1 | EAR PROGRAM—RESEARCH ASSOCIATES PROGRAM

EAR Program—Research Associates Program 

Leveraging Big Data for Enhanced Pavement Management

ASSOCIATE: Raphael Isokpehi

SCHOOL: University of Lagos, Nigeria

PERIOD: August 2021–August 2022

OBJECTIVE: Apply big data techniques to pavement management.

ADVISER: Nadarajah Sivaneswaran, FHWA Office of Infrastructure Research and Development

SUMMARY: Pavement management, a significant 

component of highway infrastructure asset management, 

involves using data, methodologies, and procedures to plan 

maintenance, preservation, rehabilitation, and renewal 

actions. This work relies on different types of data:

• Inventory data: The physical elements of a road system. 

• Pavement construction history data: The pavement 

structure and layer composition. 

• Condition data: The condition of elements that can be 

expected to change over time.

• Traffic and climate data: The traffic and environmental 

loading pavement is subjected to over time. 

• Cost data: Measures historical investment used for future 

cost estimates.

A wide range of rapidly advancing technologies is now 

available to highway engineers for measuring attributes of 

a pavement network. Many of these technologies 

effectively collect massive amounts of data with little or no 

impact on highway users, such as the automated 

pavement condition survey vehicle (which collects 

pavement profile and image data for surface condition 

evaluation); the ground-penetrating radar (which collects 

pavement thickness-related data); and the traffic speed 

deflection device (which collects pavement deflection data 

for structural evaluation). There is also increased 

availability of auxiliary data sources, including:

• Modern-Era Retrospective Analysis for Research and 

Applications (MERRA) and MERRA-2 climate data from 

the National Aeronautics and Space Administration’s 

Global Modeling and Assimilation Office. 

• U.S. Department of Agriculture’s Natural Resources 

Conservation Service soil survey database. 

• Connected and automated vehicle data. 

• Crowdsourced data. 

However, pavement management applications have not 

been able to fully utilize this rapidly expanding amount and 

type of “big data.” Traditional analysis procedures used in 

pavement management are not readily adaptable to the five 

v’s of big data—volume, velocity, variety, veracity, and value. 

This issue is not unique to pavement management. Recent 

advances in the field of data science, like the availability of 

open-source tools implementing artificial intelligence 

techniques such as deep learning (DL) and advances in 

data storage and processing power, provide an opportunity 

to complement proven traditional analysis approaches with 

data science and DL techniques to better leverage big data 

for more informed pavement management.

In his 12-mo Research Associateship Program tenure, 

Raphael Isokpehi, Ph.D., focused on applying big data 

techniques to pavement management. In his project, titled 

“Leveraging Big Data for Enhanced Pavement Management,” 

Isokpehi considered:

• Diverse data types for pavement management.

• Wide range of technologies for measuring attributes 

of a pavement network.

• Effective collection of massive amounts of data 

at traffic speed.

• Auxiliary data’s increased availability. 

The common themes in the project were big data to 

knowledge, knowledge sharing, knowledge 

management, and knowledge visualization. After the 

first 6 mo of the project, the following three research 

objectives were established:

• Objective 1: Construct integrated datasets for pavement 

management from diverse data sources, including data 

collected for pavement management and other readily 

available auxiliary data.
EAR PROGRAM—RESEARCH ASSOCIATES PROGRAM | 2

EAR Program—Research Associates Program

• Objective 2: Design interactive analytics for geospatial 

data investigations and DL of pavement conditions and 

performance predictions.

• Objective 3: Evaluate the benefits of interactive knowledge 

visualizations to stakeholders in pavement management.

The project’s primary data source was a series of datasets 

collected by a traffic speed deflectometer (TSD) on road 

sections from 17 State highway agencies and the National 

Park Service. A version of the constructed TSD data 

contained 180 million data points providing pavement big 

data for diverse investigations (objective 1). Another 

constructed dataset was the geographic coordinates 

(longitude and latitude) for the road sections with TSD data 

and the 2,581 sections of the Long-Term Pavement 

Performance program. 

Additionally, Isokpehi obtained datasets on oil and gas wells 

in Kansas and Pennsylvania. These auxiliary datasets 

provided examples to design interactive analytics for 

investigating the geospatial data of pavement conditions 

(objective 2). He developed several interactive analytics to 

support decisionmaking, learning, planning, and other 

complex cognitive activities. 

A Guideline for Leveraging Big Data for Enhanced Pavement Management

Author: Raphael D. Isokpehi, Ph.D.

© Raphael Isokpehi. 

Isokephi and his team engaged and collaborated with 

pavement management experts through participation in 

conferences (objective 3). The research project collaborated 

with other pavement engineers and researchers in the 

public and private sector as well as academia through the 

Transportation Pooled Fund study “Pavement Structural 

Evaluation with Traffic Speed Deflection Devices” and the Big 

Data for Smart Pavement Management and Deflection at 

Road Traffic Speed User Group. 

RESOURCES:

Isokpehi, R., A. Nadkarni, and N. Sivaneswaran. 2022. “Big 

Data Investigations to Knowledge for Enhanced Pavement 

Management.” Presented at the 11th International 

Conference on Managing Pavement Assets. Chicago, IL: 

Transportation Research Board.

Isokpehi, R., A. Nadkarni, and N. Sivaneswaran. 2022. “Big 

Data Investigations to Knowledge for Enhanced Pavement 

Management.” Presented at the 101st Annual Meeting of 

the Transportation Research Board. Washington, DC: 

Transportation Research Board.

Publication No.: FHWA-HRT-23-032

HRTM-30/01-23(Web)E

https://doi.org/10.21949/1521978