David L Schrank

Conceptual Framework and Trucking Application for Estimating Impact of Congestion on Freight

Few analytical techniques fully incorporate freight aspects into transportation system monitoring, system evaluation, and project selection. Transportation investment decisions are frequently based on typical performance measures of travel time and delay for passenger travel, and little, if any, attempt is made to incorporate goods movement into such analysis. Research was performed by the Texas Transportation Institute for a better understanding of freight mobility and reliability issues. The research developed a conceptual framework to help transportation professionals communicate, visualize, and understand factors affecting freight mobility and reliability; a methodology with which to estimate congestion for the conceptual framework; and two applications of the methodology to truck freight (one in Austin, Texas, and one in Denver, Colorado). The conceptual framework visually incorporates the effects of geographic area, commodity type, and time period on freight mobility and reliability. This framework provides a method to communicate freight congestion, mobility, and reliability and can be expanded to include all freight modes (truck, rail, maritime, air, and pipeline). The conceptual framework and methodology can help transportation professionals to better communicate, understand, and make planning-level decisions based on the factors that affect freight mobility and reliability.

Developing a Total Peak Period Travel Time Performance Measure

Transportation performance measures based on travel time quantities satisfy a range of mobility purposes. The measures can show the effect of many transportation and land use solutions, and they are relatively easy to communicate to a range of audiences. The concept of total travel time has been discussed since the early 1950s, but because of data inaccessibility, the planning community has rarely used total travel time as a measure. For the initial implementation of the total peak period travel time measure in the Urban Mobility Report, data from the reports primary data sets were combined in a new way to estimate road users’ total travel time during the peak period. Data shortcomings were addressed with simplifying assumptions to create a calculation method that would offer a more refined value than would the use of raw or incomplete data. Total peak period travel time can provide additional explanatory power to a set of mobility performance measures and bridge the gap between traditional delay-based measurement and accessibility.

Speed-Based Autosegmentation Method for Reporting Moving Ahead for Progress in the 21st Century Act Performance Measures

An early step in the process of performing any mobility analysis is the segmentation of the roadway network. Traditional manual segmentation includes reviewing maps and geometric roadway characteristics to segment roadways into logical, similarly behaving segments. This task is time consuming and does not inherently use the actual speed data in the segmentation process. There is a need for an automated procedure to provide a first cut of roadway segments for analysis. The roadway segmentation procedure presented used a comparison of average annual 15-min speeds by day of week to judge whether adjacent roadway links exhibited similar traffic patterns and should be grouped together for mobility analysis. The procedure used relatively simple calculations to provide a single-number criterion indicating the relative degree of similarity between pairs of adjacent or near-adjacent roadway links. Researchers developed an automated data processing framework for autosegmenting freeways by using INRIX speed data and used this processing framework to evaluate the autosegmentation method in comparison with known congested locations in Houston, Texas. Comparison with known congested segments from the Texas Department of Transportations 100 most congested roadways list showed reasonably good agreement with congested locations in Houston. The methods explained in this paper are particularly useful for transportation agencies interested in segmenting their roadway networks to produce performance measure requirements expected from the Moving Ahead for Progress in the 21st Century Act.

Preparing for System Performance Measures and Target-Setting Requirements of the Moving Ahead for Progress in the 21st Century Act: Lessons Learned in Virginia

The Moving Ahead for Progress in the 21st Century Act (MAP-21) established performance-based planning and programming requirements to improve transportation decision making and increase the accountability and transparency of federal highway programs. At the time of the writing of this paper, the MAP-21 performance requirements for system performance, congestion mitigation and air quality, and freight had not yet been released, but many transportation agencies had begun efforts to understand and implement performance management activities in anticipation of the MAP-21 system performance requirements. The Virginia Department of Transportation (DOT) has long been a leader in transportation performance activities. Continuing this trend, the Virginia DOT had great interest in evaluating system performance (beginning with the Interstate system) to demonstrate how system-wide measures could be computed and how targets could be set. The Virginia DOT contracted with the Texas A&M Transportation Institute to develop these measures for the Virginia Interstate system as a pilot test. This paper describes the successfully developed and demonstrated methodology for computing a number of mobility, reliability, and congestion magnitude performance measures along with targets for selected measures. Researchers divided the Virginia Interstate roadways into 199 reporting segments. Performance statistics were computed for each segment and aggregated up to the urban area, Virginia DOT district, and statewide level. Major lessons learned from this effort are documented in this paper and should serve to provide additional guidance to other agencies beginning this type of performance measurement effort.

Greenhouse Gas Emissions and Urban Congestion: Incorporation of Carbon Dioxide Emissions and Associated Fuel Consumption into Texas A&M Transportation Institute Urban Mobility Report

The Texas A&M Transportation Institutes Urban Mobility Report (UMR) is acknowledged to be the most authoritative source of information about traffic congestion and its possible solutions. As policy makers from the local to national levels devise strategies to reduce greenhouse gas (GHG) emissions, the level of interest in the environmental impact of urban congestion has increased. To this end, the researchers developed and applied a methodology to determine carbon dioxide (CO2) emissions caused by congestion for inclusion in the UMR. The methodology also estimated fuel consumption on the basis of the CO2 emissions estimates. The researchers developed a five-step methodology with data from three primary data sources: (a) FHWAs Highway Performance Monitoring System, (b) INRIX traffic speed data, and (c) the U.S. Environmental Protection Agencys Motor Vehicle Emissions Simulator model. Results were intuitive and reasonable when emission rates (pounds of CO2 per mile) were compared with the emissions inventories in selected cities. The researchers incorporated the new methodology for all urban areas into the 2012 UMR and plan to include the same measures in future releases of the report. The researchers reported that, in 2011, 56 billion pounds of additional CO2 were produced in all 498 urban areas during congestion only; this amount equated to 2.9 billion gallons of wasted fuel. The amount of CO2 produced under free-flow conditions (i.e., absent congestion) was 1.8 trillion pounds in 2011 in all 498 urban areas.

Incorporating Urban-Area Truck Freight Value into the Urban Mobility Report

For more than 30 years, the Texas A&M Transportation Institute (TTI) has developed methodologies and appropriate performance measures for estimating congestion performance and communicating them to technical and nontechnical audiences. TTIs Urban Mobility Report (UMR) has historically focused on passenger car congestion (i.e., congestion caused by the average commuter). However, roadway traffic congestion certainly affects both commuters and goods movement. With the documented growth of freight shipments and value, particularly in trucking, researchers developed and applied a methodology to include in UMR the truck freight commodity value that is affected by congestion in urban areas. The methodology uses data from FHWAs Freight Analysis Framework (FAF) and Highway Performance Monitoring System (HPMS). Commodity values supplied by the FAF are integrated into truck vehicle miles of travel calculated from the HPMS roadway inventory. Researchers estimated that

Estimating the Effect of Operational Improvements in the Houston Area

7 trillion worth of commodities were trucked on Americas urban streets and highways during 2010. At the urban-area level, the results of the truck value measure appear to be intuitive, as bigger cities consume more goods and the greater consumption means a higher value of freight movement. The addition of a truck value to the UMR provides another dimension to inform policy makers and decision makers about the congestion problem. This addition also serves to inform trucking stakeholders with an estimate of the amount of truck value that is affected by congestion. Researchers will continue to include the truck freight value performance measure in subsequent releases of the UMR.

Developing and Implementing a Port Fluidity Performance Measurement Methodology using Automatic Identification System Data

Measuring the effects of transportation system improvements has long been a challenge. Two estimates—the congestion index method and the travel delay method—are used to produce a range of possible benefits resulting from the implementation of transportation projects and programs. The congestion index method uses the roadway congestion index (RCI), and its relationship with congestion cost per capita, to estimate the benefit derived from operational improvements. The RCI provides an areawide assessment of congestion based on freeway and principal arterial street system daily vehicle kilometers of travel per lane kilometer data. The travel delay method calculates congestion cost by associating the value of time, cost of fuel, and cost of operating commercial vehicles with travel delay estimates. These two methods are used to estimate the effects of three types of transportation system improvement projects/programs—freeway traffic management system, high-occupancy vehicle lanes, and regional computerized tra...

SELECTING TRAVEL RELIABILITY MEASURES

The U.S. Army Corps of Engineers (USACE) is responsible for the maintenance of federally authorized navigation channels and associated infrastructure. As such, USACE requires objective performance measures for determining the level of service being provided by the hundreds of maintained navigation projects nationwide. To this end, the U.S. Army Engineer Research and Development Center partnered with Texas A&M Transportation Institute to develop a freight fluidity assessment framework for coastal ports. The goal was to use archival automatic identification system (AIS) data to develop and demonstrate how ports can be objectively compared in relation to fluidity, or the turnaround time reliability of oceangoing vessels. The framework allows USACE to evaluate maintained navigation project conditions alongside port system performance indices, thereby providing insight into questions of required maintained channel dimensions. The freight fluidity concept focuses on supply chain performance measures such as travel time reliability and end-to-end shipping costs. Although there are numerous research efforts underway to implement freight fluidity, this is the first known application to U.S. ports. This paper covers AIS data inputs, quality control, and performance measures development, and also provides a demonstration application of the methodology at the Port of Mobile, Alabama, highlighting travel time and travel time reliability operating statistics for the overall port area. This work provides foundational knowledge to practitioners and port stakeholders looking to improve supply chain performance and is also valuable for researchers interested in the development and application of multimodal freight fluidity performance measures.