Browsing by Author "Stern, Raphael"

Now showing 1 - 10 of 10
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    Air travel data during the COVID-19 pandemic in the United States
    (2020-11-25) Shang, Mingfeng; Pham, Joseph; Vrabac, Damir; Butler, Brooks; Paré, Philip E; Stern, Raphael; rstern@umn.edu; Stern, Raphael; University of Minnesota Transportation Cyber-Physical Systems Lab
    This dataset contains flight data for all commercial flights in the Northeastern US during the COVID-19 pandemic, as well as code to calibrate and simulate an SEIR model that incorporates the flight data into the transmission process.
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    Assessing the Energy Impacts of Cyberattacks on Low-Level Automated Vehicles
    (Center for Transportation Studies, University of Minnesota, 2023-08) Stern, Raphael; Li, Tianyi; Rosenblad, Benjamin; Shang, Mingfeng
    In this study, we investigate the potential impact of stealthy cyberattacks on automated or partially automated vehicles, and consider how they will influence traffic flow and fuel consumption. Specifically, we define stealthy cyberattacks on automated vehicles where driving behavior deviates only slightly from normal driving behavior. We use simulation analysis to consider different cyberattacks, and investigate their impact on traffic flow and aggregate fuel consumption of all vehicles in the traffic flow. We find that such attacks, while difficult to detect, may substantially degrade traffic flow, and, to a lesser extent, vehicle emissions across the traffic flow.
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    Driver Comprehension of Flashing Yellow Arrows
    (Minnesota Department of Transportation, 2023-12) Davis, Gary A.; Stern, Raphael; Duhn, Melissa; Gao, Jingru
    In 2009, the FHWA's Manual on Uniform Traffic Control Devices (MUTCD) introduced the flashing yellow arrow (FYA) traffic signal as an alternative to circular green (CG) to indicate permitted left turns. The FYA is arguably a more intuitive indication that left turns are permitted but not protected and, in addition, the FYA signal heads can support time-of-day changes between protective and permissive left -turn phasing. In 2019, a Research Needs Statement stated that "Research is needed to examine driver comprehension of flashing yellow arrows in different light arrangements and the role of signage." Our objective in this project was to assess drivers' understanding of FYA signal indications and to see if the presence or absence of "Left Turn Yield" signs affect gap acceptance. This was accomplished by conducting an online survey of drivers regarding their understanding of FYA signals and by carrying out a field study of drivers' gap acceptance at a set of Twin Cities intersections.
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    Efficient Pedestrian and Bicycle Traffic Flow Estimation Combining Mobile-Sourced Data with Route Choice Prediction
    (2023-12-08) Barman, Simanta; Stern, Raphael; Levin, Michael W.; Lindsey, Greg; rstern@umn.edu; Stern, Raphael; University of Minnesota CAVe Lab
    Accurate estimate of traffic flow measures like annual average daily traffic (AADT) is vital to making decisions about roadway planning, safety, maintenance, operation etc. Methodology to inexpensively obtain an accurate estimate of traffic flow especially for pedestrian and bicyclist traffic is lacking in the literature. High expenses of conducting household surveys and setting up traffic monitoring stations to collect data motivated us to look for cheaper solutions. In this study, we develop a methodology to inexpensively obtain a good estimate of pedestrian and bicyclist traffic flow from mobile data sources while avoiding the privacy issues associated with models based on household survey data. However, the accuracy of mobile data is unknown and may vary in different locations. To deal with erroneous data sources we use different techniques to estimate and keep improving an origin-destination (OD) matrix from the observed link flows to ultimately get the actual link flows. In our model, we enforce the consistency between the number of productions and attractions of trips for different regions with the OD-matrix. Using the network topology, we use trip distribution based on the gravity model to generate an initial OD-matrix. Then we use an optimization formulation to improve the initial OD-matrix so that the link flow obtained using the improved OD-matrix matches with the partially observable link flows. Furthermore, we present the performance of the solution algorithm for the Twin Cities' bicycle and pedestrian networks. We also compare the accuracy of our estimate with manually collected traffic flow data for the real networks.
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    Guidelines for safer pedestrian crossings: Understanding the factors that positively influence vehicle yielding to pedestrians at unsignalized intersections
    (Minnesota Department of Transportation, 2023-06) Stern, Raphael; Li, Tianyi
    Many factors influence an individual driver's decision to yield or not yield to individual pedestrians attempting to cross the road at an unsignalized crossing. This study collects observational data from more than 3,300 crossing events at 18 intersections in Minnesota to further our understanding of what factors positively influence driver yielding. Using the collected data, a statistical analysis was conducted to identify features that most strongly correlate with driver yielding. Event specific features such as speed were found to greatly influence yielding, with vehicles traveling at a speed of greater than 25 mph significantly less likely to yield to pedestrians than vehicles traveling at speeds lower than 25 mph. Site-specific features such as the presence of signs indicating a crossing were also strongly correlated with driver yielding. The results provide indication of which features of unsignalized crossings correlate with higher driver yielding rates. These findings can be used to guide policy and design at sites where a high driver yielding rate is desirable.
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    Identifying Deer Vehicle Collision Concentrations in Minnesota
    (Minnesota Department of Transportation, 2023-11) Stern, Raphael; Moen, Ron; Zare, Arian; Bober, Marissa
    Deer-vehicle collisions (DVCs) represent a significant hazard on Minnesota roads, with roughly 1,200 DVCs reported annually to the Minnesota Department of Public Safety (MnDPS) and many more going unreported. While DVCs are common across Minnesota, local variations in deer density as well as roadway characteristics and use patterns make DVCs more likely to occur on some roadways than others. Moreover, the true extent of DVC concentrations is unclear due to the high proportion of DVCs that go unreported. This report presents findings from research that (1) uses data to identify areas of DVC concentration based on the specific roadway characteristics and (2) presents a methodology to estimate DVC reporting rate across the state. This methodology is applied in a pilot study in the Duluth area, as well as in an extended search area that includes highways spread across much of outstate Minnesota to estimate the DVC reporting rate.
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    Mobile-device data, non-motorized traffic monitoring, and estimation of annual average daily bicyclist and pedestrian flows
    (Minnesota Department of Transportation, 2024-06) Barman, Simanta; Levin, Michael W.; Lindsey, Greg; Petesch, Michael; Scotty, Suzy; Stern, Raphael
    People who walk and bike are the most vulnerable road users. However, understanding where they walk and bike requires continual data monitoring. Traditional methods rely on physical sensors in the infrastructure to detect the presence of pedestrians and bicyclists. However, these are expensive to deploy and only detect road users at the specific locations they are deployed. Instead, this study develops methods to use mobile phone based GPS data to estimate the number of bicyclists and pedestrians, and applies this methodology to the Twin Cities Metro area in Minnesota. The developed methodology is able to estimate average pedestrian and bicyclist volumes with relatively high accuracy.
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    Modeling stochastic human-driver car following behavior in oscillatory traffic conditions
    (Center for Transportation Studies, University of Minnesota, 2021-08) Stern, Raphael; Shang, Mingfeng
    Accurately modeling the realistic and unstable traffic dynamics of human-driven traffic flow is crucial to being able to understand how traffic dynamics evolve, and how new agents such as autonomous vehicles might influence traffic flow stability. This work is motivated by a recent dataset that allows us to calibrate accurate models, specifically in conditions when traffic waves arise. Three microscopic car-following models are calibrated using a microscopic vehicle trajectory dataset that is collected with the intent of capturing oscillatory driving conditions. For each model, five traffic flow metrics are constructed to compare the flow-level characteristics of the simulated traffic with experimental data.
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    Toward implementation of max-pressure control on Minnesota roads: Phase 2
    (Minnesota Department of Transportation, 2024-10) Stern, Raphael; Levin, Michael W.; Kiani, Amirhossein
    Max-pressure (MP) traffic signal control is a new and innovative control algorithm that uses upstream and downstream vehicle counts to determine signal timing that maximizes throughput. While this method has been extensively tested in simulation, it has not yet been tested on actual traffic signals in the US. To close this gap, this report presents the results of the development of a hardware-in-the-loop traffic signal testbed where microsimulation is used to simulate realistic traffic conditions, and the MP algorithm is used to control the signal display using a traffic controller (Q-Free MaxTime controller). The hardware-in-the-loop results demonstrate that MP can be safely deployed on North American traffic signal control hardware.