About

David Noyce is the Arthur F. Hawnn Professor of Transportation Engineering and an Executive Associate Dean at the College of Engineering at the University of Wisconsin-Madison. His research focuses on the operational and behavioral aspects of transportation safety and operations, including advanced traffic control devices, traffic signalization, smart corridors, and intelligent intersections with vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication technologies. He has a particular interest in improving the safety and efficiency of protected and permissive left-turns at signalized intersections, with his work contributing to the national implementation of the flashing yellow arrow permissive left-turn indication. Noyce is also deeply involved in crash data recording technology, vehicle crash analysis, and reconstructing crashes involving automated vehicles to enhance safety analysis. He is the current director of the Traffic Operations and Safety (TOPS) Laboratory and the Wisconsin Driving Simulator Laboratory, which have conducted extensive research in transportation safety, operations, and multimodal technologies. Noyce has led over $35 million in research activities through the TOPS Lab and is an associate director of the Safety Research Using Simulation (SaferSIM) Center, collaborating with multiple universities. His work with full-scale driving simulation has spanned 25 years, studying driver comprehension, behavior, and the effects of various traffic control devices, design features, and new technologies on driver performance. Additionally, Noyce's interests extend to transportation and building construction, focusing on productivity, efficiency, work zone management, and safety, with recent collaborations involving the Federal Highway Administration to improve roadway construction safety and efficiency. He maintains an active teaching portfolio in transportation operations, geometric design, and safety, and has developed a transportation laboratory equipped for real-time traffic data collection and testing new equipment and technologies.

Research topics

• Computer science
• Transport engineering
• Simulation
• Engineering
• Human–computer interaction

Selected publications

• Empirical modeling of vehicle-bicycle interactions on high-speed rural roads: passing lateral distance and speed

  Accident Analysis & Prevention · 2026-04-15

  articleOpen accessSenior author

  Driving behavior and interactions with bicyclists on rural roads have not been quantified and modeled extensively. Naturalistic bicycling data for 1,991 passing events were collected on a rural two-lane roadway (55 mph, 88 kph speed limit) to quantify how opposing traffic and vehicle platooning influence passing lateral distance, speed, and aerodynamic forces. Results indicate that opposing traffic significantly reduces passing lateral distance by an average of 2.0 ft (61 cm) and decreases speed by an average of 2.3 mph (3.7 kph). Platooning leads to progressively reduced passing distance and speed among following vehicles. The reductions reflect limited available space and increased risk for bicyclists when opposing vehicles are present. The estimated aerodynamic lateral forces created by passenger vehicles were well below tolerable safety limits for bicyclists. To surpass tolerable limits, passenger vehicles would have to pass at a lateral distance of 0.9 ft (27 cm) at a speed of 55 mph (88 kph). Lateral distance and speed were found to be independent at a disaggregate level. Leading vehicles' lateral distance followed a Log-normal distribution and speed followed a Weibull distribution. Theoretical joint probability density functions were developed for leading and following vehicles with and without opposing traffic. Pairwise differences among lead and follower vehicles were similar and resembled a Normal distribution. The developed joint probability density functions can be used for calibration and validation of driving simulators, or development of autonomous and artificial intelligence driving models. Results contribute to developing safer design guidance and risk mitigating strategies for bicyclists.

  PublisherDOI

• Safety and Mobility Impacts of Work Zone Lane and Shoulder Widths

  Transportation Research Record Journal of the Transportation Research Board · 2025-07-23

  articleSenior author

  The goal of this research was to quantify the mobility and safety impacts of different combinations of lane width and shy distance to the barrier for a given paved width in work zones. The research team developed a device to measure lateral distance and derive speed, vehicle length/type, and headway information under day and night conditions. Data were collected at 17 locations in Illinois, Michigan, and Wisconsin. Lateral distance data of over a quarter million vehicles were used for the safety analysis. Extreme value theory modeling was conducted to estimate the probabilities of right-hand edge line encroachment and right-hand barrier contact. Wider lanes were found to have decreased probabilities of edge line encroachment and barrier contact, while wider shy distances were associated with increased probability of edge line encroachment and decreased probability of barrier contact. The speeds of over 125,000 free flow vehicles were used to quantify the mobility impact. Linear regression was implemented to develop models for estimating free flow speeds in work zones. Work zone free flow speed increased with an increase in speed limit, lane width, and left-/right-hand shy distance to the barrier. A case study of a 55-mph posted work zone with two open lanes and barrier on both sides with 26-ft available paved width is presented. Results of the case study indicate that 11-ft lanes with 2-ft shy distance have a slightly lower probability of right-hand barrier contact (for vehicles in the right-hand lane) than 12-ft lanes with 1-ft shy distance, while having a greater free flow speed. This research has demonstrated how lateral distance can be collected and modeled along with speed data to assess safety and mobility impacts in work zones.

  PublisherDOI

• A Digital Twin Framework for Physical-Virtual Integration in V2X-Enabled Connected Vehicle Corridors

  IEEE Transactions on Intelligent Transportation Systems · 2025-05-09 · 9 citations

  article

  Transportation Cyber-Physical Systems (T-CPS) enhance safety and mobility by integrating cyber and physical transportation systems. A key component of T-CPS is the Digital Twin (DT), a virtual representation that enables simulation, analysis, and optimization through real-time data exchange and communication. Although existing studies have explored DTs for vehicles, communications, pedestrians, and traffic, real-world validations and implementations of DTs that encompass infrastructure, vehicles, signals, and communications remain limited due to several challenges. These include accessing real-world connected infrastructure, integrating heterogeneous, multi-sourced data, ensuring real-time data processing, and synchronizing the digital and physical systems. To address these challenges, this study develops a traffic DT based on a real-world connected vehicle corridor. Leveraging the Cellular Vehicle-to-Everything (C-V2X) infrastructure in the corridor, along with communication, computing, and simulation technologies, the DT accurately replicates physical vehicle behaviors, signal timing, communications, and traffic patterns within the virtual environment. Building upon the previous data pipeline, the digital system ensures robust synchronization with the physical environment. Moreover, the DT’s scalable and redundant architecture enhances data integrity, making it capable of supporting future large-scale C-V2X deployments. Lastly, the DT’s ability to provide feedback to the physical system is demonstrated through applications such as signal timing adjustments, vehicle advisory messages, and incident notifications. The proposed DT is a vital tool in T-CPS, enabling real-time traffic monitoring, prediction, and optimization to enhance the safety and mobility of transportation systems.

  PublisherDOI

• Dynamic Lane Assignment with Signal Optimization for Connected Autonomous Vehicle (CAV): A Synthesis of Literature

  2025-06-05

  articleSenior authorCorresponding

  Traffic demand varies significantly in urban areas, impacting intersection performance. Current control strategies assume fixed lane assignment with signal optimization that focuses on the traffic movements at each approach. This leads to inefficient use of temporal and spatial resources. To improve the intersection performance in such cases, Dynamic Lane Assignment (DLA), a component of Intelligent Transportation Systems (ITS), is employed to improve the intersection efficiency. As we move into an era where Connected and Automated Vehicle (CAV) technology is increasingly recognized for enhancing traffic safety and efficiency, it is important to consider that CAVs and human-driven vehicles (HDVs) will co-exist in the system for a substantial period. This necessitates managing CAVs alongside HDVs, which could benefit from dedicated lane assignments for CAVs together with signal optimization to enhance overall system performance and efficiency. However, research on CAV-based DLA combined with signal optimization in mixed traffic environments remains limited. This research focuses on understanding the state-of-the-art of managing intersections with DLA in mixed traffic environments. Information was gathered from previous research papers and public documents. For the synthesis, studies were categorized into the following topics: (1) application of DLA combined with signal optimization for CAVs; (2) application of DLA with CAVs for freeway management; and (3) other lane assignment strategies such as Dynamic Lane Grouping (DLG). The synthesis focuses on the assumptions, strategies, and policies related to lane assignment, as well as the methodologies employed in these studies. Finally, the paper provides a comprehensive overview of DLA strategies for intersection management, identifies research gaps, and proposes future directions for mixed traffic environments.

  PublisherDOI

• The Future Driver: Exploring the Safety Challenges of Level 4 Automated Vehicles During Manual Control

  Transportation Research Record Journal of the Transportation Research Board · 2025-05-29

  articleSenior author

  Level 4 automated vehicles (AVs) with the operational design domain (ODD) expanding over time are expected to be the future. Although Level 4 AVs do not require driver takeover, human driving will be necessary outside the ODD. While there is a significant amount of research on takeover/disengagement, no prior studies have explored the safety challenges of manual operation of Level 4 AVs. Crash sequence analysis was employed to compare crashes of the AV (during manual control) (AVM) and general driving population, using U.S. data from California Department of Motor Vehicles crash reports and the Crash Report Sampling System (CRSS) dataset, respectively. Clusters of AVM and CRSS crashes were aggregated into nine groups based on crash context. The results suggest that certain crash groups are more challenging for AVM than for CRSS. AVM crashes are vastly less severe than CRSS crashes for all but one crash group that involved right turns. Nearly half of the AVM crashes involving left and right turns were rear-end crashes, while the majority of similar CRSS crashes were side-swipe or angle. The majority of rear-end AVM crashes occur at intersections, while the converse is true for similar CRSS crashes. Intriguingly, in all the AVM rear-end crashes, the lead vehicle was an AV, suggesting hesitation on the part of the safety driver. For AVM, while lane-changing crashes were less frequent, crashes involving parked vehicles were more frequent than for CRSS. The findings indicate the importance of understanding how driver behavior changes with Level 4 AVs, and how driver training might play an important role in the safety of AVs.

  PublisherDOI

• V2X-LLM: Enhancing V2X Integration and Understanding in Connected Vehicle Corridors

  ArXiv.org · 2025-03-03

  articleOpen access

  The advancement of Connected and Automated Vehicles (CAVs) and Vehicle-to-Everything (V2X) offers significant potential for enhancing transportation safety, mobility, and sustainability. However, the integration and analysis of the diverse and voluminous V2X data, including Basic Safety Messages (BSMs) and Signal Phase and Timing (SPaT) data, present substantial challenges, especially on Connected Vehicle Corridors. These challenges include managing large data volumes, ensuring real-time data integration, and understanding complex traffic scenarios. Although these projects have developed an advanced CAV data pipeline that enables real-time communication between vehicles, infrastructure, and other road users for managing connected vehicle and roadside unit (RSU) data, significant hurdles in data comprehension and real-time scenario analysis and reasoning persist. To address these issues, we introduce the V2X-LLM framework, a novel enhancement to the existing CV data pipeline. V2X-LLM leverages Large Language Models (LLMs) to improve the understanding and real-time analysis of V2X data. The framework includes four key tasks: Scenario Explanation, offering detailed narratives of traffic conditions; V2X Data Description, detailing vehicle and infrastructure statuses; State Prediction, forecasting future traffic states; and Navigation Advisory, providing optimized routing instructions. By integrating LLM-driven reasoning with V2X data within the data pipeline, the V2X-LLM framework offers real-time feedback and decision support for traffic management. This integration enhances the accuracy of traffic analysis, safety, and traffic optimization. Demonstrations in a real-world urban corridor highlight the framework's potential to advance intelligent transportation systems.

  PublisherOA PDF

• How similar or different are automated vehicle and human-driven vehicle crash patterns? Findings from crash sequence analysis

  Accident Analysis & Prevention · 2025-09-13 · 1 citations

  article
  PublisherDOI

• Supporting Safe System Approach Decision-Making Through Crash Sequence Analysis

  Transportation Research Record Journal of the Transportation Research Board · 2025-11-13

  articleSenior author

  The Safe System Approach (SSA) aims to eliminate fatal and serious injury roadway crashes through a holistic view of the road system, moving away from traditional safety analysis based exclusively on historical crash data. One reason for this is the classification of crashes into broad categories (e.g., head-on, sideswipe), which does not capture crash progression or contributing factors. In this context, this paper applies crash sequence analysis to historical crash data and uses the findings to proactively identify safety issues in similar contexts, in alignment with the SSA framework. The method uses sequence-of-events information from crash data to generate clusters of crashes with similar underlying characteristics. Data from fatal and serious injury crashes from urban intersections in the state of Ohio between 2018 and 2022 were used in the analysis. The results show 12 clusters with unique characteristics that consider the sequence of events of each crash. Although derived from crash data, the clusters offer an in-depth understanding of the factors associated with each one and help identify cluster-specific countermeasures related to various SSA elements. State and local jurisdictions can use the presented methodology in transportation safety programs, by focusing on the clusters that represent local challenges or on countermeasures related to the issues of multiple clusters. Finally, the method can also be associated with site-specific analysis, providing a comprehensive toolkit for practitioners.

  PublisherDOI

• Automated Vehicles vs. Human Drivers: Modeling Driving Behavior Using Data from Field Experiments

  2024-06-13 · 1 citations

  articleSenior authorCorresponding

  As automated vehicles (AVs) gradually gain prevalence on public roads, understanding their distinctive driving behavior is crucial for traffic management and planning. This study conducted field experiments using an SAE Level-3/4 AV and collected driving data of AVs and human drivers on public roads using sensors including GPS, radar, camera, and LiDAR. The Wilcoxon rank-sum test is used to identify the difference in the behavior between AVs and human drivers. In addition, logistic regression and Extreme Gradient Boosting (XGBoost) are used to classify AVs and human drivers. Results suggest that there exists a significant difference in driving behavior between AVs and human drivers. Moreover, features including the mean speed and the distance from the vehicle to the detected objects are positively related to the probability of the vehicles being AVs, while the standard deviation of speed and the mean acceleration are negatively associated with it. Furthermore, XGBoost accurately identifies AVs and human drivers using the extracted features with an average area under the curve of 0.92. Results from interpreting results from XGBoost indicate that it performs better when the mean speed is either in the low or high ranges. Moreover, AVs and human drivers are hard to differentiate using the model when the vehicle is too far from other objects. This study underscores the substantial divergence in driving behavior between AVs and human drivers, offering valuable insights for the evaluation of the impact of AVs on traffic conditions.

  PublisherDOI

• Winter Maintenance Multispectral Performance Evaluation: Salt Brine versus Solid Salt Applications

  Journal of Cold Regions Engineering · 2024-09-16 · 1 citations

  articleSenior author

  Performance measures that are commonly used in winter maintenance provide valuable information. However, some metrics are subjective (i.e., visual inspection), rely on historical records, depend on agencies’ practices, or only capture specific information. Winter maintenance performance measures are evaluated independently, and relationships among variables are not assessed. Therefore, the metric travel disruption (TD) is proposed in this study to perform a multispectral evaluation with input, output, and outcome-based performance measures to compare salt brine with solid salt applications. The metric TD integrates different sources of information to estimate a continuous performance measure that accounts for storm, vehicle operations, and roadway conditions over time instead of intermittent measurements or one-time observations, such as roadway friction or time to bare/wet. The input performance measures included storm conditions, type of chemical material, frequency of application, and application rate. The output performance measures were the amount of material used and the lane kilometer (miles) covered. The outcome performance measures consisted of time to bare/wet, speed reduction period, speed recovery period, storm impact period, maximum speed reduction, and TD. For salt brine, previous research has shown reduced salt usage, shorter times to bare/wet, and higher friction. However, there are some concerns regarding the operational and safety performance of roadways that are treated with salt brine. Winter treatment field data and vehicle probe data from the National Performance Management Research Data Set (NPMRDS) were collected from the study (salt brine) and control (solid salt) routes in Wisconsin. For the amount of salt used, the study routes used 32.6% less overall salt than the control routes per storm. The TD indicated that, on average, there was no statistically significant difference between the study and control routes. Despite the lower amount of salt that was used, roadways that were treated with salt brine had a similar operational performance as roadways that were conventionally treated with solid salt, which made salt brine application a cost-effective strategy. This evaluation demonstrated a multispectral performance evaluation of winter maintenance to appropriately quantify the effectiveness of treatments.

  PublisherDOI

Frequent coauthors

• Kelvin R. Santiago-Chaparro

  University of Wisconsin–Madison

  302 shared

• Hiba Nassereddine

  University of Wisconsin–Madison

  276 shared

• Jon Riehl

  University of Wisconsin–Madison

  192 shared

• Madhav Chitturi

  University of Wisconsin–Madison

  104 shared

• Andrea Bill

  University of Wisconsin–Madison

  85 shared

• Xiao Qin

  Dalian Maritime University

  51 shared

• Steven T. Parker

  University of Wisconsin–Madison

  27 shared

• K C Kacir

  25 shared

Labs

• Traffic Operations and Safety (TOPS) LaboratoryPI

Awards & honors

• 2019 Institute of Transportation Engineers, Fellow
• 2017 American Society of Civil Engineers, Fellow
• 2016 College of Engineering, University of Wisconsin-Madison…
• 2014 Transportation Research Board, Outstanding Paper Award,…
• 2014 Transportation Research Board, Patricia F. Waller Award…