As used here, system performance refers to how efficiently and reliably people and freight carriers can travel to destinations on the transportation network. This chapter focuses on measures that can be used to determine whether certain aspects of system performance are improving or declining over time.¹  The performance measures discussed are accessibility, congestion, reliability, resiliency, and security. Other aspects of system performance, such as safety, energy usage, and environmental impacts, are discussed separately in other chapters. The chapter concludes with a discussion of the economic benefits of improved system performance.

System performance measures often are viewed from the perspectives of both the user and the operator. Users are interested in characteristics, such as travel cost, travel time, and the reliability of successfully completing a trip within a certain time, each of which directly affects their ability to accomplish a trip purpose. Owners and operators are concerned with the level of service provided to users and the ability to respond to service disruptions so as to promote reliable and safe mobility and accessibility.

System Accessibility

System accessibility is defined as the ability of travelers and freight shippers to reach key destinations, such as hospitals, job sites, schools, factories, airports, ports, and community centers. In evaluating system performance, it is important to know how accessibility has changed over time. The measure most often used is the number of destinations reachable within a given travel time, in particular the number of jobs that are accessible. The Center for Transportation Studies, at the University of Minnesota, has developed a method for comparing morning peak-period accessibility to jobs by automobile across 51 U.S. metropolitan areas for 1990, 2000, and 2010 [UMN CTS 2013]. Figure 4-1 shows how accessibility to jobs has changed from 1990 to 2010. In 1990, for example, 2 million jobs across 51 metropolitan areas were accessible in an average travel time of 44 minutes by automobile. A decade later, in 2000, the average travel time increased to 52 minutes. But by 2010 that average travel time dropped to 47 minutes as travel speeds increased (to about where they were in 1990) [UMN CTS 2013]. The crossing of the 2000 and 2010 lines in figure 4-1 most likely reflects the impact of the December 2007 through June 2009 recession and the subsequent slow recovery, that is, not as many jobs were available for access.

A second University of Minnesota study [UMN CTS 2014] extends the analysis to consider transit accessibility to jobs. This more limited effort considers only morning peak-period transit schedules in 46 of the 50 largest (by population) U.S. metropolitan areas in January 2014. The 10 metro areas with the greatest accessibility to jobs by transit were (in rank order) New York, San Francisco, Los Angeles, Washington, Chicago, Boston, Philadelphia, Seattle, Denver, and San Jose. New York dominates this list by a wide margin. Due to its development density and extensive transit resources, it has 210,000 jobs accessible by transit within 30 minutes of total travel time and 1.2 million jobs accessible within 60 minutes. In contrast, for the ninth ranked city, Denver, where the jobs and population are more dispersed and transit service includes a rapidly expanding light rail system and an extensive bus network, the comparable accessibility figures are 20,000 jobs available within 30 minutes and 176,000 jobs available within 60 minutes. A more robust analysis would include other time periods, including tracking how transit accessibility changes over time, and other travel modes.

Congestion

The ability of travelers to reach a destination in a cost-effective, safe, and reliable manner is an important aspect of the Nation’s transportation system. The characteristics of making such trips, including travel time, costs, and access to facilities/services, are used to indicate the level of mobility afforded to users.

Road congestion in urban areas is one of the major causes for travel time delay. The Texas Transportation Institute has monitored congestion levels on the U.S. road network for decades and has reported in an annual Urban Mobility Report ² on the number of hours of congestion experienced by network users and the associated economic costs [TAMU TTI 2015]. Recent editions of the report provide data for 498 urban areas in the United States.

Table 4-1 shows the estimates for annual hours of delay, the number of gallons of wasted fuel due to delay, the dollar value of delay and wasted fuel, and a measure called the Travel Time Index (TTI).³ For example, a TTI value of 1.21 indicates that a trip taking 30 minutes without congestion will take an average of 21 percent longer, or just over 36 minutes (1.21 × 30), during the peak travel period.

Road congestion, in terms of amount and cost, has steadily increased since 2000. The exception was the economic recession from the end of 2007 to the middle of 2009, which had a dampening effect. Congestion in the Nation’s urban areas in 2014 had an economic cost of $160 billion compared to $114 billion in 2000 (2014 dollars). The average yearly delay per commuter rose from 37 hours in 2000 to 42 hours in 2014, a 13.5 percent increase, and the total national hours of delay in 2014 reached 6.9 billion hours—about a third higher than the 2000 total. The effects of congestion on truck movements accounted for $28 billion (17.5 percent) of the total congestion cost [TAMU TTI 2015]. In addition, the average commuter:

• wasted 19 gallons of fuel in 2014 (a week’s worth of fuel for the average U.S. driver), up from 8 gallons in 1982;
• experienced an average yearly delay of 42 hours in 2014; and
• planned for approximately 2.41 times (freeway only) as much travel time as would be needed in non-congested conditions to arrive at their destination on time 9 times out of 10 [TAMU TTI 2015].

The worst congestion levels (defined as “extreme, severe, or heavy”) affected only one in nine trips in 1982, whereas this proportion increased to more than one in three trips in 2014. In addition, the most congested sections of road (labeled extreme and severe) handled only 26.0 percent of all urban road travel, but accounted for 80 percent of peak period delays as shown in figure 4-2. It is important to note that congestion levels have increased over the past 30 years in all urban areas, from the largest to the smallest. Congestion is worse in the afternoon, but it can occur at any time throughout the day. Between 2011 and 2014, notable progress was made in reducing congestion during the afternoon peak hours of 4 and 5 pm (figure 4-3).

The Federal Highway Administration (FHWA) uses vehicle probe data⁴ to compile the Urban Congestion Trends report, which tracks 3 congestion measures in the 52 largest urban areas in the United States. While not as comprehensive as the Urban Mobility Report, which covers 498 urban areas and all of the congestion indicators reported above, the smaller scope of Urban Congestion Trends allows for more frequent updates. The latest edition of this report shows that congestion remained unchanged or marginally improved in 2015 [USDOT FHWA 2016]. The average duration of daily congestion⁵  decreased from 5 hours and 3 minutes in 2014 to 4 hours and 40 minutes in 2015, while the Travel Time Index (TTI) increased slightly, from 1.33 to 1.34.

Congestion and delays are not limited to roadways. The average length of commercial airline flight delays has been over 50 minutes in every year since 2004 and reached 59 minutes in 2015, even though the number of arriving domestic flights operated by the large U.S. airlines decreased by 18.4 percent over that period (table 4-2). More than 634,000 flights arrived at the gate more than 2 hours behind schedule in 2015. Mainline carrier’s average aircraft size (seats per aircraft mile) increased in 2015 by 3.5 seats, from 145.6 to 149.1, which is the highest level since 1994. This trend is forecasted to continue through 2035, especially with the retirement of older, smaller narrow-body aircraft (i.e., MD-80’s, 737-300/400/500, and 757’s). Airlines are retiring these less efficient aircraft and shifting to wide-body and larger narrow-body aircraft [USDOT FAA 2016], which often require more separation in the air and on the ground. Larger aircraft (a.k.a. “heavy”) typically require a safety margin or separation of 4 to 8 nautical miles from the following aircraft. This is because of wake turbulence, which is a violent or unsteady movement of air that forms behind an aircraft, especially during takeoff and landing. Operational factors and weather conditions may require additional separation, which may contribute to congestion and delays.

Flight delays are caused by a variety of reasons, ranging from extreme weather to disruptions in airline carrier operations (figure 4-4). The combined effects of non-extreme weather conditions, airport operations, heavy traffic volume, and air traffic control contributed to 22.9 percent of delays in 2015, a 10.6 percentage point improvement from 2004. Flight delays can ripple through the U.S. aviation system as late arriving flights, for whatever reason, delay subsequent flights—the cause of 39.8 percent of delays for scheduled flights in 2015.

Congestion is especially a problem for time-sensitive freight shipments. Various performance indicators are used to monitor time-related system performance. The USDOT’s FHWA, in cooperation with the American Transportation Research Institute (ATRI), is working to quantify the impact of traffic congestion on truck-based freight at 250 specific locations across the United States. Similar to the TTI, the primary measure is the ratio of uncongested speed to congested speed at key freight locations (often interstate- to-interstate interchanges). For example, a 21.3 mph peak period average speed and a 41.2 mph non-peak period average speed in Chicago yields a ratio of 1.94. Some of the most congested truck bottlenecks on freight- heavy highways in 2013 could be found in Chicago, IL (1.94), Austin, TX (1.93), Atlanta, GA (1.61), and Houston, TX (1.46) [USDOT BTS 2015].

On the inland water network, the U.S. Army Corps of Engineers (Corps) is responsible for 239 lock chambers and monitoring the movements of barges and other commercial vessels. In 2015 barge tows experienced an average delay of 2.4 hours navigating a lock (table 4-3), the largest delay on record and more than double the delay in 2000 [USACE 2016]. Furthermore, the percent of vessels that experienced any delays increased from 35 to 48 percent. The increase in delay is most likely due to the aging of the locks in the inland water system. On older systems, the majority of tows must be split into two parts and locked through their smaller (e.g., 600-foot) lock chambers, which were not designed to handle today’s longer (e.g., 1,200-foot) tows. The average age of locks under jurisdiction of the Corps is over 63 years,⁶  and it is expected that delays will likely increase without the needed rehabilitation and reconstruction of key locks.

System Reliability

Reliability is defined as the level to which one can make trips with some certainty that the actual trip will occur within an expected range of travel times. More reliability means less uncertainty associated with trips due to events such as crashes, vehicle breakdowns, and similar incidents; work zones; unannounced road work; weather; and special events that can often lead to widely varying travel times from one day to the next for the same trip.

The Planning Time Index (PTI)⁷  is used to estimate the extra time that one should plan for a trip to assure on-time arrival with 95 percent confidence. For example, a PTI of 1.5 means that for a traveler to arrive on time 19 out of 20 times, the traveler should allow 50 percent more time. This means 30 extra minutes should be budgeted for a trip that in free flow conditions would typically take 60 minutes to arrive on-time. The extra time allowed, in this example 30 minutes, is called the buffer index, which is often used to assess system reliability. Figure 4-5a shows that the Travel Time Index (TTI) has been trending upward with 2015 levels mostly above 2013 and 2014, indicating that (as noted above) urban traffic congestion has been increasing . Based on PTI data collected from 52 cities between 2013 and 2015, travelers would have to plan at least 150 percent more travel time to arrive “on-time” for 19 out of 20 trips (figure 4-5b). Through the first half of 2015 that minimum rose to at least 180 percent more travel time, indicating less reliability due to higher traffic congestion. Figure 4-5c shows the potential impact of weather on travel as the congested hours were generally higher in winter than in summer months. It also shows that average congested hours per day in 2015 fell slightly below their 2014 levels. So while congestion levels were higher, for some unknown reason they didn’t last quite as long.

For non-highway modes, different measures can be used to assess system reliability. For passenger transportation, for example, on-time performance is often an indicator of service reliability. Amtrak experienced a significant improvement in on-time performance with a record 83.0 percent on-time performance in 2012, but which declined to 71 percent in 2015 (table 4-4). Greater improvement in on-time performance is seen for trips over 400 miles in length, where on-time performance jumped from 42.1 percent in 2005 to 68.1 percent in 2015. The vast majority of passenger train services outside the Northeast Corridor are provided over tracks owned by and shared with the Class I freight railroads. As a result, Amtrak’s on-time performance is largely dependent on the condition and performance of the host railroads, with the important exception of Amtrak-owned tracks in the Northeast Corridor.

U.S. airlines reported that over 18 percent of domestic scheduled flights, or more than one million flights, arrived at the gate at least 15 minutes late in 2015. The average length of delay for late arriving flights was almost an hour. Almost 11 percent, or 636,000 flights, arrived at the gate more than 2 hours late (table 4-2). Late arrivals peaked at 24.1 percent in 2007, and since then have been in the range of about 18 to 20 percent.

For the U.S. Army Corps of Engineers inland waterway locks, system reliability can be measured as the percent of time a lock is unavailable for use (defined as the cumulative periods over a year during which a lock facility was unable to pass traffic). Locks could be unavailable for a number of reasons, ranging from scheduled maintenance, unexpected stoppages due to operational issues, and weather conditions such as flooding and ice. For example, high water levels and flows shut down 22 locks and stopped cargo movements along the Upper Mississippi River and its confluences in late April 2013 [USACE 2013]. As shown in figure 4-6, the total number of hours of unavailability in 2015 was almost 132,000, nearly 75 percent higher than the level in 2000. Lock unavailability due to scheduled operations, such as maintenance, ranged from 46 to 85 percent over the period shown and averaged 61 percent. Scheduled downtime was 62 percent of total down time in 2015. Unscheduled lock chamber downtime peaked during the 2006 to 2010 timeframe, over which it averaged about 77,000 hours per year. Over the past 4 years unscheduled lost time dropped to more typical levels, averaging about 52,000 hours per year.

System Resiliency

Many parts of the Nation’s transportation system are vulnerable to both natural and man- made disruptions. Because of this vulnerability, transportation firms and agencies have become interested in providing a system that is resilient to disruptive impacts. A resilient transportation system has design-level robustness that can withstand severe blows, respond appropriately to threats, and mitigate the consequences of threats through response and recovery operations [USDOT VOLPE 2013].

System Disruptions from Extreme Weather

The United States has experienced extreme weather events throughout its history. However, with the heavy concentration of the Nation’s population in urban areas (many along the coasts) and with a strong reliance on the efficient movement of people and goods, recent weather events have resulted in extensive economic and community costs. For example, the U.S. Department of Commerce (USDOC), National Oceanic and Atmospheric Administration (NOAA) estimated that the United States experienced 188 weather/climate disasters (or about 5 per year on average) since 1980, including such events as hurricanes, tornadoes, floods, and droughts/wildfires. The overall damage from each of these events exceeded $1 billion, resulting in more than a $1 trillion cumulative cost to the Nation [USDOC NOAA NCEI 2016]. Part of the physical recovery costs and overall economic impact were due to the damage and disruption to the transportation system. The year 2005 was the most costly since 1980, with over $200 billion in damages and 2,002 deaths due to extreme weather. In 2015 there were ten such events causing 155 deaths and estimated damages of $22.4 billion.

Hurricane Sandy and the January–February 2015 New England blizzards are two recent examples of extreme weather events that disrupted the transportation system. Hurricane Sandy caused extensive damage in October 2012 along the New Jersey, New York, and Connecticut coasts and record flooding in lower Manhattan. Roads and bridges were damaged throughout the region, and road and rail tunnels were flooded. The region’s major airports were closed, and transit service was not restored in many areas until several months after the storm [Kaufman, Qing, Levenson and Hanson 2012].

Between January 24th and February 25th, 2015, severe winter weather produced blizzard- like conditions and record setting snowfalls throughout the New England region. Boston and Worcester, MA, were hit particularly hard, each recording over 94 inches of snow over the 30-day period. The transportation system in the region was severely disrupted. Over those 30 days the Massachusetts Department of Transportation implemented 171 lane or road closures of significant duration. Massachusetts Bay Transportation Authority commuter rail, heavy rail, and light rail services ran between 50 and 80 percent of normal levels over much of the period, and ferry service was similarly reduced. Boston Logan International Airport experienced 4,576 flight cancellations, impacting approximately 230,000 passengers. AMTRAK canceled all Northeast corridor service between New York and Boston on January 27th, and canceled two or more trains on 10 additional days [MEMA 2015].

A snow event of even broader impact occurred in the eastern United States in January of 2016. Box 4-A provides some illustrative information about that storm and its impacts.

There are economic and other costs associated with such major disruptions, including those resulting from cleanup and infrastructure repair, foregone commercial opportunities (e.g., lost business sales due to closures), and loss in productivity. For example, the economic impact to New Jersey and New York resulting from Hurricane Sandy was estimated at $67 billion [USDOC NOAA 2016], although some studies have suggested that the impact was less given the economic rebound associated with the recovery from the hurricane [Rutgers University 2013]. This cost included the estimated expenditures to replace the roads, bridges, and transit facilities damaged by the storm. IHS Global Insight estimates that each day of snow- related shut down in Massachusetts results in direct and indirect economic impacts exceeding $250 million⁸  [IHS 2015].

Although the impacted regions suffered huge losses during their respective storms, one of the key lessons from each event was the importance of transportation system resilience. Major transportation facilities— roads, bridges, transit systems, ports, and airports—were in operation within weeks of the severe weather. In most cases advanced preparations by state and local government agencies (e.g., moving transit vehicles out of vulnerable areas and establishing emergency management centers) can mitigate disruption to transportation systems [MTA 2012]. The existence of redundant paths in the transportation network provided travel options for both person and freight trips seeking to avoid travel blockages. In all three cases the transportation agencies were able to quickly put the transportation system back into operation, thus minimizing the economic impact to state and regional economies.

Security Concerns

The Transportation Security Administration (TSA), of the U.S. Department of Homeland Security, screens people as they pass through security checkpoints at 450 airports with Federal screening, and at other passenger checkpoints. In 2015 TSA officers screened more than 708 million passengers (more than 1.9 million per day), 1.6 billion carry-on bags, 432 million checked bags, and 12.9 million airport employees. Despite news headlines that report long lines when they do occur, nationwide less than 2 percent of passengers (14.1 million) waited in line for more than 20 minutes.

These TSA inspections prevented a wide array of prohibited items from being brought onto passenger aircraft, notably 2,653 firearms, 83 percent of which were loaded (see box 4-B). Other prohibited items discovered in checked and carry-on bags included many thousands of knives, swords, and other sharp blades; ammunition; gunpowder, black powder, flashbang grenades, and fireworks; and inert and replica explosive devices. Federal air marshals flew more than a billion miles to help keep the skies secure for travel [USDHS TSA 2016].

International piracy incidents at sea, including attacks, boardings, hijackings, and kidnappings, are another security concern affecting U.S. citizens traveling overseas. Piracy activity has been monitored closely by the Office of Naval Intelligence (ONI), especially after the hijacking of the U.S.- flagged Maersk Alabama on April 8, 2009. In 2015 the waters of Southeast Asia experienced 254 piracy events, an increase of 54 over those reported for 2014. The Gulf of Guinea, in West Africa, had 96 events, about the same number as in 2014. The Horn of Africa waters, which have been of major concern since 2009, had no events in 2015 and only two attempted boardings in 2014 [USN ONI 2016].

Economic Benefits of Improved System Performance

Maintaining and improving the performance of the transportation system provides benefits to people and economy at all levels. Performance can improve either through investments that expand capacity, such as adding a transit station, or enhance performance on the existing system through investments, such as improving intersection signal timing and adding a high-occupancy lane. Improving system performance improves the economy by reducing congestion, linking markets, and increasing shipment and personal travel time reliability. It is also important to maintain the existing infrastructure to ensure continued economic growth.

Comprehensive estimates of economic benefits associated with improved system performance are extremely limited. The Urban Mobility Scorecard [TAMU TTI 2015] includes an estimate of the cost to system users of about $160 billion in delay and fuel wasted in congestion costs in 2014. The 2012 Urban Mobility Report [TAMU TTI 2013] also estimated the beneficial effects of public transportation and roadway operational improvements to reduce these costs. For public transportation, the analysis examined what would happen if transit services were eliminated in the 498 urban areas that were part of the study. The additional system cost (or the cost avoided given transit service) is thus considered the benefit of transit investment. For 2011 the savings included 865 million hours of delay and 450 million gallons of fuel, resulting in an estimated $20.8 billion (2011 dollars) in cost savings. For road operational improvements, the report estimated 364 million hours of delay eliminated and 194 million gallons of fuel saved, resulting in an estimated $8.5 billion in cost savings.

The Federal Highway Administration and Federal Transit Administration examined transportation investment benefits versus the costs in its 2013 Status of the Nation’s Highways, Bridges, and Transit: Conditions and Performance Report (C&P) [FHWA 2013]. The C&P finds that the annual capital investment level needed to maintain the conditions and performance of highways and bridges at 2010 levels through the year 2030 is projected to range from $65.3 billion to $86.3 billion per year, depending on the growth rate of vehicle- miles traveled. Moving existing transit assets to a state of good repair would require an annualized investment of $18.5 billion through the year 2030. These investment levels hold the potential to maintain the transportation related economic development on its current upward trend, while investing at lower levels could hamper economic growth.

Based on a 2005 survey in Portland, Oregon, the White House Council of Economic Advisors [NECPCE 2014] reported the economic costs associated with poor transportation system performance.  Higher business costs were found to cause Portland businesses to hold more inventories or rely on additional distribution centers:

• Portland General Electric estimated that it spend approximately $500,000 a year for maintenance crew travel time.
• Nike spends an additional $4 million per week to carry an extra 7-to-14 days of inventory in case of shipping delays.
• One day of delay requires American President Line’s eastbound trans-Pacific services to increase its use of containers and chassis by 1,300, which adds $4 million in costs per year.
• A week-long disruption to container movements through the Ports of Los Angeles and Long Beach could cost the national economy between $65 and $150 million per day.

The economic benefits of transportation investments can stem from meeting needed capacity expansions. For the trucking industry alone, the Federal Highway Administration calculated that highway bottlenecks cause more than 243 million hours of delay each year, at a cost of $7.8 billion annually [FHWA 2007]. A recent FHWA report [LAWRENCE 2015] provides a compendium of case studies of benefit cost analysis of operational improvements, such as coordinated arterial traffic signal timing, transit signal priority, ramp metering at freeway on- ramps, high occupancy toll lanes, work zone traffic management, and travel demand management. Some of the case studies provide actual estimated economic benefits of the improvements. For example, implementing adaptive signal control in Greeley and Woodland Park, CO, was estimated to generate annual benefits of $2.2 million, and providing a freeway service patrol program in Florida had an estimated benefic cost ratio of 6.7.

The American Recovery and Reinvestment Act (Recovery Act) of 2009 allocated an additional $48 billion into improving transportation infrastructure and operations. A 2012 report by the Treasury [TREASURY] summarizes the historical and recent literature linking transportation investments to economic growth, from the linking of the national rail network to the creation of the interstate highway system and into the 22nd century with high speed rail and connected vehicle technologies. While studies differ in the magnitude of the economic return on investment, they all find positive economic returns to transportation investments. Fernald [AER 1999] found that previous investments in infrastructure led to substantial productivity gains and highlighted the potential for further increases in productivity through additional, well- targeted investments.

References

American Road and Transportation Builders Association (ARTBA) Transportation Development Foundation. 2011. The 2010 U.S. Transportation Construction Industry Profile. Washington, DC.

IHS Global Insight (IHS). 2015. The Economic Costs of Disruption from a Snowstorm; Study Prepared for the American Highway Users Alliance. Available at http://www.highways. org/ as of May 2016.

Kaufman, S., C. Qing, N. Levenson and M. Hanson. 2012. Rudin Center for Transportation, New York University Wagner Graduate School of Public Service. Transportation During and After Hurricane Sandy (Nov. 15, 2012). Available at http:// wagner.nyu.edu/ as of May 2016.

Massachusetts Emergency Management Agency (MEMA). 2015 Severe Winter Weather Pattern Impacts - Supplemental Information (March 27, 2015). Available at http://www. mass.gov/ as of May 2016.

Metropolitan Transportation Authority (MTA).

MTA Prepares for Hurricane Sandy. Oct. 26. 2012. Available at http://www.mta.info/ as of May 2016.

National Economic Council and the President’s Council of Economic Advisors (NECPCEA), An Economic Analysis of Transportation Infra- structure Investment,. July 2014. Available at https://www.whitehouse.gov/sites/default/files/ docs/economic_analysis_of_transportation_in- vestments.pdf as of December 2016.

Nelson, A.C. et al. 2009. The Best Stimulus for the Money: Briefing Papers on the Economics of Transportation Spending. Metropolitan Research Center, University of Utah.

Lawrence, M. et al. 2015. Transportation Systems Management and Operations Benefit-Cost Analysis Compendium. FHWA-HOP-14-032. U.S. Department of Transportation, Federal Highway Administration. Available at http://www.ops. fhwa.dot.gov/ as of May 2016.

Rutgers University. 2013. The Economic and Fiscal Impacts of Hurricane Sandy in New Jersey: A Macroeconomic Analysis, Issue Paper No. 34. Available at http://bloustein. rutgers.edu/ as of May 2016.

Texas A&M University (TAMU), Texas Transportation Institute (TTI).

— 2015. 2015 Urban Mobility Scorecard. Available at http://tti.tamu.edu/ as of May 2016.

— 2013. 2012 Urban Mobility Report. Available at http://tti.tamu.edu/ as of May 2016.

The American Economic Review (AER).

Roads to Prosperity? Vol. 89, No. 3, June 1999. American Economic Association. Available at https://www.jstor.org/ stable/117036?seq=1#page_scan_tab_contents as of December 2016

Transportation Research Board (TRB). Funding and Managing the U.S. Inland Waterways System: What Policy Makers Need to Know, Special Report 315, 2015. The National Academies, Washington, DC. Available at http://www.trb.org as of May 2016.

University of Minnesota (UMN), Center for Transportation Studies (CTS).

—2014. Access Across America: Transit 2014. Report CTS 14-11. Available at http://www.cts. umn.edu/ as of May 2016.

—2013. Access Across America: Auto 2013. Report CTS 13-20. Available at http://www.cts. umn.edu/ as of May 2016.

U.S. Army Corps of Engineers (USACE), Navigation Data Center:

—2013. Lock closures (as of Apr. 23, 2013). Personal Communications.

—2016. Lock Use, Performance, and Characteristics. Available at http://www. navigationdatacenter.us/ as of May 2016.

U.S. Department of Commerce (USDOC). National Oceanic and Atmospheric Administration (NOAA). National Centers for Environmental Information (NCEI). 2016. Billion-Dollar Weather and Climate Disasters. Available at https://www.ncdc.noaa.gov/ billions/ as of August 2016.

 U.S. Department of Homeland Security (USDHS). Transportation Security Administration (TSA). The TSA Blog: TSA 2015 Year in Review (January 21, 2016). Available at http://blog.tsa.gov/ as of May 2016.

U.S. Department of Transportation (USDOT). Bureau of Transportation Statistics (BTS). Freight Facts and Figures 2015. Available at http://www.bts.dot.gov/bts/ as of May 2016.

U.S. Department of Transportation [USDOT]. Federal Aviation Administration [FAA]. 2016. FAA Aerospace Forecast: Fiscal Years 2016 - 2036. Available at https://www.faa.gov/ as of May 2016.

U.S. Department of Transportation (USDOT). Federal Highway Administration (FHWA):

—2007. Understanding Freight Bottlenecks, FHWA-HRT-07-003. Available at https://www.fhwa.dot.gov/publications/ publicroads/07mar/05.cfm as of December 2016

—2013. Conditions and Performance Report. Available at https://www.fhwa.dot.gov/ policy/2013cpr/ as of December 2016.

—2015. Urban Congestion Trends, FHWA- HOP-16-0326. 2016. Available at http://www. ops.fhwa.dot.gov/ as of August 2016.

U.S. Department of Transportation (USDOT), Volpe National Transportation Systems Center (VOLPE). 2013. Beyond Bouncing Back: Critical Transportation Infrastructure Resilience. Available at http://www.volpe.dot. gov/ as of May 2016.

U.S. Department of the Treasury, An Economic Analysis of Infrastructure Investment, 2012. Available at https://www.treasury.gov/ resource-center/economic-policy/Documen ts/20120323InfrastructureReport.pdf as of December 2016

U.S. Navy (USN). Office of Naval Intelligence (ONI). 2016. Horn of Africa/Gulf of Guinea/ Southeast Asia: Piracy Analysis and Warning Weekly (PAWW) Report (May 26, 2016). Available at http://www.oni.navy.mil/ as of May 2016.

Weisbrod, G. and A. Reno. 2009. Economic Impact of Public Transportation Investment. American Public Transportation Association, Washington, DC.

¹ The Moving Ahead for Progress in the 21st Century Act (MAP-21) requires the U.S. Department of Trans- portation to establish performance measures and stan- dards for several program/policy areas. MAP-21 also requires statewide and metropolitan transportation plan- ning agencies to establish and use performance-based approaches for transportation decision-making. The Fixing America’s Surface Transportation (FAST) Act, enacted in December 2015, continues these initiatives.

² In 2015 the report title was changed to Urban Mobility Scorecard.

³ The ratio of the travel time during the peak period to the time required to make the same trip at free-flow speeds.

⁴ Vehicle probe data are based on real-time vehicle posi- tions, typically obtained from the vehicle’s GPS receiver or the operator’s mobile phone.

⁵ Hours of congestion is defined as the amount of time when highways operate at less than 90 percent of free- flow speeds.

⁶ A recent study [TRB 2015] shows that, when adjusted for the dates of major rehabilitation projects, the effec- tive average age of locks is about 10 years less, but that still puts the average age at over 50 years.

⁷ The ratio of travel time on the worst day of the month compared to the time required to make the same trip at free-flow speeds.

⁸ IHS estimates for other, more populous states are: New York, $700 million; Illinois, $400 million; Pennsylvania, $370 million; Ohio, $300 million; and New Jersey, $290 million.