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Project Details
STATUS

Completed

START DATE

10/01/14

END DATE

06/29/18

RESEARCH CENTERS InTrans, MTC
SPONSORS

Midwest Transportation Center
USDOT/OST-R
Wichita State University

Researchers
Principal Investigator
Pingfeng Wang

MTC Lead, Wichita State University

Co-Principal Investigator
Janet Twomey

About the research

This study explores the gap between quantitative and qualitative assessment of engineering resilience in the domain of complex transportation infrastructure systems. A conceptual framework was developed for modeling engineering resilience, and then a Bayesian network was employed as a quantitative tool for the assessment and analysis of engineering resilience. A case study involving a transportation system for an aircraft manufacturing supply chain was employed to demonstrate the developed research and tools. The developed resilience quantification and analysis approach using Bayesian networks could empower system designers to have a better grasp of the weaknesses and strengths of their own systems against system disruptions induced by adverse failure events.


Funding Sources:
Midwest Transportation Center
USDOT/OST-R ($50,000.00)
Wichita State University ($50,000.00)
Total: $100,000.00

Contract Number: DTRT13-G-UTC37

Project Details
STATUS

Completed

START DATE

10/01/14

END DATE

06/29/18

RESEARCH CENTERS InTrans, MTC
SPONSORS

Midwest Transportation Center
USDOT/OST-R
Wichita State University

Researchers
Principal Investigator
Pingfeng Wang

MTC Lead, Wichita State University

Co-Principal Investigator
Krishna Krishnan

About the research

The objectives of this research were to conduct theoretical and experimental investigations to develop a new battery health management paradigm based on a novel, self-cognizant dynamic system (SCDS) approach to predict and prevent failures of safety-critical battery systems (e.g., lithium plating and thermal runaway) for electric vehicles (EVs) and hybrid electric vehicles (HEVs) and develop an onboard diagnostics tool and alarm system for early awareness of these potential impending failures.

This research developed a technique that can adaptively recognize the dynamic characteristics of an operating battery system over time without relying on expensive, time-consuming battery tests for the prediction and prevention of safety-critical battery system failures. Battery failure prognostics employing the proposed SCDS-based health management paradigm can not only account for normal battery capacity fading over time but also identify abnormal safety-critical failures that usually happen in a relatively shorter time period.


Funding Sources:
Midwest Transportation Center
USDOT/OST-R ($49,000.00)
Wichita State University ($49,000.00)
Total: $98,000.00

Contract Number: DTRT13-G-UTC37

Project Details
STATUS

Completed

START DATE

11/01/15

END DATE

06/29/18

SPONSORS

Kansas Department of Transportation
Midwest Transportation Center
USDOT/OST-R
Wichita State University

Researchers
Principal Investigator
Pingfeng Wang

MTC Lead, Wichita State University

About the research

The researchers at Wichita State University collaborated with the Kansas Department of Transportation’s Traffic Management Center in Wichita, Kansas, to develop an Intelligent Highway Management System (IHMS). The functions of the IHMS were to conveniently extract specific incident-relevant record data from high-dimensional, high-volume time series datasets; autonomously analyze online traffic-related data (e.g., volume and speed) for incident diagnosis/identification; and create autonomous optimization that facilitates traffic control decision making to reduce average incident clearance and traffic recovery time.

The IHMS integrates multiple technologies to improve traffic flow and safety. It also streamlines vehicular operations by managing congested traffic, which has become a major problem, as it leads to issues in safety, productivity, and environmental performance. In this study, the researchers developed a transportation system simulation methodology that could be used to reduce traffic congestion, as well as restore traffic to its normal conditions, by allowing vehicles to reroute and avoid congested roads, in turn dipping the speed profile for a faster and quicker recovery. The created simulation system was customized for the City of Wichita and implemented in Simulation of Urban Mobility (SUMO). Simulation results indicate that this approach reduces traffic congestion, provides for quicker incident recovery, and is a solution to ongoing safety, productivity, and environmental performance issues.


Funding Sources: 
Kansas Department of Transportation
Midwest Transportation Center
USDOT/OST-R ($20,000.00)
Wichita State University ($20,000.00)
Total: $40,000.00

Contract Number: DTRT13-G-UTC37

Project Details
STATUS

In-Progress

START DATE

10/01/15

END DATE

09/30/17

RESEARCH CENTERS InTrans
SPONSORS

Kansas Department of Transportation
Midwest Transportation Center
USDOT/OST-R
Wichita State University

Researchers
Principal Investigator
Pingfeng Wang

MTC Lead, Wichita State University

About the research

The objective of this project is to develop a Highway Incident Management System (HIMS), through collaboration with the Kansas Department of Transportation (KDOT) Traffic Management Center (TMC) in Wichita, Kansas. The anticipated functions of the HIMS are to 1) convenient extraction of specific incident-relevant record data from high-dimensional, high-volume time-series datasets, (2) autonomous analysis of online traffic-related data (e.g., volume and speed) for incident diagnosis/identification, and (3) autonomous optimization that facilitates traffic control decision making, to reduce average incident clearance and traffic recovery time. In this investigation, a total of 182 actively logged incidents, together with the traffic information from multiple online monitoring facility units during the month of April 2015 in Wichita will be used to facilitate the model and technology development.

The outcomes of this research will be the following:

(1)     Analysis results of the online traffic data, for the development of related computational models for modeling the highway incident clearance and recovery times

(2)     A technical tool to analyze the online traffic related data for highway incident diagnosis/identification

(3)     A model with technical tools to facilitate traffic control decision making that can help reduce the average incident clearance and traffic recovery time

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