Education

Research Description

Dr. Williams is interested in intelligent transportation systems, travel time reliability, real-time control system optimization, transportation network simulation, applied statistics and time series analysis in transportation, and traffic flow theory. Funding for Dr. Williams' research program has come from a variety of sources including NSF, NCHRP, SHRP2, and the North Carolina Department of Transportation.

Publications

Impacts of Private Autonomous and Connected Vehicles on Transportation Network Demand in the Triangle Region, North Carolina

Hasnat, M. M., Bardaka, E., Samandar, M. S., Rouphail, N., List, G., & Williams, B. (2021), JOURNAL OF URBAN PLANNING AND DEVELOPMENT. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000649

Investigating the relationship between freeway rear-end crash rates and macroscopically modeled reaction time

Ahmed, I., Williams, B. M., Samandar, M. S., & Chun, G. (2021), TRANSPORTMETRICA A-TRANSPORT SCIENCE. https://doi.org/10.1080/23249935.2021.1914769

Crash Classification by Congestion Type for Highways

Song, T.-J., Kim, S., Williams, B. M., Rouphail, N. M., & List, G. F. (2020), APPLIED SCIENCES-BASEL, 10(7). https://doi.org/10.3390/app10072583

Application of a Discontinuous Form of Macroscopic Gazis-Herman-Rothery Model to Steady-State Freeway Traffic Stream Observations

Ahmed, I., Williams, B. M., & Samandar, M. S. (2018), TRANSPORTATION RESEARCH RECORD, 2672(20), 51–62. https://doi.org/10.1177/0361198118799166

Data-driven approach for identifying spatiotemporally recurrent bottlenecks

Song, T.-J., Williams, B. M., & Rouphail, N. M. (2018), IET INTELLIGENT TRANSPORT SYSTEMS, 12(8), 756–764. https://doi.org/10.1049/iet-its.2017.0284

Real-Time Prediction of Seasonal Heteroscedasticity in Vehicular Traffic Flow Series

Huang, W., Jia, W., Guo, J., Williams, B. M., Shi, G., Wei, Y., & Cao, J. (2018), IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 19(10), 3170–3180. https://doi.org/10.1109/TITS.2017.2774289

Reliability Assessment Tool: Development and Prototype Testing

List, G. F., Rouphail, N., Smith, R., & Williams, B. (2018), TRANSPORTATION RESEARCH RECORD, 2672(14), 29–38. https://doi.org/10.1177/0361198118798296

Validation and Calibration of Freeway Reliability Methodology in the Highway Capacity Manual: Method and Case Studies

Karmakar, N., Aghdashi, S., Rouphail, N. M., & Williams, B. M. (2018), TRANSPORTATION RESEARCH RECORD, 2672(15), 93–104. https://doi.org/10.1177/0361198118798723

Weigh Station Impact on Truck Travel Time Reliability: Results and Findings from a Field Study and a Simulation Experiment

Samandar, M. S., Williams, B. M., & Ahmed, I. (2018), TRANSPORTATION RESEARCH RECORD, 2672(9), 120–129. https://doi.org/10.1177/0361198118791667

Advances in travel time reliability monitoring and assessment

List, G. F., & Williams, B. (2017), Journal of Intelligent Transportation Systems, 21(6), 435–438. https://doi.org/10.1080/15472450.2017.1403536

View all publications via NC State Libraries

View publications on Google Scholar

Grants

STRIDE: Identifying and Mitigating Congestion Onset

US Dept. of Transportation (DOT)(1/01/20 - 12/31/21)

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STRIDE: Identifying and Mitigating Congestion Onset

Sponsor: US Dept. of Transportation (DOT)

Start Date: 1/01/20

End Date: 12/31/21

Abstract

This project aims to help transport agencies use “big data” to help mitigate congestion and manage system performance, for both freeways and arterials (and especially arterials, which have seen less attention). Two investigatory objectives are planned. In the first, we will create and train an algorithm to spot the onset of incidents and recurring congestion, so that system managers can be more responsive. Our hypothesis is that early responses help reduce the impacts (the queues are shorter, disappear quicker, and create less delay). We will 1) fuse data such as real time traditional detector data, CV data, and other online data, to produce a significant and consistent data-stream of high volume and high velocity heterogeneous data; and 2) use deep reinforcement learning to train an AI-based algorithm to spot the onset of these events, distinguish between them, and generate response suggestions based on effective past system responses or modeling of the system with selected strategies. In the second investigatory thread, we will create a performance monitoring algorithm that uses policy-based targets (e.g., speeds of 45 mph or better during congested conditions) and “big data” technologies to help agencies improve the efficacy of their congestion mitigation efforts. We will use condition-based policy travel rates to simplify the inference process and ensure that performance management focuses on situations that have the greatest need. In both these efforts, we will produce analysis tools that practitioners can use (stand-alone, prototype software intended to be integrated into existing system management platforms); as well as guide books for the use of the algorithms; and a project report. In the first year we will develop the analysis procedures (e.g., congestion “alarms” and monitoring “tools”); and in the second year, we will fine tune these algorithms and recommend real time strategies that use these tools to address impending or spreading congestion.

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NCDOT University Transportation Center of Excellence on Mobility and Congestion

NC Department of Transportation(2/01/20 - 7/31/22)

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NCDOT University Transportation Center of Excellence on Mobility and Congestion

Sponsor: NC Department of Transportation

Start Date: 2/01/20

End Date: 7/31/22

Abstract

The NCDOT is launching a bold and forward-looking effort to establish multi-university transportation centers of excellence to provide broad-based, multidisciplinary research into the applications and impacts of cutting edge technologies and emergent, disruptive trends. The projects included in our center proposal were custom-built to address the research areas spelled out in the request for proposals for the desired Mobility and Congestion center. The three themes are as follows: • Theme #1: Big Data and Data-Driven Transportation Management and Decision Support • Theme #2: Active Transportation Management/Integrated Corridor Management • Theme #3: Transit and Mobility as a Service

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AED Drone Delivery Project Support

Duke University(9/01/19 - 5/31/21)

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AED Drone Delivery Project Support

Sponsor: Duke University

Start Date: 9/01/19

End Date: 5/31/21

Abstract

The NGAT research team will work with the Duke project team that includes representatives from Durham County Emergency Services and other key stakeholders. The team will design a set of experiments for evaluating the human factors impacts of using drones to deliver AEDs in simulated out-of-hospital cardiac arrest events. The NGAT team will support the team with experiment design analysis, flight test coordination, drone flight operations, and data capture. All flight operations will meet federal, state, and local requirements for authorized use of small drones carrying an AED unit for research. The NGAT team will provide the aircraft and ground support resources necessary to accomplish the flight operations.

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STRIDE: Fly-By Image Processing for Real Time Congestion Mitigation

US Dept. of Transportation (DOT)(8/01/18 - 11/30/20)

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STRIDE: Fly-By Image Processing for Real Time Congestion Mitigation

Sponsor: US Dept. of Transportation (DOT)

Start Date: 8/01/18

End Date: 11/30/20

Abstract

Traffic monitoring is the centerpiece of congestion mitigation and traffic management. Whilst surveillance technologies have matured enough to provide informative depiction for the traffic, the status quo of the system cannot support immediate congestion mitigation acts. Proactive congestion mitigation requires a) real-time surveillance for traffic parameters, b) prediction for imminent congestion onset, in order to c) inform responsible parties to take immediate actions to prevent congestion. This framework is founded on short time analysis (1-5 minutes) which is not valid up to date. We foresee that using a “flock” of interconnected, self-managed drones, can establish a deployable system to perform immediate monitoring/assessment for traffic condition to infer if congestion is approached. The drones will use their own computational and communication capabilities to host an integrated reconnaissance platform that performs traffic monitoring and traffic analysis in real-time fashion. Unlike conventional image processing approaches, a specialized “inverse image” processing technique will be investigated in this project to suit the limited computing abilities for the drones, namely “Model Based Image Processing” (MBIP). In this technique, the targeted feature is represented graphically using specific statistical distribution patterns and/or equations. The patterns/equations will be utilized as an inverse filter that will be applied by the reconnaissance device to trigger if the feature has been observed, in this case the feature will correlate to imminent congestion onset. This module requires a very limited computational power and therefore it can be easily integrated in onboard drone circuits. The targeted feature (herein, features correlated to traffic congestion), will be reproduced utilizing a traffic simulation models. The developed framework will introduced as Traffic Model-Based Image Processing (T-MBIP). The proposed methodology will be tested first in a hardware in the loop simulation environment examine its fidelity and then, if possible, in full scale field environment.

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Rural Freight Transport Needs

NC Department of Transportation(8/01/18 - 8/15/20)

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Rural Freight Transport Needs

Sponsor: NC Department of Transportation

Start Date: 8/01/18

End Date: 8/15/20

Abstract

The rural areas of North Carolina have freight transportation needs that are very different from those of the urban areas. Much of their economic activity is focused around agriculture, forestry, and tourism/retirement. The principal transport mode is highway. Socio-economic conditions are challenging. Moreover, two of the rural areas, in the southwest and northeast, do not have urban areas that naturally serve as economic hubs. In the southwest, the nearest such areas are Atlanta and Chattanooga, both out of state; in the northeast, it is Norfolk which is again out of state.

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Impacts of Autonomous Vehicle Technology on Transportation Systems

NC Department of Transportation(8/01/18 - 7/31/21)

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Impacts of Autonomous Vehicle Technology on Transportation Systems

Sponsor: NC Department of Transportation

Start Date: 8/01/18

End Date: 7/31/21

Abstract

Autonomous vehicle (AV) technology is expected to fundamentally change transportation systems. The Transportation Planning Branch at NCDOT, which is responsible for the state’s long-range transportation plan, needs state-of-the-art information and predictions on AV technology and its potential impacts on transport to be better prepared for the upcoming changes and maximize the social benefits that this technology will enable. The Transportation Systems group faculty (Drs. Bardaka, List, Rouphail, and Williams) and Dr. Frey (Environmental Engineering) in the Department of Civil, Construction, and Environmental Engineering at NCSU as well as Dr. Cummings, the Director of the Humans and Autonomy Laboratory at Duke University will work together to leverage existing research in the area of AV technology to evaluate impacts and provide policy and future research recommendations to NCDOT. The study will include a comprehensive literature review on AV technology and its impact on transportation demand, capacity, mobility, traffic safety, emissions, energy use, and land use. The results of previous research will be analyzed and case studies for North Carolina will be developed. The study will also provide recommendations to NCDOT regarding changes in policies and regulations, future test plans and test infrastructure, and research priorities in the area of AV technology. As part of this study, the researchers will work closely with the Transportation Planning Branch to provide guidance on how existing models (such as the statewide demand model) could be adapted to account for the presence of AVs.

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STRIDE-Workforce Development and Tech Transfer

US Dept. of Transportation (DOT)(8/01/17 - 6/30/19)

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STRIDE-Workforce Development and Tech Transfer

Sponsor: US Dept. of Transportation (DOT)

Start Date: 8/01/17

End Date: 6/30/19

Abstract

The funds provided under this STRIDE proposal will support NCSU definition and development of ideas for detailed follow-on workforce development and technical transfer activities including necessary coordination with University of Florida and other STRIDE partner institutions.

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STRIDE-Project J. Transit in the Era of Shared Mobility

US Dept. of Transportation (DOT)(5/31/17 - 1/31/19)

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STRIDE-Project J. Transit in the Era of Shared Mobility

Sponsor: US Dept. of Transportation (DOT)

Start Date: 5/31/17

End Date: 1/31/19

Abstract

The objective of this study is to analyze the changes in transit ridership in the United States with the introduction of on-demand ridesourcing options, also known as Transportation Network Companies (TNCs). The study will be undertaken in two phases by four different universities within the STRIDE consortium. The first phase is designed to have early analysis that can be easily published to show the value of STRIDE-funded research. The second phase will allow the researchers to investigate data and methods that could be used for further study about how TNCs and transit work together and against one another.

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STRIDE-Project I. Freeway Management for Optimal Reliability

US Dept. of Transportation (DOT)(1/19/17 - 11/30/19)

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STRIDE-Project I. Freeway Management for Optimal Reliability

Sponsor: US Dept. of Transportation (DOT)

Start Date: 1/19/17

End Date: 11/30/19

Abstract

This project will develop tools for analyzing and optimizing system reliability on freeways. These tools include analytical and simulation frameworks for the optimization and near real-time performance forecast of active traffic management (ATM) systems. The proposed traffic congestion mitigation toolbox will include local and/or system-wide adaptive ramp metering, integrated rampmetering and variable speed limit control, hard shoulder running, speed harmonization, dynamic pricing of express lanes, optimized traffic diversions and efficient incident response and management. ATM deployment is a means to meet specific reliability goals below a desirable agency specified threshold. This two-year project will develop a methodological framework, a novel integrative process of system modeling, and will select and optimize appropriate strategies from the ATM toolbox to meet reliability goals. We will also test the validity of the proposed approach using data from a minimum of three freeway facilities in the Southeast region at both rural and urban locations.

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STRIDE-Project G. Improving Work Zone Mobility through Planning, Design and Operations

US Dept. of Transportation (DOT)(1/19/17 - 12/31/20)

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STRIDE-Project G. Improving Work Zone Mobility through Planning, Design and Operations

Sponsor: US Dept. of Transportation (DOT)

Start Date: 1/19/17

End Date: 12/31/20

Abstract

This project consists of a series of tasks intended to yield results that will better inform transportation agencies in their planning, design, and operations of work zones. A selected set of freeway lane closure scenarios across a wide range of traffic volumes and vehicle mixes will be modeled using microscopic traffic simulation. The modeling effort will produce key measures of effectiveness such as travel time, delay, and queue lengths so that the impact of these common lane closure scenarios across a range of traffic conditions will be documented. Additionally, a range of lane merge configurations will also be examined. Data archives from previous research on freeway work zone mobility by some of the team members will be utilized to describe driver behaviors and extend freeway work zone modeling efforts. Driver behaviors and actions under a range of traffic conditions and roadway geometric configurations will be examined. Also, additional modeling needs identified during the study will be supported using an extensive traffic data archive from previous research.

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