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- Li, Zhixia, et al.
(författare)
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Calibrating VISSIM roundabout model using a critical gap and follow-up headway approach
- 2013
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Ingår i: Proceedings of the 16th International Conference Road Safety on Four Continents. - Linköping : Statens väg- och transportforskningsinstitut.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Roundabouts have been continuously constructed in the U.S. in recent years, as studies have shown their capability of reducing crash risk and severity when compared with signalized intersections. Despite of the safety benefits offered by roundabouts, operational efficiency is required be analyzed when considering building roundabouts. As a prevailing simulation platform for modeling roundabouts, VISSIM have been widely applied in practice to facilitate analyzing the operational performance of roundabouts. Considering that an essential prerequisite to preparing a VISSIM roundabout model is to calibrate the model by adjusting VISSIM parameters, comprehensive calibration guidance is of great importance to practitioners. Previous calibration research has conducted qualitative analysis to study the impact of VISSIM parameters on roundabout capacity. However, parameter values based on field data and quantitative calibration guidelines are more helpful to facilitate fast and accurate modeling of roundabouts. This paper addresses these important needs. Speed trajectories of free-flow entering vehicles were collected in the field using a radar sensor. Location, length, speed distribution, and deceleration rate parameters for the VISSIM Reduced Speed Areas (RSA) were determined through the analysis of the radar data. The impact of VISSIM parameters on critical gap and follow-up headway was quantitatively analyzed through sensitivity analysis of minimum gap for PR, speed distribution and deceleration rate for RSA, and additive and multiplicative settings for the Wiedemann 74 model. Numerical guidelines for calibrating VISSIM roundabout models were ultimately developed, and validated via a case study.
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| 2. |
- Zheng, Dongxi, et al.
(författare)
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Watch the traffic signal : a signal state extraction system for naturalistic driving videos
- 2016
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Konferensbidrag (refereegranskat)abstract
- Naturalistic driving studies (NDS) are revolutionizing road safety research. Naturalistic driving data provide a new window into driver behavior that promises a deeper understanding than was ever possible with crash data, roadside observations, or driving simulator experiments. NDS collect extensive vehicle network data, vehicle internal videos (e.g., drivers and passengers), and vehicle external videos (e.g., forward roadway). The video record of the driver and surrounding road situation often provide a more revealing account of driver behavior. Data size becomes a double edged sword for most NDS. Fully or partially automated procedures are needed for data reduction. Both intentional and unintentional violations of traffic signals can cause severe consequences, like fatal right-angle crashes. Knowing the signal state when the driver navigated through the intersection is the first step towards judging the driver’s compliance and analyzing deeper human factors issues. A system that codes traffic signal state from georeferenced front-view videos was developed for use with the Second Strategic Highway Research Program (SHRP2) NDS data. GPS coordinates and a free online map database are used to identify candidate frames from lengthy videos for computer vision processing. The computer vision algorithm uses color histograms and shape matching to detect traffic signals. Vehicle movement is used to select, from multiple signals the one corresponding to the vehicle’s traffic lane. Temporal relationship between frames is employed for the purpose of refining detection results. Experiments were conducted on daytime videos and showed reasonable performance given the severe challenges posed by the SHRP2 data. Misclassifications were primarily due to other vehicles’ taillights, reddish yellow signals, yellowish red signals, green traffic signs, etc. Misses were due to distant frames with very few pixels corresponding to the signals and reduced color conspicuity compared to background scenes (e.g., green signals and the sky look similar). The system is an insightful first step towards using computer vision techniques to support signal state coding of the large volumes of naturalistic driving video data. Challenges revealed by the experimental results are valuable knowledge for future improvement.
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