Understanding the Contributions of Operations, Technology, and Design to Meeting Highway Capacity Needs (2012) / Chapter Skim
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14 C h a p t e r 2 The four major methodological improvements described in this chapter dramatically increase the realism and sensitivity of existing dynamic traffic assignment tools. These improvements have been incorporated into two dynamic traffic assignment (DTA)
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15 Note: DTA = dynamic traffic assignment; MOE = measure of effectiveness.
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16 enhancement 1: Stochastic Capacity for Freeway Bottlenecks In this section, the stochastic nature of freeway breakdown and queue discharge is discussed through a comprehensive analysis of sensor data collected at bottleneck sites in the San Francisco Bay Area, California, and San Antonio, Texas. A new procedure is proposed to define the stochasticity of freeway breakdown and queue discharge based on time-indexed data of speed-flow profiles (1)
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17 Based on the three criteria, seven on-ramp bottlenecks (three with three travel lanes and four with four travel lanes) were selected as the study sample.
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18 of the specific study sites. The procedure for calculating these site-specific thresholds is described in the following paragraphs.
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19 equivalent flows and then aggregated into 15-minute prebreakdown headways (i.e., 3,600/flow rate)
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20 also indicates that the slope continually decreases with increasing values of time headway (i.e., decreasing flow rate)
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21 seven study sites. The corresponding parameters are shift = 1.5 seconds, µ = -0.97, and s = 0.68.
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22 lengths. The results indicate that there is a statistically significant relationship between Ct and Ct-1.
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23 represents the on-ramp demand and allocated capacity, while the y-axis represents the total freeway mainline demand and allocated capacity. The diagonal line represents the merge area -- or downstream freeway -- capacity (no served flows can be above that line)
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24 vehicles per hour and upstream of the on-ramp roadway at a rate of DR - CRamp vehicles per hour. Region III In Region III, the ramp demand exceeds one half the capacity of the freeway mainline, as shown in Figure 2.9.
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25 enhancement 2: Stochastic Capacity and turn pocket analysis on arterials Traffic flow along arterial street systems is affected by the operating characteristics of each individual approach along the arterial and system effects from upstream and downstream intersections (i.e., queue spillback or blockage)
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26 Traditional DTA models assume a constant saturation flow rate during the green time for either the approaching links or for individual turn movements at these locations. How ever, it is well known that the saturation flow rate for indi vidual links and turn movements varies significantly according to the behavior of individual drivers.
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27 standard deviation of the lognormal distribution for the two data sets remained in the same range as for the data set as a whole. On this basis, it was concluded that the lognormal distribution model would be applied to the mean saturation headway predicted by the DTA model with a standard deviation of 0.15.
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28 A clock-based simulation scheme is used in this study. The simulation time interval is denoted as DT, which should not be shorter than the shortest free-flow link travel time in the network (e.g., 6 seconds)
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29 In Figure 2.15a, four through vehicles occupy the downstream through pocket, so f T = true, while the left turning vehicle can still enter the left turn bay. Figure 2.15b shows how the through blockage event is triggered when the fifth through vehicle arrives at the gate.
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30 over a certain range, and a driver's traveling experience on a single day can be dramatically affected by the underlying realized capacity values on that particular day. In other words, travelers will experience different travel times on the same path over different days even for the same path flow pattern because of the inherent travel time variability introduced by stochastic capacity.
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31 travelers. In the above utility function, Equation 2.13, the travel time standard deviation (TSD)
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32 Multiple User Classes and Conceptual Route Choice Simulation Framework Three types of user classes are considered in this study: pretrip information users, en route information users, and unequipped users. The different user classes have access to different types of travel information to help them make their route choice decisions.
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33 • Number of cycles with a queue at the start of the red phase (exclusively for signalized links) ; and • Link capacity (veh/h/lane)
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34 Space mean speed (mph) for a linear corridor encompassing multiple links (i)
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35 Short-Lane Effect Modeling When the short-lane effects are modeled at signalized intersections, DYNASMART-P provides a promising alternative to microscopic simulation for analyzing short turn pocket effects. However, its mesoscopic platform does not model vehicle interactions or lane discipline, requiring some modifications in order to take into account the effects of blockage and spillback.
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36 macroscopic models are sensitive to one or more of these requirements, none accounts for the effects of pocket spillback in a comprehensive manner. references 1.
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