index, travel time, and percentage of arrivals on green. Results indicated that although timings developed in HCS reduced delay, they drastically increased number of stops. Tru-Traffic signal timings, when only offsets are optimized, performed better than timings developed by all of the other tools. Finally, Vistro increased arrivals on green, but it also increased delay. Optimized signal plans were transferred manually from optimization tools to Vissim. Therefore, future research should find methods for automatically transferring optimized plans to Vissim.

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