| 2. |
- Gómez, Radhameris A., et al.
(författare)
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An evaluation of traffic control devices and driver distraction on driver behavior at railway-highway grade crossings
- 2016
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Konferensbidrag (refereegranskat)abstract
- The U.S. railroad system is made up of over 700 railroads operating on 140,000 miles of track, with more than 130,000 at-grade public railway-highway crossings, or intersections in which any part of a roadway intersects with railroad tracks. Safety at these railroad-highway grade crossings and the related traffic control devices used to communicate with drivers is of major concern. There are three factors that influence a driver’s behavior at a given crossing. First, traffic control devices, including warning devices at the railway-highway at-grade crossings, provide the driver with information. Second, assuming that the driver identifies the warning, the driver’s prior knowledge influences his or her expectancy regarding various railroad-highway grade crossing situations and, therefore, the way in which the driver responds to the hazard presented by the crossing. Finally, the driver’s own physiological (e.g., impaired) and psychological (e.g., distracted) state will modify the role that conspicuity and expectancy have on the driver’s behavior In order to address the gap that exists in our understanding of driver distraction at railway-highway at-grade crossings, two driving simulator experiments were conducted to determine the role that distraction has on the effectiveness of warning devices at grade crossings. In the first experiment, the role of distraction in reducing the benefit of typical crossbucks and flashing lights is evaluated. Participants were either engaged or not engaged in a distracting task, which included either a mock cell phone conversation or an in-vehicle task. Based upon the fact that distractions reduce the advantage of flashing lights and crossbucks, an evaluation of an alternative treatment that might be used to warn drivers of an approaching train was conducted. The question being addressed is whether these alternative markings, known as dynamic envelope pavement markings are an effective application for mitigating the effects of driver distraction at at-grade railway-highway grade crossings. Initial findings point to the fact that even when flashing lights and its respective signage and markings are properly working, a distracted driver is more likely to ignore the cues.
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| 3. |
- Tang, Yue, et al.
(författare)
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A comparative study of the application of the standard kernel density estimation and network kernel density estimation in crash hotspot identification
- 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
- Despite a growing number of studies have claimed the network Kernel Density Estimation (network KDE) a more advanced method for crash hotspot identification than the planar Kernel Density Estimation (planar KDE), few conducted comprehensive study to examine their accuracy and practicality on a large-scale basis (i.e. municipal and county). This research attempted to fill the gap by conducting a comparative study of planar KDE and network KDE using the crash data of Hampden County, Massachusetts from 2009 to 2011. A two-tier planar KDE and a network KDE were implemented using the Kernel Density tool in ESRI ArcGIS 10 and SANET 4.1 developed at University of Tokyo respectively. Results showed that (1) Planar KDE is computationally inexpensive and easily accessed. (2) Both methods yielded virtually similar hotspot patterns but with different rankings of the high crash locations. (3) In identifying specific hotspot locations, network KDE could achieve more accurate results and was more timesaving, although multiple runs of planar KDE identified specific locations as well. Accordingly, several suggestions were made for crash hotspot analysis: (1) Since KDE takes the interrelationship among crashes into consideration, it is a more statistically sound approach than traditional methods in crash hotspot identification and can be widely adopted by state and local agencies for initiating safety improvement projects. (2) Planar KDE is recommended to identify general hotspot patterns on large-scale basis for its practicality and efficiency. (3) Network KDE is recommended to identify specific intersections and roadway segments for accuracy.
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