American
Association of State Highway and Transportation Officials
Special
Committee on Research and Innovation
FY2023
NCHRP PROBLEM STATEMENT TEMPLATE
Problem Number: 2023-B-31
Problem Title
Characterizing
economically disconnected areas in the era of Artificial Intelligence.
Background Information and
Need For Research
Transportation
equity has long been a persistent issue that transportation officials have been
trying to address for decades. While
significant efforts have been made, achieving equity is yet a persistent
challenge that may not always be easily realized. Equity in transportation
could be related to mobility and accessibility, equal balance of cost and
benefits, spatial distribution of costs and benefits, and/or creating
opportunities for travelers to make travel decisions. Problems related to these equity aspects
often result in creation of economically disconnected areas in cities around
the world and in the U.S.
Recent
advancements in transportation technologies have resulted in the ability to
collect data with unprecedented variety and amounts (Big Data) which can be
leveraged to improve mobility, safety, and the environment. Yet, whether and how such data could be best
utilized to address the equity problem remains an unanswered question. For
example, lack of transportation service in many areas, especially the suburban
and rural areas, may lead to unintended biases when analyzing the
transportation system behavior.
Advanced
data analytics and artificial intelligence (AI) have offered ample
opportunities to address data scarcity and associated equity issues. AI techniques can help to effectively extract
and understand structure and patterns underlying the data and make robust
inferences for future conditions and trends.
With these capabilities, it is envisioned that AI can help analyze
effects of previously implemented strategies to address a variety of equity
issues related to transportation safety, mobility, accessibility, affordability,
resiliency, and recreation. In essence,
this research aims to address the following key questions:
1- What data is available that can be used
to address the equity in transportation? What data gaps need to be addressed?
What traditional and untraditional sources can help close these gaps? What
methodologies and techniques should be implemented to collect these data?
2- How can these data be used to identify
economically disconnected districts? What are the characteristics
(socioeconomic, transportation system related, etc.) that should be used to
identify these districts?
3- What system-wide strategies have been
and should be implemented to close the gaps?
4- What tools could be used to empower
economically disadvantaged groups to close the equity gap (provide more
“personalized” resources) at the individual level?
5- What framework should be adopted to
inform decision makers based on lessons learned from Question 3 above? How the
public and private stakeholders could be effectively involved and collaborate
within this framework? How could this framework be resilient and adaptable to
mega-level events such as COVID-19 to ensure equitable access to diverse
opportunities?
It
is expected that the research team will leverage advanced analytical techniques
and emerging AI technologies [data fusion of structured and unstructured data,
predictive analytics, pattern recognition, etc.] to address the research
questions above. Addressing these
questions will result in an effort that will align with the goals of the AASHTO
Committee on Planning to “Promote a multi-disciplinary planning process that
encourages State DOTs to define a long-term vision; establish a framework for
system and project-level actions; integrate the perspectives of the many and
varied stakeholders across modes, sectors, and jurisdictions; and promote an
alignment between vision and program implementation”, and “Support State DOTs in the understanding and
use of models, tools, analysis techniques, methodologies and professional
development that are important in implementing a multi-modal transportation
planning process”.
Literature Search Summary
Transportation
planning decisions often have significant equity impacts. Particularly due the
emergence of new technologies and transportation services (such as transit,
automated vehicles, electric vehicles and charging stations, ride-share
services, and connected vehicle environments), transportation equity issues
have brought a significant attention to policy makers. In fact, recent studies noted that the
benefits from such consumer-facing technologies are usually not fairly
distributed among population groups, leading to equity issues. A comprehensive review of literature on the
TRID database and other databases showed that while significant efforts have
been directed to address the equity issue, such efforts were mostly directed on
the policy side. Whether other
approaches were investigated, including extracting indicative measures using
advanced data analytics and AI from past experiences, was not part of the
driving force for such policies. The
National Academies Press’s Report on the 2019 Critical Issues in Transportation
identified equity as one of twelve areas with pressing needs. Additionally, the report indicated that
artificial intelligence may offer an opportunity to “guarantee significant
future changes and benefits”. Therefore,
the targeted research herein aims to investigate how AI tools can help guide an
effective decision-making process to help address the transportation equity challenge.
Research Objective
The
objective of this research is to develop a comprehensive framework that can be
used by departments of transportation, metropolitan planning organizations and
municipalities to address transportation equity issues in their jurisdictions.
The framework should (I) provide science-driven methodologies to identify and
diagnose the causes of transportation equity issues and characterize
economically-disconnected districts in urban areas; and (II) provide a tested
process that can be used by the different stakeholders to develop comprehensive
plans to address transportation equity issues in their jurisdictions. Accomplishment of the project objective will
require working on at least the following tasks.
Task
1. Provide a comprehensive literature review on transportation-related equity
issues in urban areas and their adverse social and economic consequences. The survey is expected to provide a
categorization of the different equity issues with respect to for example,
mobility, accessibility, safety, environmental quality, and recreation. Additionally, the review should cover
transportation system-based strategies (e.g., information systems, public
transportation, toll subsiding, carpooling, shared-economy mechanisms, etc.)
that were successfully adopted to address transportation equity in urban areas.
Task
2. Develop science-driven methodologies to identify equity issues and
characterize economically disconnected districts in urban areas. The task should
identify data requirements and describe the sources and methods to collect this
data.
Task
3. Validate the methodologies developed in Task 1 by demonstrating how they can
be used to diagnose transportation equity issues and characterize economically
disconnected district in at least two different urban areas.
Task
4. Develop an AI-based process that can
be used by the stakeholders to construct a comprehensive plan that integrates a
subset of the strategies identified in Task 1 to address transportation equity
issues in their jurisdictions. The
process is expected to account for possible synergies and conflicts among these
strategies, their cost of deployment and how they contribute to equity goals
defined for each jurisdiction.
Task
5. Demonstrate the application of the
process developed in Task 4 for at least two different urban areas (testbeds)
and illustrate - using appropriate predictive modeling techniques - the
effectiveness of the integrated plans developed by this process.
Urgency and Potential Benefits
State
and local DOTs are facing increasingly challenging problems in transportation
operation, management, and safety. The
recent literature shows that AI and ML tools present unique opportunities to
address many of those complex problems and issues. AI research in transportation has advanced
recently at an unprecedented rate and will continue to shape the future of
transportation research by offering more effective solutions to unique transportation
problems in many areas. Yet, as we brace
for a new intelligence revolution wherein AI and ML will play a significant
role in decision support systems, it is not clear to State DOTs what
implications and opportunities AI will have on critical issues such as
transportation equity, fairness, diversity, policies, workforce development,
etc. This research needs statement
highlights the importance and potential of addressing these issues with AI
tools. This will ensure that State DOTs
are well prepared for and have enough understanding of the potential
implications of AI in the broad transportation domain.
Implementation Considerations
The
planning divisions of state DOTs and MPOs will likely be the direct users of
the results and products generated from this research. The goal of the project is to help state DOTs
and MPOs to adopt practical data-driven procedures to incorporate due equity
considerations into their long-range transportation planning process. The AI-based framework developed from this research
is expected to be generic and flexible to capture equity aspects at different
spatiotemporal scales to ensure transferability. This will help to facilitate
adoption at the state level by considering the availability of data and
computing resources, which may vary significantly across states. To guarantee successful implementation of the
anticipated products from this research, state DOTs will need to identify and
leverage their existing data sources. Data exchange and fusion from related
divisions within state DOTs and MPOs are anticipated. Data sources from the
private sector may be required depending on the desired level of sensitivity.
Recommended Research Funding
and Research Period
Consistent
with other recent framework development efforts under the NCHRP program, the
recommended research fund to perform this research is $650,000, including $635,
000 for accomplishing the research objectives and $15,000 for online surveys
required to collect the data required for the research. The recommended research period is 36 months.
Problem Statement Author(s):
For each author, provide their name, affiliation, email address and phone.
• Southern Methodist University, Khaled
Abdelghany, 214-768-4309, khaled@lyle.smu.edu [AED50 Member]
• University of Tennessee, Osama A.
Osman, 423-425-4398, Osama-osman@utc.edu [AED50 Member]
• Old Dominion University, Sherif
Ishak, 757-683-5572, sishak@odu.edu
[AED50 Member]
• University of Georgia, Jidong Yang,
Jidong.Yang@uga.edu [AED50 Member]
• University of Missouri, Yaw
Adu-Gyamfi, 573-882-7546, adugyamfiy@missouri.edu [AED50 Member]
• University of South Alabama, Min-Wook
Kang, 251-460-6174, mwkang@southalabama.edu [AED50 Friend]
• Virginia Transportation Research
Council, Mo Zhao, 434-293-1938, Mo.Zhao@VDOT.Virginia.gov [AED50 Member]
Potential Panel Members: For
each panel member, provide their name, affiliation, email address and phone.
Person Submitting The Problem
Statement: Name, affiliation, email address and phone.
• Julius Codjoe
• 225-767-9761
• Julius.codjoe@la.gov
• Louisiana Department of
Transportation and Development/Louisiana Transportation Research Center