TxDOT Project 0-5410 Developing Freight Highway Corridor Performance Measure Strategies in Texas.

TxDOT Project 0-5410

Developing Freight Highway Corridor Performance Measure

Strategies in Texas

Team: Center for Transportation Research

R. Harrison, L. Loftus-Otway, J. West, M. Schofield

Texas Transportation InstituteD. Middleton, D. Shrank, T. Lomas

TxDOTL. Gregorcyk (PC), G. Malatek (PD), D. Stewart (RTI)

FHWA and ATRI

Goals of Scoping Study

1. Review literature on urban and intercity corridors

2. Evaluate potential FPMs3. Examine ATRI data to evaluate

future freight data use4. Link Intercity and Urban

Corridors5. Identify TxDOT Needs

Top Issues in Motor Carrier Industry 14 MC experts surveyed Several issues could be directly

linked to vehicle location technology

82% of motor carriers believe congestion is a serious problem

(ICF, 2003)

Relating General PM Practices to Freight

Advantages1. Communicatio

n2. Accountability3. Operational

Improvements

Current FPM Practices (New Jersey)NJ FPMs fall into 5 categories: Average Travel Time Measures – Congestion delay Private Sector Cost Measures – Fuel Costs Per

Mile, Insurance Costs Public Impact Measures – Freight-related

Accident Rates, Emissions Economic Impact Measures – Value of

Transportation Goods, Impact of Investments on Regional Economy

Transportation Industry Productivity Measures – Vehicle Miles Traveled, System Performance (by survey), Average Haul Length

NJIT has made the most progress in displaying FPMs, but is still using models, not direct data collection

Focus on Bottlenecks and Impediments While MN/DOT has made an ambitious

push towards FPM use, little on intercity corridors

Current FPM Practices (MN/DOT)

“Developmental measures”

“Emerging measures”

“Mature measures”

Five Recommendations for FPM UseData must be capable of:

1. measurement 2. capturing deficiencies 3. collection over time 4. forecasted 5. being easily understood

Performance measurement has two sides:

Planning and Operational

Data TechnologiesReal time technologies for collecting FPM

data

GPS locators with more constant updates Cellular phones Toll tags RFID on vehicles registration tags

To take full advantage of the data’s potential, it needs to be collected and processed as close to real-time as possible

FHWA Sponsored FPM Worki. Proof of Conceptii. 5 Interstate Highwaysiii. (a) Sample 7 DOTs

(b) Monitor 35 Interstate Highways

ATRI Method

a. ATA sample, using vehicles with GPS on designated routes

b. Vendor provides aggregate data to ATRI

c. ATRI puts GPS data on GISd. Output – maps and reportse. Planning data

5 Initial Corridors

I-70 Denver

Hurricane Katrina 2005

Collaboration with ATRI

Truck volumes over a 24-hour period

Few areas (and no intercity corridors) use anything other than a percentage of passenger vehicle volume

Texas Data for 2005

Typical Auto PeaksTypical Auto PeaksCAMPO 24-Hour Internal Person Trips In Motion Plot

(Diurnal Distribution based upon 1997 expanded household Survey Data)

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

0.25

1.00

1.75

2.50

3.25

4.00

4.75

5.50

6.25

7.00

7.75

8.50

9.25

10.0

010

.7511

.5012

.2513

.0013

.7514

.5015

.2516

.0016

.7517

.5018

.2519

.0019

.7520

.5021

.2522

.0022

.7523

.50

Time

Per

cen

t O

f T

rip

s

AM Peak Hr: 7:15-8:15

PM Peak Hr:4:45-5:45

2 hr1.5 hr

3 hr

4:15-6:15

3:15-6:15

7:00-8:30

Peak 15 Min:7:30-7:45

Peak 15 Min:5:00-5:15

El Paso Truck TrafficPercentage of Truck Traffic in Each Hour of the Day

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time CST

Pe

rce

nt

To

tal

Tru

ck

Tra

ffic

El Paso

Yet real truck volumes show that trucks make different time choices than cars

Houston Truck TrafficPercentage of Truck Traffic in Each Hour of the Day

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time CST

Pe

rce

nt

To

tal

Tru

ck

Tra

ffic

Houston

Shows truck travel demand in greater detail

Rural Truck Traffic

Percentage of Truck Traffic in Each Hour of the Day

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time CST

Pe

rce

nt

To

tal

Tru

ck

Tra

ffic

Rural

Overall Texas Truck TrafficPercentage of Truck Traffic in Each Hour of the Day

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Time CST

Pe

rce

nt

To

tal T

ruc

k T

raff

ic

Houston El Paso All Texas Rural

FPM with truck origin-destination data could result in the better models of truck movements

Linking Intercity and Urban Corridors

Urban Congestion Programs Mobility Monitoring Program

Began 2001 Uses archived detector data 30 urban areas

Urban Mobility Report Began 1982 Uses HPMS data 85 large urban areas

Urban Congestion Reporting Program Began 2002 10 urban areas

Mobility Monitoring Program Speed and count accuracy varies Lack of calibration and maintenance Use of spot speeds to estimate travel

time Lane-by-lane data combined into

“stations” Estimate link properties – “zone of

influence” Group freeway links with similar

adjacent links

traffic sensors collect data in each lane at 0.5-mile nominal spacing

summary statistics computed across all lanes in a given direction

link travel time &vehicle-miles of travel

link travel time &vehicle-miles of travel

point-based properties extrapolated to roadway links 0.5 to 3 miles in length

directional roadway sectiontravel time & vehicle-miles of travel

directional roadway section travel time & vehicle-miles of travel

link properties summed to analysis sections 5 to 10 miles in length

Lane-by-LaneLevel

SectionLevel

LinkLevel

StationLevel

Possible Linkages: ATRI & MMP

Different vehicles monitored

Sampling rates TTI and BI common Need verification

Pavement Management Uses

Future: Planning vs. Operational Improvements To date, nearly all FPM (even general

PM) use has focused on long-term planning

With more frequent, real-time data collection, trucks could become probe vehicles representing total traffic

Currently, urban traffic data provide information to metro users, Extended to intercity travelers, FPM will

provide trip estimates

Big-box and inland port locations may be driven by data on truck locations and volumes

Inland Port/Big Box Uses

Inland Port/Big Box Uses

As inland ports and big boxes locate along the TTC and other corridors, more precise truck travel patterns will prove invaluable in locating and managing distribution centers

Possible Inland Port Possible Inland Port Locations

Other Future Uses Reliability measures, either TTI’s Buffer Index

or a standard deviation, mapped on small highway segments over entire regions to pinpoint bottlenecks

1

)(1

2

N

AvgSpeedSpeedASR

N

ii

n

Real-time data processing allows (a) accidents to be accurately located when reported and (b) emergency routing

Border/port wait time management Other ITS uses such as real-time overhead

signing for traffic optimization.

Traffic Demand Management

As tolled highways are built in Texas, higher utility

Truck location data can provide real-time travel times on different routes

Using time values, travel times could calculate underpin road pricing

Questions?