Browsing by Subject "Traffic data"

Now showing 1 - 6 of 6
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    Bayesian Methods for Estimating Average Vehicle Classification Volumes
    (Minnesota Department of Transportation, 1999-10) Davis, Gary A.; Yang, Shimin
    This report describes the development of a data-driven methodology for estimating the mean daily traffic (MDT) for different vehicle classes from short classification-count samples. Implementation of the methodology requires that an agency maintain a small number of permanent classification counters (PCC), whose output is used to estimate parameters describing their monthly and day-of-week variation patterns and covariance characteristics. The probability of a match between a short classification count sample and each of the PCCs is computed, as well as the estimates of the short-counts site's MDTs which would arise if the short-count site had variation patterns identical to each of the PCCs. The final MDT estimates are then simply the weighted averages of these component MDTs, with the matching probabilities providing the weights. Empirical evaluation of the methods using data collected at the Long Term Pavement Performance Project sites in Minnesota indicated that a reliable match of a short-count site could be made using a sample consisting of a one-day classification count from each month of the year. An evaluation of two-day classification count samples indicated that a two-day count is not sufficient to reliably match the site to a factor group, justifying estimation of MDT using weighted averages. For estimating combination vehicle MDT, these samples should be taken between May and October, and between Tuesday and Thursday. In this case the estimated MDT differed on average by about 10% - 12% compared to estimates based on full year's worth of counts, and differed by less than 26%, 95% of the time.
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    Development and Field Demonstration of DSRC Based V2V-Assisted V2I Traffic Information System for the Work Zone
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2012-06) Maitipe, Buddhika; Ibrahim, Umair; Hayee, M. Imran
    This report describes the architecture, functionality and the field demonstration results of a newly developed DSRC based V2I work zone traffic information system with V2V assistance. The developed system can automatically acquire important work zone travel information, e.g., the travel time (TT) and the starting location of congestion (SLoC), and relay them back to the drivers approaching the congestion site. Such information can help drivers in making informed decisions on route choice and/or preparing for upcoming congestion. Previously, we designed such a system using DSRC based V2I-only communication, which could not handle longer congestion lengths and the message broadcast range was also very limited. Our current system, on the other hand, can achieve much longer broadcast range (up to a few tens of kms), and can handle much longer congestion coverage length (up to a few kms) by incorporating DSRC based V2I communication with V2V assistance. The new system is also portable and uses only one RSU, which can acquire traffic data by engaging the vehicles traveling on the roadside whether within or outside of its direct wireless access range. From the traffic data, it estimates important traffic parameters, i.e., TT and SLoC, and periodically broadcasts them back to the vehicles approaching the congestion well before they enter the congested area. The results from the field demonstration have indicated that new system can adapt to dynamically changing work zone traffic environments and can handle much longer congestion lengths as compared to the previous system using V2I-only communication without V2V assistance.
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    Development of a Multiple-Camera Tracking System for Accurate Traffic Performance Measurements at Intersections
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2013-02) Tang, Hua
    Automatic traffic data collection can significantly save labor work and cost compared to manual data collection. However, automatic traffic data collection has been one of the challenges in Intelligent Transportation Systems (ITS). To be practically useful, an automatic traffic data collection system must derive traffic data with reasonable accuracy compared to a manual approach. This project presents the development of a multiple-camera tracking system for accurate traffic performance measurements at intersections. The tracking system sets up multiple cameras to record videos for an intersection. Compared to the traditional single-camera based tracking system, the multiple-camera one can take advantage of significantly overlapped views of the same traffic scene provided by the multiple cameras such that the notorious vehicle occlusion problem is alleviated. Also, multiple cameras provide more evidence of the same vehicle, which allows more robust tracking of the vehicle. The developed system has mainly three processing modules. First, the camera is calibrated for the traffic scene of interest and a calibration algorithm is developed for multiple cameras at an intersection. Second, the system tracks vehicles from the multiple videos by using powerful imaging processing techniques and tracking algorithms. Finally, the resulting vehicle trajectories from vehicle tracking are analyzed to extract the interested traffic data, such as vehicle volume, travel time, rejected gaps and accepted gaps. Practical tests of the developed system focus on vehicle counts and reasonable accuracy is achieved.
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    Estimation of Crossing Conflict at Signalized Intersection Using High-Resolution Traffic Data
    (Minnesota Department of Transportation, 2017-03) Liu, Henry X.; Davis, Gary A.; Shen, Shengyin; Di, Xuan; Chatterjee, Indrajit
    This project explores the possibility of using high-resolution traffic signal data to evaluate intersection safety. Traditional methods using historical crash data collected from infrequently and randomly occurring vehicle collisions can require several years to identify potentially risky situations. By contrast, the proposed method estimates potential traffic conflicts using high-resolution traffic signal data collected from the SMART-Signal system. The potential conflicts estimated in this research include both red-light running events, when stop-bar detectors are available, and crossing (i.e. right-angle) conflicts. Preliminary testing based on limited data showed that estimated conflict frequencies were better than AADT for predicting frequencies of angle crashes. With additional validation this could provide a low-cost and easy-to-use tool for traffic engineers to evaluate traffic safety performance at signalized intersections.
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    Research Implementation of the SMART SIGNAL System on Trunk Highway (TH) 13
    (Minnesota Department of Transportation, 2013-02) Liu, Henry X.; Zheng, Jianfeng; Hu, Heng; Sun, Jie
    In our previous research, the SMART-SIGNAL (Systematic Monitoring of Arterial Road Traffic and Signals) system that can collect event-based traffic data and generate comprehensive performance measures has been successfully developed by the University of Minnesota. In this research, a new set of interfaces are developed for SMART-SIGNAL system including new prototypes of data collection unit (DCU) and refined web-based user interface. To collect high resolution event-based traffic data including both vehicle detector actuation event and signal phase change event, two types of DCUs are designed, the TS-1 DCU and TS-2 DCU for corresponding traffic signal cabinet. TS-1 DCU connects with TS-1 cabinet using pin to pin interface, and the TS-2 DCU interfaces directly with SDLC bus within TS-2 cabinet. The DCUs uses high performance microcontroller modules, and are compact and easy to install. Both DCUs are designed to be vender independent add-on module for traffic cabinet, and can be used as flexible solution to enhance data collection by agencies. The refined web-based user interface features various performance measures to public users, such as Level of Service (LOS), queue length, travel time and intersection delays. The new set of interfaces have been deployed with the SMART-SIGNAL system at 13 intersections along Trunk Highway (TH) 13 in Burnsville, MN.
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    A Tracking-Based Traffic Performance Measurement System for Roundabouts and Intersections
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2012-05) Tang, Hua; Dinh, Hai
    Automatic traffic data collection can significantly save labor work and cost compared to manual data collection. The collected traffic data is necessary for traffic simulation and modeling, performance evaluation of the traffic scene, and eventually (re)design of the traffic scene. However, automatic traffic data collection has been one of the challenges in Intelligent Transportation Systems (ITS). This project presents the development of a single camerabased video system for automatic traffic data collection for roundabouts and intersections. The system targets roundabouts and intersections because no mature data collection systems exist for these traffic scenes yet in contrast to highway scenes. The developed system has mainly processing modules. First, the camera is calibrated for the traffic scene of interest and a novel circle-based calibration algorithm is proposed for roundabouts. Second, the system tracks vehicles from the video by incorporating powerful imaging processing techniques and tracking algorithms. Finally, the resulting vehicle trajectories from vehicle tracking are analyzed to extract the interested traffic data, which includes vehicle volume, vehicle speed (including acceleration/de-acceleration behavior), travel time, rejected gaps, accepted gaps, follow-up time and lane use. Practical tests of the developed system show that it can reliably track vehicles and provide reasonably accurate traffic data in most cases.