954 resultados para Traffic Flow Modeling.


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Federal Highway Administration, Washington, D.C.

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National Highway Traffic Safety Administration, Office of Research and Development, Washington, D.C.

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"September 1990."

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v.1 Transportation needs.--v.2. Traffic flow tables.--v.3. Charts and plates.--v.4. Parking survey of the downtown area, Baltimore City.

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"September 1982."

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Vols. for <1983>- prepared in cooperation with the U.S. Dept. of Transportation, Federal Highway Administration.

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Mode of access: Internet.

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Mode of access: Internet.

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Includes highway department organization; road systems of Arizona, including interstate highways; financing and cost, road mileage, population changes, passenger car speeds, traffic flow, traffic accidents, motor vehicle registration, and sufficiency ratings.

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"To be presented before the American Electric Railway Transportation and Traffic Association convention held at San Francisco, Cal., June 23 to 26, 1930."

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Thesis (Ph.D.)--University of Washington, 2016-06

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Thesis (Master's)--University of Washington, 2016-06

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Microscopic traffic-simulation tools are increasingly being applied to evaluate the impacts of a wide variety of intelligent transport, systems (ITS) applications and other dynamic problems that are difficult to solve using traditional analytical models. The accuracy of a traffic-simulation system depends highly on the quality of the traffic-flow model at its core, with the two main critical components being the car-following and lane-changing models. This paper presents findings from a comparative evaluation of car-following behavior in a number of traffic simulators [advanced interactive microscopic simulator for urban and nonurban networks (AIMSUN), parallel microscopic simulation (PARAMICS), and Verkehr in Statiten-simulation (VISSIM)]. The car-following algorithms used in these simulators have been developed from a variety of theoretical backgrounds and are reported to have been calibrated on a number of different data sets. Very few independent studies have attempted to evaluate the performance of the underlying algorithms based on the same data set. The results reported in this study are based on a car-following experiment that used instrumented vehicles to record the speed and relative distance between follower and leader vehicles on a one-lane road. The experiment was replicated in each tool and the simulated car-following behavior was compared to the field data using a number of error tests. The results showed lower error values for the Gipps-based models implemented in AIMSUN and similar error values for the psychophysical spacing models used in VISSIM and PARAMICS. A qualitative drift and goal-seeking behavior test, which essentially shows how the distance headway between leader and follower vehicles should oscillate around a stable distance, also confirmed the findings.

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B-ISDN is a universal network which supports diverse mixes of service, applications and traffic. ATM has been accepted world-wide as the transport technique for future use in B-ISDN. ATM, being a simple packet oriented transfer technique, provides a flexible means for supporting a continuum of transport rates and is efficient due to possible statistical sharing of network resources by multiple users. In order to fully exploit the potential statistical gain, while at the same time provide diverse service and traffic mixes, an efficient traffic control must be designed. Traffic controls which include congestion and flow control are a fundamental necessity to the success and viability of future B-ISDN. Congestion and flow control is difficult in the broadband environment due to the high speed link, the wide area distance, diverse service requirements and diverse traffic characteristics. Most congestion and flow control approaches in conventional packet switched networks are reactive in nature and are not applicable in the B-ISDN environment. In this research, traffic control procedures mainly based on preventive measures for a private ATM-based network are proposed and their performance evaluated. The various traffic controls include CAC, traffic flow enforcement, priority control and an explicit feedback mechanism. These functions operate at call level and cell level. They are carried out distributively by the end terminals, the network access points and the internal elements of the network. During the connection set-up phase, the CAC decides the acceptance or denial of a connection request and allocates bandwidth to the new connection according to three schemes; peak bit rate, statistical rate and average bit rate. The statistical multiplexing rate is based on a `bufferless fluid flow model' which is simple and robust. The allocation of an average bit rate to data traffic at the expense of delay obviously improves the network bandwidth utilisation.