The National Academies Press

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From page 39... ... 39 5.1 Introduction The proliferation of mobile connected technologies has generated a plethora of data on how people and freight move throughout cities. Increasing cooperation between public agencies and technology developers has led some of these data to become available to transportation planners. Read the entire page →
From page 40... ... 40 Estimating the Value of Truck Travel Time Reliability 5.1.1 Organization An overview of the Reliability Valuation Framework is shown in Figure 5-1. Each box corresponds to a section in this chapter. Read the entire page →
From page 41... ... Reliability Valuation Framework 41 and off-peak times of the day. This variation is typically predicted reasonably well through traffic applications, historical data, and experience. Read the entire page →
From page 42... ... 42 Estimating the Value of Truck Travel Time Reliability 5.2 Measurement 5.2.1 Travel Time Data 5.2.1.1 National Performance Management Research Data Set The NPMRDS reports average travel times throughout the National Highway System at 5-minute intervals for the past several years. Records are only available for times of the day when GPS-instrumented vehicles took measurements. Read the entire page →
From page 43... ... Reliability Valuation Framework 43 Despite not being used for the NPMRDS anymore, ATRI's GPS data continue to be used in a wide range of truck planning and congestion studies nationwide. These data are said to come from approximately 800,000 instrumented trucks traveling around the country. Read the entire page →
From page 44... ... 44 Estimating the Value of Truck Travel Time Reliability • Potentially exclude major known events. Truck drivers are typically aware of major sporting events, festivals, or demonstrations that affect traffic. Read the entire page →
From page 45... ... Reliability Valuation Framework 45 5.2.3 Route Travel Times from Link Data While travel time data are typically only available at the link level, the modeling of transportation decisions -- particularly how users are affected by reliability -- needs to occur at the trip level. However, estimating the distribution of travel times on a route using link data has historically been challenging, for several reasons. Read the entire page →
From page 46... ... 46 Estimating the Value of Truck Travel Time Reliability The level of spatial correlation has been found to be influenced by many factors, including the degree of communality of traffic flows, the configuration of the roadway network, and the level of congestion (Gupta et al. Read the entire page →
From page 47... ... Reliability Valuation Framework 47 5.2.3.1 Comonotonicity Assumption This approach, first proposed by Dhaene et al. Read the entire page →
From page 48... ... 48 Estimating the Value of Truck Travel Time Reliability Application. This approach works well with the density of data typically available in NPMRDS. Read the entire page →
From page 49... ... Reliability Valuation Framework 49 5.2.3.5 Recommendations Ultimately, the best approach for a reliability analysis depends on the amount of travel time data available, the desired accuracy of the results, and the willingness to estimate complex models. Figure 5-4 describes how the approaches described above fall within these criteria. Read the entire page →
From page 50... ... 50 Estimating the Value of Truck Travel Time Reliability care most about reliability. This section describes methodologies for modeling roadway reliability in ways that are compatible with the rest of the Reliability Valuation Framework. Read the entire page →
From page 51... ... Reliability Valuation Framework 51 5.3.1.1 Limitations of Statistical Relationships Relying on statistical relationships for predicting reliability has several shortcomings. Foremost, these relationships are specific to local conditions, such as demand patterns, roadway configuration, occurrence of special events, and weather. Read the entire page →
From page 52... ... 52 Estimating the Value of Truck Travel Time Reliability SHRP 2 Project C11 Analytical Equations. Simple analyses could rely on the analytical equations described in SHRP 2 Project C11. Read the entire page →
From page 53... ... Reliability Valuation Framework 53 While the statistical relationships were estimated for Maryland, they are likely to hold for a wide range of traffic conditions. It is possible for analysts with high-quality travel time data to reestimate these relationships for local roads (only the TTI95 equations would be needed) Read the entire page →
From page 54... ... 54 Estimating the Value of Truck Travel Time Reliability in 95th percentile delays be used to scale up or down the observed 95th percentile delays without the project. This process is summarized by where j1 = predicted 95th percentile delay after the project, j0 = observed 95th percentile delay without the project, and Δj = change. Read the entire page →
From page 55... ... Reliability Valuation Framework 55 study cared most about reliability. For planning analyses, it is recommended that analysts use the marginal cost of trucking estimated by ATRI. Read the entire page →
From page 56... ... 56 Estimating the Value of Truck Travel Time Reliability benefits of these changes, for truck trips traveling between origin o and destination d, can be expressed as where Qod is the freight tonnage and VOT and VOR are specified per ton moved. This formulation assumes that freight demand and routing do not change in response to the project, which is reasonable for medium to small projects. Read the entire page →
From page 57... ... Reliability Valuation Framework 57 counts, which are often collected by using tube detectors or other methods. These counts should be adjusted seasonably following the guidance provided by AASHTO (2009) Read the entire page →
From page 58... ... 58 Estimating the Value of Truck Travel Time Reliability and the ton-hours of unreliability (THU) can be calculated as By using these measures, the average ton-velocity (TV) Read the entire page →
From page 59... ... Reliability Valuation Framework 59 congestion (recurring and nonrecurring) as well as infrastructure restrictions that affect truck operations (causing inconvenient routing and other inefficiencies) Read the entire page →

From page 39...

... 39 5.1 Introduction The proliferation of mobile connected technologies has generated a plethora of data on how people and freight move throughout cities. Increasing cooperation between public agencies and technology developers has led some of these data to become available to transportation planners.