Office of Operations Freight Management and Operations

Freight Intermodal Connectors Study

Chapter 3. Connector Characteristics, Use, Condition, and Performance

General Characteristics of Freight Intermodal Connectors

The inventory of National Highway System (NHS) freight intermodal connectors is primarily comprised of relatively short connectors. Table 5 shows that the vast majority of the connectors are short in length with 71 percent of the connectors being less than one mile, and 31 percent of the connectors are less than one-quarter of a mile long.

There are very few long connectors, but they make up the majority of the centerline miles of the NHS intermodal connector system. Twelve percent of the freight intermodal connectors are two miles or longer. From a centerline mile perspective, these longest 12 percent of connectors are responsible for nearly half of the total 1,484 miles of freight intermodal connectors in the U.S.

The average length of freight intermodal connectors increases as the roadway functional class increases. Table 6 shows the average length of freight intermodal connectors by functional classification. Local roads are the shortest connectors with an average of 0.45 miles and principal arterials are the longest connectors with an average of 1.51 miles.

Two-thirds of freight intermodal connectors are owned by city, county, or other local agencies with the other one-third owned by State agencies (Table 7). In total, 91 percent of local roads are owned by local agencies; 84 percent of collectors are owned by local agencies; and arterials are split roughly evenly between local and State agencies.

Combining the finding of Tables 5 to 7 together, it indicates that NHS freight intermodal connectors can be generalized by falling into two categories:

  1. A large number of short, local roads and minor collectors that are owned by city and local municipalities.
  2. A small number of longer arterials that are owned by State agencies.
Table 5. Distribution of Segment Lengths for Freight Intermodal Connectors.
Length (Miles) Number of Connectors Percent of Total
0 to 0.25 467 30.9%
0.26 to 0.99 613 40.5%
1.00 to 1.99 245 16.2%
2.00 to 2.99 92 6.1%
3.00 to 3.99 40 2.6%
4.00 to 4.99 20 1.3%
5.00 to 5.99 15 1.0%
6.00 to 6.99 6 0.4%
7.00 to 7.99 5 0.3%
8.00 to 8.99 2 0.1%
9.00 to 9.99 0 0.0%
10 and more 8 0.5%
All 1,513 100.0%

(Source: 2013 Federal Highway Administration Highway Performance Monitoring System.)

Table 6. Average Length of Connectors by Functional Classification.
Functional Classification Average Length Number of Connectors
Local 0.42 202
Minor Collector 0.56 7
Major Collector 0.70 386
Minor Arterial 0.99 558
Principal Arterial 1.51 356
Interstate 0.39 2
Unclassified 0.23 2
All 0.98 1,513

(Source: 2013 Federal Highway Administration Highway Performance Monitoring System.)

Table 7. Number of Connectors by Owner and Functional System Code.
Owner Average Length Local Minor Collector Major Collector Minor Arterial Principal Arterial (Other Freeways and Expressways) Principal Arterial (other) Interstate Unclassified Total Percent of Total
City or Municipal Highway Agency 0.68 121 1 251 320 3 125 Empty cell. 1 822 54%
State Highway Agency 1.66 18 4 63 172 29 149 2 1 438 29%
County Highway Agency 0.94 17 2 56 48 Empty cell. 37 Empty cell. Empty cell. 160 11%
Other Public Instrumentality 0.47 38 Empty cell. Empty cell. 2 Empty cell. 3 Empty cell. Empty cell. 43 3%
Town or Township Highway Agency 0.61 3 Empty cell. 14 15 Empty cell. 5 Empty cell. Empty cell. 37 2%
Other Local Agency 0.65 5 Empty cell. 2 Empty cell. Empty cell. 4 Empty cell. Empty cell. 11 1%
Other State Agency 1.19 Empty cell. Empty cell. Empty cell. 1 Empty cell. Empty cell. Empty cell. Empty cell. 1 <1%
State Toll Authority 0.62 Empty cell. Empty cell. Empty cell. Empty cell. Empty cell. 1 Empty cell. Empty cell. 1 <1%
Total 0.98 202 7 386 558 32 324 2 2 1,513 100%
Percent of Total Empty cell. 13% 0% 26% 37% 2% 21% 0% 0% 100% Empty cell.

(Source: 2013 Federal Highway Administration Highway Performance Monitoring System.)

Freight Intermodal Connector Truck Volumes

Truck volumes on freight intermodal connectors range from a few trucks per day to well over 1,000 per day. The Federal Highway Administration (FHWA) maintains the Highway Performance Monitoring System (HPMS) database as a national level highway information system that includes data on the extent, condition, performance, use, and operating characteristics of the nation's highways. Using FHWA HPMS data, it is estimated that the average truck volume on freight intermodal connectors is 762 trucks per day. Half of all of connectors have less than 500 trucks per day. Seventy-five percent of connectors have less than 1,000 trucks per day.

In total, it is estimated that there were 1,368,219 truck miles traveled on freight intermodal connectors in 2013. There are a small number of intermodal connector roads that carry the majority of the intermodal truck vehicle miles traveled (VMT), the amount of mileage traveled by trucks on the nation's roadways. Nearly half of all of the intermodal truck VMT occurs in the top 5 percent of freight intermodal connectors in terms of volume. Ninety-seven percent of the truck VMT is captured on the top 50 percent of connectors.

State highway agencies are the owners of just 29 percent of freight intermodal connectors, but they carry 59 percent of the total connector truck VMT. The reverse is true for city or municipal highway agencies. They own 54 percent of intermodal connectors, however carry just 29 percent of connector truck VMT. The vast majority (88 percent) of connector truck VMT occurs in urbanized areas.

The truck volume numbers combined with the centerline mileage data indicate that there is a trade-off between allocating resources to the smaller number of freight intermodal connectors with large truck VMT (more likely to be longer, State-owned arterials) versus allocating resources to the large number of very short connectors with small truck VMT (more likely to be shorter, locally owned roads).

Truck Volume Data Accuracy

The FHWA HPMS is the most comprehensive source of truck volume data on freight intermodal connectors. HPMS data can be used to examine trends in truck activity relative to other factors such as roadway characteristics and freight facility types. However, for planning studies focused on an individual freight intermodal connector, the accuracy of the HPMS is often not sufficient. The limitations of the HPMS are understandable, because the database is not intended to be utilized as a source for truck activity data on local roads with relatively low volumes.

The truck volume accuracy issue on freight intermodal connectors is primarily due to the HPMS process that is used to estimate truck volumes. This process allows for the use of truck percentage estimates for many locations, often through the use of truck percentages at nearby locations or roadways with similar functional classification. The HPMS process is appropriate to develop system level estimates of truck activity across various roadway functional classifications. However, because truck percentages on freight intermodal connectors tend to be higher than these proxy roads, this process tends to underestimate the number of trucks on the connectors.

Truck accuracy issues are most evident in examining the percentage of single unit and combination trucks on freight intermodal connectors. On most of the freight intermodal connectors, the HPMS reports the number of single unit trucks as higher than combination trucks. This is in contrast to the prevalence of combination trucks being used to access the freight facilities that are being accessed by trucks using freight intermodal connectors. The high percentage of single unit trucks on freight intermodal connectors is likely a function of the use of nearby and similar roadways to estimate single unit and combination trucks rather than the use of actual roadways. One potential improvement to the HPMS would be to utilize a unique factor for estimating single unit and combination trucks for freight intermodal connectors that is not based on factors that are used in other types of locations.

The challenges associated with using the HPMS database for freight intermodal connectors are well known by many freight facility planners. Transportation agencies generally collect new truck count data for planning studies focused on individual freight intermodal connectors.

Truck volume data is critical to estimating truck performance such as crash rates and impacts of congestion and pavement condition. Additionally, truck volume data are critical to determining the benefits of improvements and developing project prioritization related to improvements of freight intermodal connectors. Therefore, improving truck volume data is the single most important data improvement that needs to occur in terms of understanding the role, performance, and potential of the Nation's freight intermodal connectors.

Pavement Condition of Freight Intermodal Connectors

State Departments of Transportation (DOTs) submit pavement condition data into the HPMS database in the form of International Roughness Index (IRI) values. The IRI measures the smoothness of the roadway using an algorithm based on the longitudinal profile of a section of the road. (Federal Highway Administration (2013). Chapter 3—System Conditions, Status of the Nation's Highways, Bridges, and Transit: Conditions and Performance Report.) Lower IRI values indicate better pavement conditions (i.e., smoother) while higher values indicate worse conditions (i.e., rougher). Table 8 shows the condition categories for IRI measurements based on the FHWA Conditions and Performance Report.

Of the 1,239 connectors with available pavement data in the HPMS, 438 (37 percent) are rated as poor and 236 (19 percent) are rated as mediocre. Only 15 percent of the connectors have a good or very good pavement conditions.

Average IRI values for connectors owned by State highway agencies is 154 (fair) compared to an average value of 257 (poor) for city or municipal highway agencies. Additionally, average IRI values tend to decrease as the length of connectors increases (Table 9). Combined with earlier findings, this reveals that there are two primary types of connectors:

  1. Short, low-volume connectors owned by cities or municipal agencies with poor pavement condition; and
  2. Relatively long, high-volume connectors owned by State highway agencies with fair pavement condition.
Table 8. International Roughness Index Categories.
Pavement Condition Categories International Roughness Index Rating (inches/mile) Pavement Condition Description Number of Connectors Percent of Total
Very Good <60 Newly built or resurfaced and distress-free. 14 1%
Good 60-94 Smooth surface with little to no cracking or rutting. 103 8%
Fair 95-170 Serviceable with shallow rutting and moderate cracks beginning to occur, but does not affect travel speed on the connector. 428 35%
Mediocre 171-220 Same problems as fair but worse, causing some reduction in speed. 236 19%
Poor >220 Major problems with potholes, etc., causing substantial reductions in speed. 458 37%
Total Empty cell. Empty cell. 1,239 100%

Table 9. Average International Roughness Index Rating by Length of Connector.
Length (miles) Number of Connectors Average IRI
0 to 0.99 862 233
1.00 to 1.99 208 179
2.00 to 2.99 84 153
3.00 to 3.99 32 134
4.00 to 4.99 18 138
5.00 to 5.99 14 111
6.00 to 6.99 6 95
7.00 to 7.99 5 114
8.00 to 8.99 2 93
9.00 to 9.99 0 N/A
10 and more 8 96
All 1,239 211

National Performance Goals and Connector Pavement Conditions

The U.S. Department of Transportation (USDOT) set a national performance goals for 2013 of having 57 percent of vehicle miles traveled (VMT) on the entire National Highway System to be on pavements with good ride quality. As noted in the 2015 American Association of State Highway and Transportation Officials (AASHTO) Transportation Bottom Line Report, States and other owners of the road system have increasingly focused their resources on improving the road systems that are used most extensively by passengers and goods movement. As a result, the percentage of VMT on roads identified as in good condition improved between 2000 and 2010, even while the length of roads in good condition has declined from 43 percent to 35 percent.

This trend in roadway maintenance has been detrimental for the quality of freight intermodal connectors. As the connectors often have lower total vehicle volumes relative to similarly classified roadways, they also tend to fare worse in VMT-based performance metrics.

Freight intermodal connectors are more likely to approach poor condition faster than other roadways because of their high truck percentage. The 57 percent VMT goal makes it more challenging for connectors that fall into poor condition to compete for funding, because the cost to improve a roadway from poor to good condition far exceeds the cost to improve a roadway from fair to good.

State Departments of Transportation can more cost-effectively reach the 57 percent VMT goal by improving fair roads with high volumes to good condition than they can by improving freight intermodal connectors with lower volumes from poor to good condition. Additions to the national performance goals that could lead to greater improvement in roadway conditions for freight intermodal connectors include the following:

  • A goal to limit the maximum percentage of roadways in poor condition.
  • Adjustment of the 57 percent goal to be based on passenger car equivalent VMT rather than total VMT.
  • Specific goals for pavement condition of freight intermodal connectors in each State.

Speeds on Freight Intermodal Connectors

This section assesses the performance of intermodal connectors using average truck speed data available in the FHWA National Performance Monitoring Research Data Set (NPMRDS). Speeds were extracted from the database using April 2014 data for all intermodal connectors. Speeds were calculated for the following four periods:

  1. Morning hour – 8:00 a.m. to 9:00 a.m.
  2. Midday hour – 12:00 p.m. to 1:00 p.m.
  3. Afternoon hour – 5:00 p.m. to 6:00 p.m.
  4. Late night period – 12:00 a.m. to 3:00 a.m.

The late night period is assumed to represent free-flow speeds for the intermodal connectors. Speed differentials between the late night speeds and speeds from the other three periods are assumed to be based on some type of truck bottleneck.

Table 10 shows the average truck speeds for urban and rural designated roadways for each time period. The average speeds during the morning, midday, and afternoon time periods for urban roads are the same at 25 miles per hour (mi/h). Similarly, for rural roads, average speeds for the morning and midday time periods are 37 mi/hr with the afternoon time period average speed only slightly faster at 38 mi/hr. During the late night time period (midnight to 3:00 a.m.), the average rural speed is 42 mi/hr, 50 percent faster than 28 mi/hr average speeds on urban roads. The speeds also are 50 percent higher on rural roads relative to urban roads for the other three time periods. The difference between late night speeds and daytime speeds is just over 10 percent for both urban and rural locations.

There appears to be a relationship between pavement condition and truck travel speeds. Table 11 highlights average truck travel speeds by pavement condition and time of day. During the late night time period between 12:00 a.m. and 3:00 a.m. (which is assumed to be free-flow conditions), the speeds on poor pavement is 23 mi/hr compared to speeds of 27 mi/hr, 31 mi/hr, and 32 mi/hr for mediocre, fair and good pavement condition respectively. Similarly, speeds during other times of day are generally slower for pavement that is of worse condition.

There is no demonstrated relationship between pavement quality and congestion. The decrease from free-flow speeds to peak-period speeds falls within a range of 10 to 16 percent, but there is no trend in terms of this decrease relative to pavement condition.

Table 10. Average Speeds of Intermodal Connectors by Rural/Urban Designation (miles per hour).
Urban Functional System Code Number of Connectors Time Period (8:00 to 9:00 a.m.) Time Period (12:00 to 1:00 p.m.) Time Period (5:00 to 6:00 p.m.) Time Period (12:00 to 3:00 a.m.) Difference Between Late Night and Slowest Day Speed
Urban 1,380 25 25 25 28 -11%
Rural 123 37 37 38 42 -12%

(Source: 2014 National Performance Management Research Data Set.)


Table 11. Average Speeds of Intermodal Connectors by Pavement Condition (miles per hour).
International Roughness Index (IRI) Category Number of Connectors Time Period (8:00 to 9:00 a.m.) Time Period (12:00 to 1:00 p.m.) Time Period (5:00 to 6:00 p.m.) Time Period (12:00 to 3:00 a.m.) Difference Between Late Night and Slowest Day Speed
Poor 354 20 21 21 23 -13%
Mediocre 221 24 24 23 27 -15%
Fair 469 28 28 28 31 -10%
Good 118 27 28 29 32 -16%
Very Good 38 28 26 27 29 -10%

(Source: 2014 National Performance Management Research Data Set data.)

Total delay experienced on freight intermodal connectors was estimated based on the speed data in Table 10. Total urban and rural VMT was estimated based on multiplying truck volumes by connector lengths using the HPMS data. Hourly distribution for urban and rural roads was extracted from available truck count data. (Texas Department of Transportation, Developing Freight Highway Corridor Performance Measure Strategies in Texas, 2006; Hampton Roads Planning District Commission, Hampton Roads Intermodal Management System.) The speeds in Table 11 were expanded to represent periods of the day rather than single hours. Based on this analysis, it is estimated that there were 4,237 hours of truck delay every weekday on freight intermodal connectors. This equates to roughly 1,059,238 hours of truck delay annually on freight intermodal connectors. Using the percentages of truck Annual Average Daily Traffic (AADT) relative to total AADT, it is estimated that the total annual auto delay on freight intermodal connectors is 12,181,234 hours.

Freight Intermodal Connector Performance Measurement

The performance of freight intermodal connectors can be measured by using inputs across a number of data elements including speed, travel time, travel time reliability, safety, and cost. This data can be tracked over a period of time to understand if and how the performance of a connector is changing and the rate of change in performance. Additionally, these data can be used to understand how freight improvement projects have changed the performance of a connector's ability to move goods. The performance measures can also be tied to the cost of supply chains to determine the impact of performance on overall cost of goods, cost of doing business, and the competitive position of one terminal relative to others.

There are currently few freight performance measures that are tracked on a regular basis by State DOTs or metropolitan planning organizations (MPOs). FHWA recently started tracking a Freight Efficiency Index as part of the Freight Performance Measures program. The Freight Efficiency Index includes four categories: 1) intermodal; 2) truck bottlenecks; 3) border crossings; and 4) urban mobility, along with an aggregate measure of freight performance. The intermodal component of the index is based on the following features:

  • Approximately 43 miles of intermodal roadways (container ports and intermodal rail facilities) are included in this measure; more than one-half of these roadways are Functional Class Principal Arterial or above with design speeds at 35 miles per hour or higher.
  • Geographic Information Systems (GIS) and statistical algorithms are used to select data for the highway segments at each location for the corresponding quarter.
  • Average speeds are placed in a table and the key indicator is an average speed representing all locations. A quarterly measurement is developed based on the average of the average speeds at 30 facilities.

An example of the freight intermodal measure for the fourth quarter of Fiscal Year (FY) 2014 is shown in Figure 5. This graphic shows the current reading, along with the best, worst, and average readings over the last three years. The connectors measured in this index do not match with the designated NHS freight intermodal connectors, but are generally a select sample of these roadways.

Figure 5. Graph. Intermodal Component of Federal Highway Administration Freight Efficiency Index.

Figure 5 is a figure depicting the Intermodal Mobility component of the Federal Highway Administration Freight Efficiency Index for the fourth quarter of 2014.

(Source: Federal Highway Administration Quarterly Report of Freight Efficiency Index, Fourth Quarter 2014.)

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