Browsing by Author "Sangster, John"

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    Naturalistic Driving Data for the Analysis of Car-following Models
    Rakha, Hesham A.; Sangster, John; Du, Jianhe (United States. Department of Transportation, 2013-02-21)
    This report presents two research efforts that have been published as conference papers through the Transportation Research Board Annual Meeting, the first of which is under review for journal publication. The first research effort investigates the general application of naturalistic driving data to the modeling of car following behavior. The driver-specific data available from naturalistic driving studies provides a unique perspective from which to test and calibrate car-following models. As equipment and data storage costs continue to decline, the collection of data through in situ probe-type vehicles is likely to become more popular, and thus there is a need to assess the feasibility of these data for the modeling of driver car-following behavior. The first research effort seeks to focus on the costs and benefits of naturalistic data for use in mobility applications. Any project seeking to utilize naturalistic data should plan for a complex and potentially costly data reduction process to extract mobility data. A case study is provided using the database from the 100-Car Study, conducted by the Virginia Tech Transportation Institute. One thousand minutes worth of data comprised of over 2,000 car-following events recorded across eight drivers is compiled herein, from a section of multilane highway located near Washington, D.C. The collected event data is used to calibrate four different car following models, and a comparative analysis of model performance is conducted. The results of model calibration are given in tabular format, displayed on the fundamental diagram, and shown with sample event charts of speed-vs.-time and headway-vs.-time. The authors demonstrate that the Rakha-Pasumarthy-Adjerid model performs best both in matching individual drivers and in matching aggregate results, when compared with the Gipps, Intelligent Driver, and Gaxis-Herman-Rothery models. The second effort examines how insights gained from naturalistic data may serve to improve existing car following models. The research presented analyzes the simplified behavioral vehicle longitudinal motion model, currently implemented in the INTEGRATION software, known as the Rakha-Pasumarthy-Adjerid (RPA) model. This model utilizes a steady-state formulation along with two constraints, namely: acceleration and collision avoidance. An analysis of the model using the naturalistic driving data identified a deficiency in the model formulation, in that it predicts more conservative driving behavior compared to naturalistic driving. Much of the error in simulated car-following behavior occurs when a car-following event is initiated. As a vehicle enters the lane in front of a subject vehicle, the spacing between the two vehicles is often much shorter than is desired; the observed behavior is that, rather than the following vehicle decelerating aggressively, the following vehicle coasts until the desired headway/spacing is achieved. Consequently, the model is enhanced to reflect this empirically observed behavior. Finally, a quantitative and qualitative evaluation of the original and proposed model formulations demonstrates that the proposed modification significantly decreases the modeling error and produces car-following behavior that is consistent with empirically observed driver behavior.
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    Operational Analysis of Alternative Intersections
    Sangster, John (Virginia Tech, 2015-09-09)
    Alternative intersections and interchanges, such as the diverging diamond interchange (DDI), the restricted crossing u-turn (RCUT), and the displaced left-turn intersection (DLT), have the potential to both improve safety and reduce delay. However, partially due to lingering questions about analysis methods and service measures for these designs, their rate of implementation remains low. This research attempts to answer three key questions. Can alternative intersections and interchanges be incorporated into the existing level of service and service measure schema, or is a new service measure with an updated level of service model required? Is the behavior of drivers at alternative intersections fundamentally similar to those at conventional intersections, such that traffic microsimulation applications can accurately model the behaviors observed in the field? Finally, is the planning level tool made available through FHWA an accurate predictor of the relative performance of various alternatives, or is an updated tool necessary? Discussion and case study analysis are used to explore the existing level of service and service measure schema. The existing control delay measure is recommended to be replaced with a proposed junction delay measure that incorporates geometric delay, with the existing level of service schema based on control type recommended to be replaced by a proposed schema using demand volume. A case study validation of micro- and macroscopic analysis methods is conducted, finding the two microscopic methods investigated to match field observed vehicle delays within 3 to 7 seconds for all designs tested, and macroscopic HCM method matching within 3 seconds for the DDI, 35 seconds for the RCUT, and 130 seconds for the DLT design. Taking the critical lane analysis method to be a valid measure of operations, the demand-volume limitations of each alternative design is explored using eighteen geometric configurations and approximately three thousand volume scenarios, with the DLT design predicted to accommodate the highest demand volumes before failure is reached. Finally, six geometries are examined using both the planning-level tool and the validated microsimulation tool, finding that the curve of the capacity-to-delay relationship varies for each alternative design, invalidating the use of critical lane analysis as a comparative tool.