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Characterization of

Drayage Truck Duty

Cycles at the Port of

Long Beach and Port

of Los Angeles

Report

FINAL

(Revised)

Prepared for:

Port of Long Beach/ Contract

HD-7188

Port of Los Angeles/ Tetra Tech

March 2011

Prepared by

Patrick Couch

Jon Leonard

TIAX LLC

1 Park Plaza

6th

Floor

Irvine, CA 92614

Tel 949-833-7130

Fax 949-833-7134

TIAX Case D0529

1

1.0 Introduction Approximately 11,000 heavy-duty diesel trucks currently perform drayage work at the Port of

Long Beach and Port of Los Angeles, moving the majority of cargo that passes through the ports.

Based on a 2009 emissions inventory of the ports, the drayage truck fleet is the second largest

source of diesel particulate matter (DPM) and largest source of NOx emissions at the combined

port complex. Increasingly tighter federal emissions regulations, accelerated fleet turnover via

the Clean Trucks Program1, and new state rules continue to reduce the contribution of diesel

drayage trucks to the overall emissions inventory at the ports. The introduction of hybrid-electric

or other advanced drive train technologies into the drayage fleet has the potential to further

reduce emissions, while also decreasing petroleum consumption and lowering operating and

maintenance (O&M) costs for fleet owners.

To successfully develop advanced vehicles for port drayage, equipment manufacturers must

understand the typical duty cycles associated with drayage service. Superficially, drayage

operations can be grouped into three categories based on the distance of the first move of the

cargo: near-dock, local, and regional operation. However, optimized vehicle system designs

will require significantly more detailed information regarding vehicle speed, engine power, and

operating time in drayage service. The goal of this project is to collect detailed duty cycle

information for drayage truck operations in near-dock, local, and regional operation. This duty

cycle information can then be provided to equipment manufacturers to help accelerate and

improve the development of advanced drayage trucks.

2.0 Project Overview Nearly 40% of all containerized goods entering the U.S. move through the Port of Long Beach

and/or Port of Los Angeles. The majority of these containers move by drayage truck to a variety

of businesses, terminals, warehouses, trans-loading facilities, and container yards in Southern

California. Once at these facilities, these goods may then be sent out for delivery to local

businesses, loaded onto rail cars, repacked into dry vans, etc. While these facilities are spread

out around Southern California, drayage operations are often grouped into three categories based

on the first-move distance2.

Near-dock Operation: This type of operation involves very short cargo moves from two to six

miles in length, generally originating at the marine terminal. Cargo moves to the Intermodal

Container Transfer Facility (ICTF), which functions as the Union Pacific rail terminal, or nearby

container yards are included within this category.

Local Operation: A high concentration of warehouses and truck terminals, as well as a major

rail yard (Hobart), exist within 20 miles of the ports. These terminals include distribution centers

in downtown Los Angeles, Compton, and Rancho Dominguez. For the purposes of the current

project, local operation is defined as cargo moves originating or terminating at the ports and

having the other end point of the move between six and twenty miles distant from the ports.

1 http://portoflosangeles.org/ctp/idx_ctp.asp

http://www.polb.com/cleantrucks 2 First-move distance represents the distance the cargo is moved from the port terminal to another facility or terminal

before the cargo is loaded or unloaded from the truck.

2

Regional Operation: At distances greater than twenty miles from the ports, large warehouse

facilities are common and may be used to transfer goods for interstate delivery. Under the

current project, regional operation is described as cargo moves between 20 and 120 miles in

length. This effectively covers drayage operations to the Mexico border to the south, Coachella

Valley to the east, and Bakersfield to the north.

This project characterizes the duty cycle of drayage trucks operating in each of the three regions

mentioned above. The typical duty cycle in these three regions differ significantly, primarily due

to differences in relative amount of steady state and transient operation (e.g., highway cruise vs.

stop-and-go).

3.0 Test Plan and Data Collection

Test Plan Overview

Driver behavior, in addition to day-to-day variations in route, workload and traffic can

significantly affect the operation of a drayage truck. To account for these variations, the project

test plan required the collection of vehicle operational data for multiple trucks over a period of

several weeks. Specifically, a minimum of three trucks were required to be data logged and

produce one week of data in each operating region, per truck. Based on conversations with the

participating licensed motor carrier (LMC), K&R Transportation, it was assumed that each

monitored truck could be dedicated to a particular operating region for a period of one week. At

the end of the one week period, the truck would be reassigned to a new operating region for

another one week test, until the three trucks had each gathered one week of data in each region of

operation. However, rapid changes in the workload of the motor carrier due to seasonal

increases in cargo flows required the LMC to alter the deployment of its drayage fleet.

Specifically, the LMC began employing each truck in all operating regions (near-dock, local, and

regional). Based on these changes, TIAX adopted the following approach to verify data was

collected in all operating regions.

Data loggers were installed in three drayage trucks identified by the participating LMC. During

the monitoring period, the LMC was expected to ensure that the trucks performed cargo moves

within each of the three operating regions. TIAX periodically retrieved and reviewed data from

the trucks to verify that data were being collected for each truck in each operating region. While

each truck changed the region in which it operated from day-to-day, or operated in several

regions during the same day, all three trucks did perform near-dock, local, and regional

operations. As data was being collected in each region, it was determined to be sufficient to

collect data from these three trucks over a period of at least three weeks. In total, ten weeks of

data were collected; two trucks recording three weeks of data each while a third truck recorded

four weeks of data.

Operating Parameters

Duty cycle information is typically represented by plotting vehicle speed versus time on a

second-by-second basis. However, several other parameters are used to construct typical duty

cycles from the numerous trips recorded during the data collection effort. In addition, it is

anticipated that information related to engine load, fuel use, and engine revolutions per minute

3

(RPM) may be of significant benefit to a vehicle manufacturer during the design and

optimization phases of product development. Therefore, the parameters given in Table 1 were

collected to provide a more robust data set. In addition to the parameters recorded in Table 1,

the vehicle specific information shown in Table 2 was also recorded.

Table 1. Logged Parameters Parameter Units

Speed MPH

Distance miles

Position latitude/longitude

Idle Time seconds

Operating Time seconds

Acceleration MPH/second

Engine Load horsepower

Idle Fuel Use gallons per hour

Fuel Use gallons per hour

Throttle Position %

Engine Speed RPM

Table 2. Vehicle-specific Data

Parameter Units

GVWR lbs

Engine Power HP

Truck Make None

Truck Model None

VIN None

Engine Make None

Engine Model None

Transmission Type Auto/Manual

Number of Gears None

Data Collection Equipment and Operation

Under a previous demonstration program, the Port of Long Beach purchased a J19393-capable

AVIT4 data logger with analog inputs and the associated DAWN software package from HEM

Data5. This logger has the ability to collect data directly from a drayage trucks engine control

unit (ECU). In addition, the logger can collect GPS data and analog signals; providing a

complete data logging package. Based on the need to collect data from multiple trucks, the Port

of Los Angeles purchased two additional, identical AVIT data loggers for this project.

Because the data collection system was installed on trucks performing actual drayage work for a

period of several weeks, a series of custom cables and interfaces were created to allow the

equipment to be placed out of the way of the drivers. Figure 1 depicts the location of the key

components of the data collection system identified in Table 3 below.

3 J1939 is part of a set of standards defining communication of vehicle data over the on-board data bus.

4 http://www.drewtech.com/products/avit.html

5 http://hemdata.com/products/dawn

4

Table 3. Key components of the data collection system

Equipment Manufacturer Model Purpose

Data logger HEM Data AVIT Records data from the vehicle controller and other sensors (GPS, accelerometer, ignition

GPS receiver Garmin 16X HVS Reports the location, speed and direction of the truck

Ignition Sense Interface

Custom assembly using modified ATC fuse

None Monitors the state of the ignition switch for use in determining key-on/key-off events

J1939 Interface HEM Data None Interfaces data logger to the vehicle controller

Accelerometer Custom assembly using Analog Devices sensor

ADXL335 Monitors vehicle incline and accelerations

Figure 1. Location of key components of the data collection system (Image of cab courtesy of Freightliner)

The data collection system described above simultaneously collected data from the truck ECU,

GPS receiver, and accelerometer. Data were recorded at 1 Hz (one sample per second), while

the truck ignition was in the run or on position. When the truck was turned off, the logger

saved the current data file and entered a low-power sleep mode. This resulted in the recording of

a new file for each key-on event. Data were saved to a CompactFlash memory card internal to

each logger. Files saved to the internal memory card were then transferred to a computer for

further analysis using an Ethernet connection. This allowed TIAX to periodically collect data

stored on the loggers without removing any equipment from the truck.

5

4.0 Data Analysis

Approaches to development of duty cycles have varied over the years, depending on the intended

use and other factors like equipment type and available data. Under this test effort, TIAX

selected a modal analysis approach similar to that used to create the Heavy-Duty Diesel Truck

Test Schedule6, albeit without the use of microtrips. The approach for the current test effort

consisted of the following key steps. These steps and their associated terminology and concepts

are discussed in greater detail below.

1. Segment vehicle operating data into trips, where trips are defined as the period between key-on and key-off events.

2. Characterize each trip using several statistical measures, including average speed, distance, and time.

3. Identify common modes of vehicle operation and associate each trip with a mode of operation.

4. Determine the statistical average profile for each operating mode. Then select actual trip data for each mode of operation that are statistically the most representative of the

average data set for each mode.

5. Assemble modal trip data (creep, low speed transient, etc.) to create a duty cycle (speed v. time plot) that represents a cargo move for each region of operation identified in

Section 2.

Segmentation of Trip Data The segmentation of data into trips as described in step 1, above,

was accomplished by the operation of the data collection system. Because the system saves

discrete files every time the truck ignition is turned off, each file represents a trip and no further

separation is required.

Characterization of Trips - Due to the number of trips and amount of data recorded

(approximately 1,258 trips and 1.65 million data points in total), data were loaded into a SQL

database for processing. The database calculates ten statistical measures for each trip, as shown

in Table 4 below. All parameters other than total duration, non-idle duration, and percentage of

time at idle were calculated after removing data points with zero vehicle speed (idling).

Table 4. Statistical Parameters Calculated for each Trip.

Trip Parameter Units

Average Vehicle Speed MPH

Maximum Vehicle Speed MPH

Total Duration Seconds

Non-idle Duration Seconds

Percentage of Time at Idle %

Total Engine Output Energy HP-hr

Average Engine Speed RPM

Number of Stops None

Total Distance Miles

Engine Output Energy per Mile HP-hr/mile

6 Gautam et. al. Development and Initial Use of a Heavy-Duty Diesel Truck Test Schedule for Emissions

Characterization, SAE Technical Paper 2002-01-1753, 2002

6

Identification of Common Operating Modes A mode of operation is a characterized by

certain driving behaviors, usually as a result of similar driving conditions. For example, Creep

mode is typically considered to be associated with vehicle operation in queue lanes. This mode

is typified by long periods of idle, interrupted by brief accelerations and decelerations as the

truck moves forward in a queue lane. The most common modes of operation and their

characteristics vary between vocations; e.g. common operating modes in a vocation like long-

haul trucking are likely to be different from the common modes of operation in drayage.

Therefore, the trip data must be analyzed to identify modes of operation specific to drayage

operation and each trip associated with a particular mode of operation.

In this testing effort, trip modes were identified by comparing the various statistical parameters

identified in Table 4, above. After comparing the relationship of each parameter, it was

determined that plotting maximum speed versus average speed for each trip tended to separate

trips into one of four regions, as shown in Figure 2. Each region was then associated with a

particular mode of operation and named based on the type of operation the mode appears to

represent. The threshold speeds indicated were determined by identifying the natural breaks in

the data using engineering judgment.

Creep Very low speed operation, typical of operation in truck queues.

Low Speed Transient Low speed operation, typical of on-dock movement.

High Speed Transient Operation that achieves high peak speeds but does not sustain these

speeds. This operation is typical of travel on regional roads, driving in traffic or brief travel on

freeways.

High Speed Cruise High speed operation with sustained high speeds, typical of travel on

freeways.

7

Figure 2. Visualizing the relationship between parameters to identify common operating modes.

Using the metrics of average vehicle speed and maximum trip speed, as described above, each

trip was associated with a particular mode of operation. Figure 3 shows the distribution of trips

by their associated mode. A subset of trips, those beginning within port boundaries, was then

examined. It was determined that the vast majority of Creep and Low Speed Transient data

represented queuing or on-dock movements (see Figure 4). Therefore, all cargo moves that

represented near-dock, local, or regional drayage operation must be High Speed Transient or

High Speed Cruise mode trips. Further, nearly all trips greater than 20 miles in length were

identified as High Speed Cruise trips. This meant that while most operating modes could be

associated with a particular region of operation, the High Speed Transient mode represented trips

from two to twenty miles in length; essentially spanning the near-dock and local haul regions of

operation. In order to better characterize near-dock and local haul operations, the High Speed

Transient trips were further segmented by trip distance, creating two distinct modes of operation;

Short High Speed Transient and Long High Speed Transient. These two modes represent High

Speed Transient trips of less than six miles and greater than six miles in length, respectively.

8

176

342

107 103

182

0

50

100

150

200

250

300

350

400

Creep Low Speed Transient High Speed Transient High Speed Cruise

Mode

Nu

mb

er

of

Tri

ps

Long High

Speed Transient

Short High

Speed Transient

Figure 3. Distribution of trips by associated mode of operation

Figure 4. Locations of Creep and Low Speed Transient operation.

9

Determining Average Profiles and Selecting Typical Trips Once each trip is identified as

belonging to a particular mode of operation, an average profile of each mode is calculated. This

profile is simply a list of the average value of each parameter (e.g., distance, engine speed, etc).

This is done by calculating the average of the parameter for all trips in a particular mode. For

example, the average distance of High Speed Cruise operation was determined by averaging the

distances of all trips identified as High Speed Cruise trips. Table 5 summarizes the average

value of key parameters for each operating mode.

Table 5. Average Values for Key Trip Parameters by Mode of Operation

Mode Units Creep Low Speed Transient

Short High Speed

Transient

Long High Speed

Transient

High Speed Cruise

Avg. Vehicle Speed MPH 2.7 7.6 17.1 18.7 37.9

Max. Vehicle Speed MPH 4.8 16.5 41.3 47.7 58.6

Non-idle Duration seconds 44 268 890 2,117 4,767

PercentageTime at Idle

none 66% 40% 29% 27% 13%

Stops none 3.2 8.5 16.2 29 22.7

Distance miles 0.034 0.58 4.2 11.3 50.6

Energy per Mile HP-hr/mile 8.4 4.8 3.7 3.8 3.9

Having defined the average parameters for each mode of operation, trips may be ranked by how

closely they match the mode averages. Because each mode is described by several parameters, a

weighted mean system error was calculated for each trip as compared to the mode averages.

Mean system error (MSE) is described by the following equations:

i

n i

iiw

p

ppMSE

=)(

i

i

i

ipp

w max

=

A small mean system error indicates that the selected trip is close to the average profile of the

mode. While there is no limit on the number of parameters that can be used to create the mode

profiles, judicious selection of parameters is still important. First, because the MSE sums the

errors of each parameter from the mode average, increasing the number of parameters in the

profile will generally increase the MSE. Second, using parameters that duplicate certain physical

measures of the profile (e.g. Percentage of Idle, Total Duration, and Non-Idle Time) will result in

overly weighting the impact of these physical measures. Therefore, it is best to select a few

parameters that represent different physical characteristics of the vehicle operation. Based on

weightingparametertheisw

parametersofnumbertheisn

parametertheofvaluetriptheisp

valueaverageparametertheisp

where

i

i

i

:

10

these considerations, MSE was calculated for each trip using Average Vehicle Speed, Maximum

Vehicle Speed, Energy per Mile, Distance, and Stops. The trip with the smallest MSE in each

mode of operation was then selected at the most representative trip for that mode of operation.

Table 6 summarizes the average profiles for each trip mode and the profile of the trip that best

fits each mode. The relative percentage of the Best Fit Trip parameters compared to the mode

profile parameters is presented graphically in Figure 5.

Table 6. Average Mode Profiles and Best Fit Trip Statistics for each Operating Mode.

Mode

Avgerage Speed (MPH)

Maximum Speed (MPH)

Energy per Mile

(HP-hr/mile)

Distance (miles)

Stops

Creep 2.65 4.81 8.41 0.03 3.24

Best Fit Trip 2.5 4.9 9.1 0.03 3

percentage of mode profile parameter

96% 102% 109% 84% 92%

Low Speed Transient 7.64 16.47 4.79 0.58 8.54

Best Fit Trip 6.7 17 4.9 0.59 10


88% 103% 102% 102% 117%

Short High Speed Transient 17.09 41.33 3.75 4.18 16.18

Best Fit Trip 15.3 40.6 3.7 4.99 16


90% 98% 99% 119% 99%

Long High Speed Transient 18.74 47.67 3.83 11.29 29.00

Best Fit Trip 19.6 46.5 4.2 8.09 27


105% 98% 110% 72% 93%

High Speed Cruise 37.93 58.59 3.91 50.59 22.71

Best Fit Trip 38.9 58.5 3.5 48.40 19


102% 100% 89% 96% 84%

11

0%

100%

Average Speed

Maximum Speed

Energy per MileDistance

Stops

Perfect Fit

Creep

Low Speed Transient

Short High Speed Transient

Long High Speed Transient

High Speed Cruise

Figure 5. Comparison of "best fit" trip parameters by mode

Assemble Modal Data In determining the typical duty cycle for drayage operations, it is

important to recognize that a drayage move is distinct from a trip in this analysis. A trip is a

single block of truck activity between key-on and key-off events. A complete drayage move

may, and usually does, involve more than one trip (key-on/key-off event). Conceptually, a

drayage move can be separated into 1) on-dock activity followed by 2) transport of the cargo to

an off-dock location, a cargo transport trip. As described previously in this report, an analysis

of trips originating within port boundaries shows that nearly all creep and low-speed transient

trips are confined to on-dock movement. They are also predominantly less than two miles in

length. Figure 6 summarizes the distribution of trips by length and mode; as shown, each region

of operation is dominated by a single mode of operation. Specifically, near-dock, local, and

regional operations are predominately Short High Speed Transient, Long High Speed Transient,

and High Speed Cruise modes respectively. Therefore, when constructing a typical duty cycle

for a drayage move, the duty cycle should consist of some Creep and Low Speed Transient

operation prior to the High Speed Transient or Cruise operation that is responsible for the

majority of the distance of the cargo move.

12

Cre

ep

Lo

w S

peed

Tra

nsie

nt

Sh

ort

Hig

h S

pee

d T

ran

sie

nt

Lo

ng

Hig

h S

peed

Tra

nsie

nt

Hig

h S

peed

Cru

ise

0%

20%

40%

60%

80%

100%

0 to 0.1 0.1 to 2 2 to 6 6 to 20 20+

Trip Length (miles)

% o

f T

rip

s o

f In

dic

ate

d L

en

gth

Creep Low Speed Transient Short High Speed Transient

Long High Speed Transient High Speed Cruise

Figure 6. Distribution of modes of operation by trip length for trips originating within port boundaries.

Table 7 provides the number of trips by mode, for trips originating within port boundaries. As

shown, there is approximately 1.2 Low Speed Transient trips for each cargo transport trip. This

implies that, on average, each trip of two miles or greater in length that originates from the ports

is preceded by a Low Speed Transient trip. Similarly, approximately 0.6 Creep trips precede a

cargo transport trip. Note that, in practice, it is not possible to have a fraction of a trip.

Therefore, when assembling modal data, an entire Creep and Low Speed Transient trips should

be incorporated into the duty cycle. Figure 7 illustrates the precedence of a High Speed Cruise

trip by Creep and Low Speed Transient trips.

Table 7. Number of Trips Originating within Port Boundaries by Mode

On-dock Maneuvers Cargo Transport

Mode Creep Low Speed Transient

Short High Speed

Transient

Long High Speed

Transient

High Speed Cruise

76 58 30 Number of Trips 120 203

Total Cargo Transport trips: 164

On-dock Trip/ Cargo Transport Trip

0.7 1.2

13

Figure 7. High Speed Cruise trip preceded by a Low Speed Transient trip.

14

5.0 Results

Using the data analysis method described above, three duty cycles are constructed. Each duty

cycle consists of a high speed mode (either Transient or Cruise) preceded by one Creep-mode

and one Low Speed Transient mode trip. In all cases, the same Creep and Low Speed Transient

trips are used and these are the best-fit trips identified previously. Based on the distribution of

trips by trip length and mode, as shown in Figure 6, duty cycles are constructed using the

following best-fit trip data:

1. Near-dock (2 to 6 miles) Creep Low Speed Transient Short High Speed Transient 2. Local (6 to 20 miles) - Creep Low Speed Transient Long High Speed Transient 3. Regional (20+ miles) - Creep Low Speed Transient High Speed Cruise

Figure 10, Figure 11, and Figure 12 depict the resulting duty cycles for near-dock, local, and

regional operations respectively. Table 8 summarizes key statistics for each of the synthesized

duty cycles. In addition to the statistics for the synthesized duty cycles, statistics for modes of

operation are provided. Note that Table 6 provides average values for each mode of operation as

well as the best fit trip data with idle data filtered out. While this information is useful to

identify best-fit trip data, idle is an inherent part of drayage operation and should be included in

aggregate statistics. Table 9 provides statistical measures for all trips in each mode, as well as

the data set as a whole.

Table 8. Duty cycle statistics

Statistic Units Near Dock Local Regional

Duration seconds 3,049 4,643 6,167

Average Speed MPH 6.6 6.8 28.6

Maximum Speed MPH 40.6 46.5 58.5

Distance miles 5.61 8.71 49.02

Stops 30 40 34

Miles/Stop 0.19 0.22 1.44

Max. Acceleration MPH/sec 4.45 3.45 6.65

Max. Deceleration MPH/sec -5.35 -4.10 -7.30

% Idle % 50% 60% 22%

Port Bridge Crossings

Three key bridges within the port complex are heavily utilized by drayage trucks. These are the

Gerald Desmond, Heim, and Vincent Thomas bridges. Each bridge has approach grades of 5-

6.5% that vary in overall length. The Gerald Desmond Bridge has been identified as having a

particularly challenging combination of approach grade and length when travelling eastbound.

Therefore, an analysis of vehicle speed and horsepower data was conducted for eastbound

transits across the Gerald Desmond Bridge to serve as a high-end estimate of the engine power

typically used to transit the port bridges. Figure 8 shows an example of the engine horsepower

15

versus time for a truck making an eastbound crossing of the Gerald Desmond Bridge. The figure

clearly shows the high power demand required as the truck climbs the bridge. The power

demand then rapidly decreases to zero as the truck crests the bridge and travels downhill. A

typical transit across the bridge takes approximately 60 seconds, with more than half the time

associated with climbing the bridge.

Figure 9 provides a distribution of the recorded engine power data for all data points associated

with eastbound crossings of the Gerald Desmond Bridge where the truck was travelling at greater

than 20 miles per hour. Data were filtered based on vehicle speed to remove data points

associated truck activity near, but not on, the bridge. For example, trucks operating on Pier T, D,

and E that may pass under the bridge will register GPS locations that appear to be on the bridge.

0

100

200

300

400

500

0 10 20 30 40 50 60 70

Time (seconds)

En

gin

e P

ow

er

(HP

)

Figure 8. Example of engine power during an eastbound crossing of the Gerald Desmond Bridge.

0%

5%

10%

15%

20%

25%

100 150 200 250 300 350 400 450 500 More

Engine Power (HP)

% o

f D

ata

Po

ints

Figure 9. Distribution of engine power data for eastbound crossings of the Gerald Desmond Bridge.

16

Usage of Duty Cycles in Dynamometer Testing

One anticipated use of the duty cycles presented in this report is the production of emissions and

fuel economy estimates for advanced technology vehicles through dynamometer testing. While

the data used to create the duty cycles are real world truck activity data, there are several

potential issues to consider when attempting to replicate these duty cycles in dynamometer tests.

1. Vehicle weight The combined weight of the truck, container chassis, and container, known as the gross combined weight (GCW), was not available in the data collected

during the testing. Therefore, the actual GCW for each mode of operation cannot be

determined. It is recommended that an average GCW be estimated for dynamometer

testing. Most drayage trucks are Class 8 vehicles registered to haul a GCW of 80,000 lbs.

However, due to a variety of issues with loading a truck safely to its maximum weight,

most drayage trucks are loaded to less than 80,000 lbs. The Federal Highway

Administration estimates that a typical 5-axle semi-truck is loaded to approximately

65,000 lbs GCW7. Therefore, it is recommended that a test weight of 65,000 lbs be used

as an average loaded weight. A weight of 80,000 lbs should be used as a maximum test

weight.

2. Trace gradients As previously mentioned, the vehicle speed data used to construct the duty cycles are real world data. Therefore it is not beyond the capability of a test truck to

replicate the vehicle speed traces (vehicle speed vs. time charts in the figures below) on a

dynamometer. In practice however, it can be very difficult to replicate sections of the

speed trace that change rapidly (high gradient regions). In part, this is simply a limitation

of the human drivers ability to follow a trace with such rapid changes in vehicle speed.

Additionally, rapid accelerations and decelerations can be difficult or impossible to

reproduce on many dynamometers because they may damage the dynamometer. The

duty cycles presented in this report may need to be filtered or smoothed to reduce trace

gradients to acceptable levels for the dynamometer facility conducting the testing.

3. Test duration The duty cycles presented in this report range in duration from 2,300 to 7,200 seconds. While this is representative of actual drayage operation, long tests

increase the chance for dynamometer testing errors due to driver fatigue and greater

aggregation of emissions data from small deviations from the test cycle. Somewhat more

practical duty cycles may be created by reducing periods of significant idle and, in the

case of the Regional Haul duty cycle, reducing the duration of the cruise portion of the

test.

7 Table III-4, Comprehensive Truck Size and Weight Study, 2000. Federal Highway Administration.

17

6.0 Recommendations

The Port of Long Beach and Port of Los Angeles continue to support the development and

deployment of clean, advanced drayage trucks through their Technology Advancement Program.

Based on the ports joint goal to support the development of these advanced vehicles, TIAX

makes the following recommendations:

1. Make available the results of this report, including the detailed drive cycle data, to interested developers of low emission vehicle technologies.

2. Produce useable dynamometer test cycles for near-dock, local, and regional drayage operations by:

a. Performing a statistical analysis of existing chassis test cycles to identify any test cycles that may be sufficiently similar to the five modes of operation identified in

this report, that the existing test cycles could be used in place of the real world

data presented here.

b. Filter and scale the real-world duty cycle data to produce dynamometer test cycles that comply with the limitations of current chassis dynamometer laboratories.

c. Examine average loaded and empty cargo container weights to determine reasonable dynamometer test weights.

Best Match - Creep

idTime(seconds)Vehicle Speed(MPH)

104010400.00

104010510.00

104010620.00

104010730.00

104010840.00

104010950.00

104011060.00

104011170.00

104011280.00

104011390.00

1040114100.00

1040115110.00

1040116120.00

1040117130.00

1040118140.00

1040119150.00

1040120160.00

1040121170.00

1040122180.00

1040123190.00

1040124200.00

1040125210.00

1040126220.00

1040127230.00

1040128240.00

1040129250.70

1040130261.80

1040131271.90

1040132281.70

1040133291.80

1040134301.80

1040135311.80

1040136321.80

1040137331.80

1040138341.80

1040139351.80

1040140361.80

1040141371.80

1040142381.80

1040143391.20

1040144400.00

1040145410.00

1040146420.00

1040147430.00

1040148440.00

1040149450.00

1040150460.00

1040151470.00

1040152480.00

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15967417980.00

Best Match - Short HST


57804200.00

57804310.00

57804420.00

57804530.00

57804640.00

57804750.00

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57804970.00

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579522148030.50

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579525148330.00

579526148428.90

579527148529.80

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