USING DATA TO MAKE YOUR TRUCKING OR LOGISTICS COMPANY MORE EFFICIENT AND PROFITABLE FOR THE

TRAFFIC CLUB OF MEMPHIS

Dan Pallme, Director4/8/2014

Contents

Set the Stage: Memphis, TN Background Literature Review Research Objectives Case Study Descriptions Conclusions Memphis – Are you prepared?

Set the Stage: Memphis

5 class one railroads serving

one region

Set the Stage: Memphis

4th largest inland water port in

the USA

Set the Stage: Memphis

Nation’s3rd busiest trucking

corridor

Set the Stage: Memphis

2 modes moving time sensitive

freight

Set the Stage: Memphis

1 university leading the way

researching how to keep America’s business moving

Blue-Gray Spring Game – This Friday

Background

The efficient freight transportation planning is based on the existence of an accurate and comprehensive database

GPS data provide the ability to track singular vehicle movements and the corresponding trip characteristics

This information can reduce the number of assumptions and increase the accuracy of analysis

Therefore, GPS data can be extremely useful in freight planning and research

Research topics could include the development of freight performance measures, the evaluation of freight policies, etc.

1st Case Study: GPS Data for Developing FPMs

Freight movement is a significant aspect of transportation planning and economic success of a region

Therefore, it is important to develop Freight Performance Measures (FPMs)

With MAP-21, new incentives are in place for (DOTs) to integrate FPMs into transportation planning and operations

GPS data can be extremely useful in developing FPMs

With FPMs, agencies can have additional tools for more effective freight transportation planning and research

Literature Review (Freight Performance Measures)

Mallet et al., 2006 FHWA created the Freight Performance Measure Initiative in

2002, to address the lack of data on freight movements and its impact on congestion

The initiative focused on collecting travel time data for freight corridors and delay times at border crossings combined with urban congestion data

Gordon Proctor & Assoc., 2011 In 2010, the National Cooperative Freight Research Program

(NCFRP) produced a report defining PMs related to freight Analysis included the highest freight movement in the U.S. by

different modes of transportation The report focused on different FPMs such as average speeds,

travel time, link delay, miles of congested roadway, travel rate cost-per-mile, driver wages, fuel cost, number of crashes, cost of crashes, etc.

Literature Review (Freight Performance Measures)

Southworth et al., 2011 FHWA collected and published the results of the Commodity

Flow Survey taken every five years since 2007 called the Freight Analysis Framework (FAF)

Database provided detailed information on commodity tonnage by mode but did not provide enough data regarding single vehicle movements, required for many FPMs

McCormack, 2009 Collecting and processing truck GPS data to evaluate

network performance Travel time and reliability were found to be the most

common performance measures GPS Data can be very useful for public agencies. Data

limitations and cost are major concerns

Literature Review (GPS data in FPMs development)

Figliozzi et al., 2011 Processing truck GPS data to produce performance

measures Study focused on analyzing the travel time reliability of

specific corridors Data provided by ATRI

Wang et al., 2014 Suggested a methodology to estimate link travel time

using GPS data Mapping methods were found to be more efficient

compared to naïve methods

Research Objectives

Describe case studies of utilizing GPS data in freight transportation research

1st Case Study: Use GPS Data to Develop FPMs Develop truck travel demand and temporal patterns on

interstates and intermodal freight facilities

2nd Case Study: Use GPS Data to evaluate a freight policy

Evaluate the impact of new HOS rules on traffic congestion

3rd Case Study: Using real data from a Memphis West Coast Railroad

What does it mean

Case Study Area

Case study area included: a 212 miles long segment on I-40

between Memphis and Nashville, TN

major freight facilities within the borders of the greater Memphis area

I-40 between Memphis and Nashville, TN

Memphis Study Area

Dataset Characteristics

Dataset provided by American Transportation Research Institute (ATRI)

GPS database comprised of attributes such as truck routes and trip characteristics

The database included: Truck unique ID Truck location Date and time of observation Truck speed and heading

Data analyzed for a two-month period (Sep. –Oct. 2011), 3%-8% of total truck population

Data Display

Truck Travel Demand Pattern on I-40

1:00

2:00

3:00

4:00

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011

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9.00

Mon

Tues

Wed

Thurs

Fri

Hour of Day

Per

cent

age

Vol

ume

(%)

Truck Trip Time Pattern on I-40

1:00

2:00

3:00

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5:00

6:00

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011

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2

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Mon

Tues

Wed

Thurs

Fri

Hour of Day

Tri

p T

ime

(Hou

rs)

Truck Turn Time Distribution

15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300 300+0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

Public Warehouse Private Warehouse

Distribution Center Intermodal Facility

Turn Time (Minutes)

Fre

quen

cy

Facilities Turn Time Prediction Model

Regression models were developed to predict turn times using truck volumes as the predictor

Variables for model development: Y: turn-time of the facility (min) x1: volume per time interval

Three time intervals (15 min, 30 min, and 60 min) were tested for each facility

A 5-fold “Hold Out” cross validation technique was applied for selecting the most representative models

Facility Turn-Time Models

Facility Type Time Interval (min)

Model (min) R2

Intermodal Facilities

15 0.55

Distribution Centers

60 0.014

Private Warehouse 15 0.35

Public Warehouse 60 + 0 0.0001

2nd Case Study: GPS Data for Evaluating Policies

276,000 large trucks were involved in highway crashes during 2010 in the U.S. (NHTSA, 2012)

The Federal Motor Carrier Safety Administration (FMCSA), proposed new HOS rules to improve safety standards

HOS rules define the allowable driving and working hours and the required rest periods

New regulation created significant controversy regarding the potential effects on truck operations and congestion

GPS data can be used to evaluate the impact of new HOS rules on traffic congestion

Hours-of Service (HOS) Regulation

“Formerly, when a driver finished work between 1 a.m. and 5 a.m. on Saturday, he could go back to work Sunday night. Now he can’t

start until 5 a.m. Monday”Source:

http://www.truckinginfo.com/channel/fleet-management/article/story/2013/08/the-effects-of-the-new-hours-of-service.aspx

Literature Review (HOS Rules Development)

Interstate Commerce Commission (ICC), 1936 (Source: FMCSA, 2000) First attempt of regulating HOS rules Maximum 15 on-duty hours per day (12 working hours + 3 hour rest periods) Maximum 60 on-duty hours per week or 70 hours per 8 continuous days

Interstate Commerce Commission (ICC), 1962 (Source: FMCSA, 2000) 24-hour restrictions no longer exist 8-hour off duty recovery period Maximum allowable number of 10 hours continuous driving

Federal Motor Carrier Safety Administration (FMCSA), 2003 (Source: FMCSA, 2011) Maximum allowable working hours were set to 14 per 24 hours 10-hour off-duty recovery period Maximum of 11 hours continuous driving Drivers could start a new weekly working period after 34 continuous hours of

rest

Literature Review (Impact of new HOS Rules)

Blanchard, 2012 High level of rejection of new regulation among truck drivers Increased congestion during peak hours Potential increase in crash rates due to increased interaction with

passenger vehicles

SCDigest Editorial Staff, 2013 Many believe that the impact on productivity will be minor, about

1.5%, due to the limited number of long-haul drivers which the restart period is expected to have the greatest effect

American Shipper, 2013 Need for additional drivers due to new rules Driver shortage could result in higher operation costs Potential late deliveries

Impact of HOS Rules

Methodology focused on tracking the impact of new rules on congestion after identifying the change in Level of Service (LOS)

The HCM methodology (HCM, 2000) for LOS estimation was modified to

utilize GPS data

Impact of New Rules on Congestion

LOS was calculated for a 4.5 miles long highway segment on I-40 at Exit 201A

A to B

A to C

A to D

B to C

B to D

B to E

B to F

C to D

C to E

C to F

D to E

D to F

No Change0

5

10

15

20

25

30

35

40

LOS Change due to Additional Truck Demand

LOS Change

Perc

enta

ge o

f Sce

nari

os (%

)

Formulas – Estimating Flow Rate

𝑉𝑝: refers to 15-min passenger car equivalent flow rate (pc/h/ln) 𝑉: refers to hourly volume (veh/h) 𝑃𝐻𝐹: refers to peak hour factor 𝑁: refers to number of lanes 𝑓𝐻𝑉: refers to heavy vehicle adjustment factor 𝑓𝑝: refers to driver population factor

Formulas – Heavy Vehicle Adjustment Factor

TAADT: is the truck annual average daily traffic (provided by FAF database)

TPDT: is the truck percent of TAADT and refers to the corresponding daily truck traffic demand pattern

Dtr: is the directional split and refers to the directional distribution of trucks hourly volume

AADT: is the annual average daily traffic (provided by FAF database)

Dtot: is the directional split and refers to the directional distribution of total hourly volume

PDT: is the percent of hourly AADT and refers to the corresponding daily traffic demand pattern. This specifies the proportion of the total AADT which affects traffic conditions of a roadway link during a specific time period of the day

Formulas – Adjusted per hour

AADT: is the annual average daily traffic (provided by FAF database)

Dtot: is the directional split and refers to the directional distribution of total hourly

PDT: is the percent of daily traffic factor and refers to the corresponding daily traffic demand pattern

PT: is the proportion of the additional trucks which affected a specific road link during a time period of the day

CF: is an adjustment factor and corresponded to the proportion of the total truck population

AT: is the number of additional trucks which were operating between 1:00 and 5:00 a.m. on a typical Monday

3rd Case Study: Applying Real-World Data

Class 1 Western Railroad Large portion of traffic that moves to

the Nashville area Data: 6 am – 9 am on Mondays and

Fridays

• * Extreme weather event: snow & ice

• Holiday week taking out of the data

Results

10% more activity due at the gates on Monday over Friday

23% more activity in volumes on Monday over Friday

Was this due to HOS?

Conclusions on Research

GPS data can be extremely useful in freight research.

Three case studies of utilizing GPS data were presented In the first case study, truck travel demand and temporal

patterns on interstates and intermodal freight facilities were developed

The second case study focused on evaluating the impact of a freight policy (new HOS rules) on congestion

Future research Combined GPS data with commodity and trip time

information to develop a comprehensive description of freight movements by trucks in TN

Comparison analysis of data before HOS took place and now

Comparison analysis of gate activity broken down by in-bound and out-bound

State of Tennessee Philosophy

Article in the Memphis Business Journal yesterday.http://www.bizjournals.com/memphis/blog/2014/04/tennessee-transportation-funding-reflects-federal.html?ana=e_du_pub&s=article_du&ed=2014-04-07

Highlights Gov. Bill Haslam and TDOT unveiled 3 year $1.5

Billion budget 59 transportation projects 41 Counties 14 Statewide Program

TDOT will adopt a “pay-as-you-go” strategy Tennessee is only one of four states whose

transportation systems carry no debt

Future Construction around Memphis and Shelby County – That will impact travel patterns

I-40 / I-240 Interchange - $100 million http://www.tdot.state.tn.us/i40-240memphis/ Now through Summer, 2017

I-55 / Crump Boulevard Interchange Improvement - $35.7 million http://www.tdot.state.tn.us/i55/ Buying right of way Timeframe: 3 years (estimated completion 2020)

Lamar Corridor $637.9 million http://www.tdot.state.tn.us/documents/LamarAvenueCorridor_June2011.pdf Buying right of way Timeframe: 8 years: 2023 at the earliest

Southern Gateway Project $1 Billion http://www.southerngatewayproject.com Where? Timeframe: EIS should be completed next year

Recommendations

Be prepared Use data! Collaboration Off peak Pricing considerations Potential operational changes?

What great infrastructure we will have when it is done!!!!

http://www.memphis.edu/ifti

Dan Pallmedepallme@memphis.edu

Closing Thought