992 resultados para traffic signals
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This paper investigates the platoon dispersion model that is part of the 2010 Highway Capacity Manual that is used for forecasting downstream traffic flows for analyzing both signalized and TWSC intersections. The paper focuses on the effect of platoon dispersion on the proportion of time blocked, the conflicting flow rate, and the capacity flow rate for the major street left turn movement at a TWSC intersection. The existing HCM 2010 methodology shows little effect on conflicting flow or capacity for various distances downstream from the signalized intersection. Two methods are suggested for computing the conflicting flow and capacity of minor stream movements at the TWSC intersection that have more desirable properties than the existing HCM method. Further, if the existing HCM method is retained, the results suggest that the upstream signals model be dropped from the HCM method for TWSC intersections.
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Invasive species are known to affect native species in a variety of ways, but the effect of acoustic invaders has not been examined previously. We simulated an invasion of the acoustic niche by exposing calling native male white-banded tree frogs (Hypsiboas albomarginatus) to recorded invasive American bullfrog (Lithobates catesbeianus) calls. In response, tree frogs immediately shifted calls to significantly higher frequencies. In the post-stimulus period, they continued to use higher frequencies while also decreasing signal duration. Acoustic signals are the primary basis of mate selection in many anurans, suggesting that such changes could negatively affect the reproductive success of native species. The effects of bullfrog vocalizations on acoustic communities are expected to be especially severe due to their broad frequency band, which masks the calls of multiple species simultaneously.
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n recent years, the development of advanced driver assistance systems (ADAS) – mainly based on lidar and cameras – has considerably improved the safety of driving in urban environments. These systems provide warning signals for the driver in the case that any unexpected traffic circumstance is detected. The next step is to develop systems capable not only of warning the driver but also of taking over control of the car to avoid a potential collision. In the present communication, a system capable of autonomously avoiding collisions in traffic jam situations is presented. First, a perception system was developed for urban situations—in which not only vehicles have to be considered, but also pedestrians and other non-motor-vehicles (NMV). It comprises a differential global positioning system (DGPS) and wireless communication for vehicle detection, and an ultrasound sensor for NMV detection. Then, the vehicle's actuators – brake and throttle pedals – were modified to permit autonomous control. Finally, a fuzzy logic controller was implemented capable of analyzing the information provided by the perception system and of sending control commands to the vehicle's actuators so as to avoid accidents. The feasibility of the integrated system was tested by mounting it in a commercial vehicle, with the results being encouraging.
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In this paper an on line self-tuned PID controller is proposed for the control of a car whose goal is to follow another one, at distances and speeds typical in urban traffic. The bestknown tuning mechanism is perhaps the MIT rule, due to its ease of implementation. However, as it is well known, this method does not guarantee the stability of the system, providing good results only for constant or slowly varying reference signals and in the absence of noise, which are unrealistic conditions. When the reference input varies with an appreciable rate or in presence of noise, eventually it could result in system instability. In this paper an alternative method is proposed that significantly improves the robustness of the system for varying inputs or in the presence of noise, as demonstrated by simulation.
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This paper describes a knowledge model for a configuration problem in the do-main of traffic control. The goal of this model is to help traffic engineers in the dynamic selection of a set of messages to be presented to drivers on variable message signals. This selection is done in a real-time context using data recorded by traffic detectors on motorways. The system follows an advanced knowledge-based solution that implements two abstract problem solving methods according to a model-based approach recently proposed in the knowledge engineering field. Finally, the paper presents a discussion about the advantages and drawbacks found for this problem as a consequence of the applied knowledge modeling ap-proach.
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One pathway in forming synaptic-like microvesicles (SLMV) involves direct budding from the plasma membrane, requires adaptor protein 2 (AP2) and is brefeldin A (BFA) resistant. A second route leads from the plasma membrane to an endosomal intermediate from which SLMV bud in a BFA-sensitive, AP3-dependent manner. Because AP3 has been shown to bind to a di-leucine targeting signal in vitro, we have investigated whether this major class of targeting signals is capable of directing protein traffic to SLMV in vivo. We have found that a di-leucine signal within the cytoplasmic tail of human tyrosinase is responsible for the majority of the targeting of HRP-tyrosinase chimeras to SLMV in PC12 cells. Furthermore, we have discovered that a Met-Leu di-hydrophobic motif within the extreme C terminus of synaptotagmin I supports 20% of the SLMV targeting of a CD4-synaptotagmin chimera. All of the traffic to the SLMV mediated by either di-Leu or Met-Leu is BFA sensitive, strongly suggesting a role for AP3 and possibly for an endosomal intermediate in this process. The differential reduction in SLMV targeting for HRP-tyrosinase and CD4-synaptotagmin chimeras by di-alanine substitutions or BFA treatment implies that different proteins use the two routes to the SLMV to differing extents.
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Texas Department of Transportation, Research and Technology Transfer Office, Austin
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Federal Highway Administration, Office of Implementation, Washington, D.C.
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Federal Highway Administration, Office of Implementation, Washington, D.C.
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Federal Highway Administration, Office of Research and Development, Washington, D.C.
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Texas State Department of Highways and Public Transportation, Transportation Planning Division, Austin
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Texas State Department of Highways and Public Transportation, Transportation Planning Division, Austin
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Transportation Department, Office of University Research, Washington, D.C.
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Federal Highway Administration, Washington, D.C.