Publications

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

Simulation of Work Zones with Lane Closures in Proximity of Freeway Interchanges

Yeom, C., Rasdorf, W., Rouphail, N., & Schroeder, B. (2018), IEEE INTELLIGENT TRANSPORTATION SYSTEMS MAGAZINE, 10(3), 184–195. https://doi.org/10.1109/MITS.2018.2842027

Application of high-resolution vehicle data for free-flow speed estimation

Rouphail, N. M., Kim, S., & Aghdashi, S. (2017), Transportation Research Record, (2615), 105–112.

Assessing the impact of closely-spaced intersections on traffic operations and pollutant emissions on a corridor level

Fernandes, P., Coelho, M. C., & Rouphail, N. M. (2017), Transportation Research. Part D, Transport and Environment, 54, 304–320. https://doi.org/10.1016/j.trd.2017.05.016

The Effect of a roundabout corridor's design on selecting the optimal crosswalk location: a multi-objective impact analysis

Fernandes, P., Salamati, K., Rouphail, N. M., & Coelho, M. C. (2017), International Journal of Sustainable Transportation, 11(3), 206–220. https://doi.org/10.1080/15568318.2016.1237689

Freeway work zone free-flow speed model development

Yeom, C., Hajbabaie, A., Rouphail, N. M., Rasdorf, W., & Schroeder, B. J. (2017), ITE Journal, 87(11), 38–44.

Lane utilization model development for diverging diamond interchanges

Yeom, C., Schroeder, B. J., Cunningham, C., Salamati, K., & Rouphail, N. M. (2017), Transportation Research Record, (2618), 27–37.

Multi-scenario optimization approach for assessing the impacts of advanced traffic information under realistic stochastic capacity distributions

Li, M. X., Rouphail, N. M., Mahmoudi, M., Liu, J. T., & Zhou, X. S. (2017), Transportation Research. Part C, Emerging Technologies, 77, 113–133. https://doi.org/10.1016/j.trc.2017.01.019

Quantifying travel time variability at a single bottleneck based on stochastic capacity and demand distributions

Li, M. X., Zhou, X. S., & Rouphail, N. M. (2017), Journal of Intelligent Transportation Systems, 21(2), 79–93. https://doi.org/10.1080/15472450.2016.1163639

Turboroundabouts along corridors analysis of operational and environmental impacts

Fernandes, P., Rouphail, N. M., & Coelho, M. C. (2017), Transportation Research Record, (2627), 46–56.

View all publications via NC State Libraries

Grants

Predicting Lane Change Intensity within Urban Interchange Influence Areas

NC Department of Transportation(8/01/18 - 7/31/20)

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Predicting Lane Change Intensity within Urban Interchange Influence Areas

Sponsor: NC Department of Transportation

Start Date: 8/01/18

End Date: 7/31/20

Abstract

This research is intended to assist NCDOT in improving mobility and safety performance at urban interchange influence areas (IIA's) in North Carolina, to remedy the excessive levels of discretionary lane changes occurring at those locations. The research will predict how driver lane changing behavior is impacted by local traffic, by control and by site conditions. A second objective will be to ascertain whether changes in signing, markings or other traveler information near the IIA can induce fewer discretionary lane changes and thus reduce unnecessary traffic turbulence near interchanges. Currently NCDOT has no means to track lane changing behavior. This research will take advantage of an existing (and continuously expanding) NC State high resolution second by second trip database which uses an in-vehicle OBD-II unit (called i2d) in the Triangle Region. Supplemented with controlled experiments and other data sources the research will produce predictions of lane changing behavior at the vehicle scale, based on present IIA geometrics, the prevailing traffic states and any implemented lane-discipline-inducing treatments. To achieve these objectives, we are proposing to develop a statistical model to predict lane change intensity in urban interchange influence areas. Lane changes will be characterized as mandatory or discretionary. Our i2d data can inform us which type of lane change it is, based on knowledge of the trip origin and destination. Initially, however we will assume that all lane changes are discretionary except when it is known that the vehicle is either entering or exiting the IAA. Thus our predictor variable will be the expected discretionary lane change intensity per vehicle mile in the IIA.

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Impacts of Autonomous Vehicle Technology on Transportation Systems

NC Department of Transportation(8/01/18 - 7/31/20)

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Impacts of Autonomous Vehicle Technology on Transportation Systems

Sponsor: NC Department of Transportation

Start Date: 8/01/18

End Date: 7/31/20

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-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 F. Integrated Implementation of Innovative Intersection Designs

US Dept. of Transportation (DOT)(5/16/17 - 11/15/19)

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STRIDE-Project F. Integrated Implementation of Innovative Intersection Designs

Sponsor: US Dept. of Transportation (DOT)

Start Date: 5/16/17

End Date: 11/15/19

Abstract

Alternative intersections and interchanges (AII) such as the Restricted Crossing U-Turn (RCUT), can increase safety and capacity, thereby reducing congestion for both vehicular and non-vehicular traffic. Often, RCUTs provide extended capacity for an existing intersection, typically for intersections which are under consideration for grade separation due to capacity constraints. Limited construction funding can often delay costly bridge projects for years. Consequently, AII designs can be useful in many cases because the intersection strategy provides the opportunity to extend the service life of intersections experiencing problems that will not likely be resolved otherwise in the foreseeable future. While many agencies recognize the operational and safety benefits of RCUT designs, the comparative evaluations undertaken during the selection process often have a very narrow scope. For example, in evaluating the effectiveness of RCUT intersections, studies typically model only vehicular operations. However, the implementation of RCUT intersections may significantly impact other users of other modes, such as pedestrians and bicyclists. In addition, RCUT designs may have broader level impacts on the corridor if operations are improved at one intersection and not at other upstream and downstream intersections. Accordingly, this proposed project will explore system level integration alternatives related to RCUT designs and will provide guidance for their use to optimize operations and to extend the useful life of facilities. In addition, the research team will explore the multimodal impacts of this intersection design to look at ways to improve operations for all users.

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Characterizing the Determinants of Vehicle Traffic Emissions Exposure: Measurement and Modeling of Land-Use, Traffic, Emissions, Transformation and Transport

US Environmental Protection Agency (EPA)(1/01/17 - 5/31/19)

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Characterizing the Determinants of Vehicle Traffic Emissions Exposure: Measurement and Modeling of Land-Use, Traffic, Emissions, Transformation and Transport

Sponsor: US Environmental Protection Agency (EPA)

Start Date: 1/01/17

End Date: 5/31/19

Abstract

Our proposal will determine the most important variables that explain spatial and temporal variance of near road traffic-related pollutant concentrations: We will explore the relative influence of traffic activity, the built environment (roadways and other built structures), and environmental (e.g. temperature, wind and background concentrations) factors on multi-pollutant transport, differential evolution and how all of these influence human exposure. We will also demonstrate novel surrogates of near-road traffic-related pollution: We will develop data and modeling approaches to quantify exposure concentrations of multiple pollutants emitted from vehicles or formed as secondary pollutants in the near-roadway microenvironment: fine particulate matter (PM), ultrafine particles (UPF), semi-volatile organic compounds (SVOCs), nitrogen dioxide, and carbon monoxide. The role of individual pollutants and mixtures of pollutants, and whether some pollutants are good surrogates for others, will be assessed. We will improve inputs for exposure models for traffic-related health: We will explore the implications of our measurement findings by applying them in spatial and temporal analysis of the relationship between human exposure (or surrogates for human exposure) and adverse effects, including evaluation of mixtures of pollutants and other proxies for exposure

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RTI Scholars Program: High Resoloution In-Vehicle Sensing and Communicaton

RTI International (aka Research Triangle Institute)(9/06/16 - 5/15/17)

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RTI Scholars Program: High Resoloution In-Vehicle Sensing and Communicaton

Sponsor: RTI International (aka Research Triangle Institute)

Start Date: 9/06/16

End Date: 5/15/17

Abstract

This collaborative research between ITRE and RTI will be carried out by Dr. Nagui Rouphail and Dr. Alan Karr, Director of the Center of Excellence for Complex Data Analysis (CoDA) at RTI. It is intended to occur in the period between August 16 and December 31, 2016 or the entire Fall Semester of AY16, and will focus on the area of transportation data science. There is a long history of successful research collaboration between Drs. Karr and Rouphail in the area of transportation data analytics, dating back to the mid 1990’s. At that time Dr. Karr resided at NISS and Dr. Rouphail started at NC State. Funding for those collaborations was derived from such sources as the USDOT IDEA Program, BTS, NSF and the Strategic Highway Research Program (SHRP-2). Currently, both Rouphail and Karr are collaborating on a FHWA funded research on the development of transportation data visualization tools at the network and connected vehicle (probe) levels. The proposed collaboration builds on previous joint research which had focused on fixed infrastructure generated data. As the timeline schematic below implies, mobility data sources are shifting from aggregated infrastructure sensing, to monitoring individual vehicles (probes) and now individual travelers. This is in line with the anticipated safety and mobility measurements generated from connected and autonomous vehicles (CAV). The proposed collaboration will focus on high resolution (1 Hz) vehicle dynamics data that are locally generated from a continuously monitored fleet of 15-20 ITRE-instrumented vehicles for which mobility and safety data have been archived since April 2014. To date nearly 30 million detailed data records of thousands of trips in the Triangle region have been archived. We will explore those rich data to characterize human driving behavior as it relates to safety, mobility and environmental impacts with consideration of the driving context.

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Characterizing the Determinants of Vehicle Traffic Emissions Exposure: Measurement and Modeling of Land-Use, Traffic, Emissions, Transformation and Transport

US Environmental Protection Agency (EPA)(8/15/15 - 6/30/17)

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Characterizing the Determinants of Vehicle Traffic Emissions Exposure: Measurement and Modeling of Land-Use, Traffic, Emissions, Transformation and Transport

Sponsor: US Environmental Protection Agency (EPA)

Start Date: 8/15/15

End Date: 6/30/17

Abstract

Our proposal will determine the most important variables that explain spatial and temporal variance of near road traffic-related pollutant concentrations: We will explore the relative influence of traffic activity, the built environment (roadways and other built structures), and environmental (e.g. temperature, wind and background concentrations) factors on multi-pollutant transport, differential evolution and how all of these influence human exposure. We will also demonstrate novel surrogates of near-road traffic-related pollution: We will develop data and modeling approaches to quantify exposure concentrations of multiple pollutants emitted from vehicles or formed as secondary pollutants in the near-roadway microenvironment: fine particulate matter (PM), ultrafine particles (UPF), semi-volatile organic compounds (SVOCs), nitrogen dioxide, and carbon monoxide. The role of individual pollutants and mixtures of pollutants, and whether some pollutants are good surrogates for others, will be assessed. We will improve inputs for exposure models for traffic-related health: We will explore the implications of our measurement findings by applying them in spatial and temporal analysis of the relationship between human exposure (or surrogates for human exposure) and adverse effects, including evaluation of mixtures of pollutants and other proxies for exposure

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INNOVATIONS IN DATA VISUALIZATION USING THE RESEARCH DATA EXCHANGE (RDE) AND THE WEATHER DATA ENVIRONMENT (WXDE)

Federal Highway Administration (FHWA)(10/01/15 - 3/31/17)

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INNOVATIONS IN DATA VISUALIZATION USING THE RESEARCH DATA EXCHANGE (RDE) AND THE WEATHER DATA ENVIRONMENT (WXDE)

Sponsor: Federal Highway Administration (FHWA)

Start Date: 10/01/15

End Date: 3/31/17

Abstract

ABSTRACT Data visualization tools will greatly enhance usability and thus the use of datasets archived in the RDE and WxDE. These resources are expected to be widely used by researchers as well as those involved in the development, deployment, and evaluation of connected vehicle technologies and data applications. Given the diversity of users as well as the diversity of the datasets potentially available to them, our approach to the task is to create a flexible, powerful framework consisting of the following: • A toolbox of visualization components, such as maps, charts, scatterplots, histograms and even tabular summaries. These components will be available to use in both static and dynamic/animated formats. The individual components will be provided in such a way that a user can assemble multiple dataset views into a dashboard that both summarizes the data and supports drill down options. The multiple dashboard views will be linked, so that selections in one view propagate to the others, and each visualization component will be constructed using open source software, such as D3.js. • One or more translation engines that will enable the views to operate on any dataset contained in the RDE and WxDE regardless of its native format (for example, CSV, SQL, etc.). The software tool resulting from this effort should serve multiple purposes including characterizing as well as evaluating the quality of one or more datasets.

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SHRP2 L38 Reliability Data and Analysis Tools Implementation Assistance Program Proof of Concept Pilot Study

NC Department of Transportation(8/16/15 - 6/30/19)

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SHRP2 L38 Reliability Data and Analysis Tools Implementation Assistance Program Proof of Concept Pilot Study

Sponsor: NC Department of Transportation

Start Date: 8/16/15

End Date: 6/30/19

Abstract

At the conclusion of the Second Strategic Highway Research Program (SHRP2) research phase, the SHRP2 Implementation Assistance Program (IAP) was established by the Federal Highway Administration to “help State departments of transportation (DOTs), metropolitan planning organizations (MPOs), and other interested organizations deploy SHRP2 Solutions.” The “SHRP2 Solutions” mentioned in this program mission statement consist of implementable tools in each of the SHRP2 focus areas, namely Reliability, Capacity, Safety, and Renewal. As is clear from the project title, the pilot study detailed in this project authorization document involves reliability data and analysis tools. Prior to and concurrent with the SHRP2 research phase, the North Carolina Department of Transportation has been actively and continually engaged in direct support for complementary research in the domain of mobility and reliability. Key NCDOT-sponsored research projects in this effort include completed projects RP 2006-13 Effectiveness of Traveler Information Tools; HWY 2009-05 Assessing Operational, Pricing, and Intelligent Transportation System Strategies for the I-40 Corridor Using DYNASMART-P; and RP 2011-07 Mobility and Reliability Performance Measurement and the ongoing project RP 2013-08 Smartlink – Baseline for Measurement of Benefits. Engagement in these research efforts has put NCDOT in a strong position to lead the way in implementing. Bolstered by this strong position, the NCDOT submitted a proof of concept pilot study application under round 4 of the IAP in the summer of 2014. NCDOT’s application was approved for FHWA implementation assistance funding, and the statement of work included in this project authorization document outlines the research tasks defined in the IAP application. The proof of concept pilot study envisioned by the application covers all the tools in the Reliability Data and Analysis Tools bundle, namely the implementable research products from the following SHRP2 research projects: L02 Establishing Monitoring Programs for Mobility and Travel Time Reliability; L05 Incorporating Reliability Performance Measures into the Transportation Planning and Programming Processes; L07 Evaluation of Cost-Effectiveness of Highway Design Features; L08 Incorporation of Travel Time Reliability into the Highway Capacity Manual; and C11 Development of Improved Economic Analysis Tools Based on Recommendations from Project C03 [Interactions between Transportation Capacity, Economic Systems, and Land Use merged with Integrating Economic Considerations Project Development]. The SHRP2 data and analysis tools implementation supported by this IAP proof of concept pilot study is fully consistent with NCDOT initiatives aimed at providing enhanced system performance monitoring and measurement. These monitoring and measurement capabilities are essential to enabling performance-based decision support and the assessment of the impact of strategic transportation investments. The results, findings, and recommendations arising from this pilot study will help refine that application of the SHRP2 reliability data and analysis tools, solidify NCDOT’s leadership position in this emerging area, and disseminate lessons learned to the national transportation management community.

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Traveler Response Architecture using Novel Signaling for Network Efficiency in Transportation (TRANSNET)

US Dept. of Energy (DOE)(10/01/15 - 8/30/18)

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Traveler Response Architecture using Novel Signaling for Network Efficiency in Transportation (TRANSNET)

Sponsor: US Dept. of Energy (DOE)

Start Date: 10/01/15

End Date: 8/30/18

Abstract

The National Transportation Center at the University of Maryland (NTC@Maryland) with Arizona State University, North Carolina State University, and the University of Florida, supported by Google, Uber, RubyRide, INRIX, TomTom, HERE, and State Departments of Transportation in Maryland, North Carolina, and Florida, proposes an Integrated, Personalized, REal-time Traveler Information and Incentive (iPretii) technology that encompasses: a) A person-level travel behavior, traffic simulator, and energy estimator called the System Model (SM); and b) A Control Architecture (CA) with personalized signal design based on behavioral research, user intent prediction, signal optimization, and signal delivery modules. We envision that iPretii will enable public and private-sector entities to deliver personalized incentives to guide a subset of travelers to adjust both their driving behavior and choices of route, departure time, and mode. These personalized incentives minimize energy consumption by optimizing driving style, mitigating congestion, and increasing vehicle occupancy. iPretii will be developed within two years to quantify possible energy efficiency gains in the Washington, DC-Baltimore megaregion with a population of 8.3 million, under recurrent and non-recurrent congestion (e.g., accidents, work zones, adverse weather). Multiple field tests are designed to identify and address technology gaps, and to demonstrate iPretii readiness for real-world implementation.

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