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The null hypothesis is then tested with the following calculation, where s? is the sample variance, n is the number of observations, and x is the sample mean:

Figure 2 The null hypothesis is then tested with the following calculation, where s? is the sample variance, n is the number of observations, and x is the sample mean:

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FIGURE 1 US-75 congested freeway corridor (FR = frontage road). The bottleneck under consideration forms at the convergence of US-75 NB and the entrance from SH190-GBT onto US-75 NB. At that point there are several possible reasons for bottleneck formation: May (/0) describes bottleneck formation as the point at which flow is no longer equal to demand but is limited by the roadway capac- ity. Intuitively, the flow at the bottleneck formation point, which is limited by the roadway capacity, is equal to the roadway capacity. Field-observed roadway capacity is estimated to be the observed queue discharged in 1 h immediately downstream of the bottleneck formation point on a single date during the evening peak period. The simulation attempts to replicate a single occurrence of congestion so
FIGURE 1 US-75 congested freeway corridor (FR = frontage road). The bottleneck under consideration forms at the convergence of US-75 NB and the entrance from SH190-GBT onto US-75 NB. At that point there are several possible reasons for bottleneck formation: May (/0) describes bottleneck formation as the point at which flow is no longer equal to demand but is limited by the roadway capac- ity. Intuitively, the flow at the bottleneck formation point, which is limited by the roadway capacity, is equal to the roadway capacity. Field-observed roadway capacity is estimated to be the observed queue discharged in 1 h immediately downstream of the bottleneck formation point on a single date during the evening peak period. The simulation attempts to replicate a single occurrence of congestion so
TABLE 1 Summary of Capacity Sensitivity to Parameter Modification
TABLE 1 Summary of Capacity Sensitivity to Parameter Modification
FIGURE 2 Simulated average capacity (with +SE) at four levels of CCO.
FIGURE 2 Simulated average capacity (with +SE) at four levels of CCO.
FIGURE 3 Simulated average capacity (with +SE) at four levels of CC1. Significant changes in capacity appear to result when the cali- brated value is modified in both directions. In this case, it appears that as CC2 increases, the average capacity of the corridor de- creases. The average capacity also increases significantly as CC2 decreases. These results are not unexpected. As CC2 gets smaller, it is implicit that the simulation driver’s following behavior is much more aggressive. This means that the driver will be speeding up and
FIGURE 3 Simulated average capacity (with +SE) at four levels of CC1. Significant changes in capacity appear to result when the cali- brated value is modified in both directions. In this case, it appears that as CC2 increases, the average capacity of the corridor de- creases. The average capacity also increases significantly as CC2 decreases. These results are not unexpected. As CC2 gets smaller, it is implicit that the simulation driver’s following behavior is much more aggressive. This means that the driver will be speeding up and
FIGURE 4 Simulated average capacity (with +SE) at four levels of CC2.
FIGURE 4 Simulated average capacity (with +SE) at four levels of CC2.
FIGURE 5 Simulated average capacity (with +SE) at four levels of CC4 and CC5.
FIGURE 5 Simulated average capacity (with +SE) at four levels of CC4 and CC5.
FIGURE 7 Simulated average capacity (with +SE) at four levels of look-back distance. However, this rule does not work in all cases. If the look-back dis- tance is too great, traffic will tend to bunch up on the right-hand side of the roadway far upstream of the exit ramp in an unrealistic man- ner. Gomes et al. (9) noted this aspect of look-back distance and suggested that look-back distance be calibrated carefully for each individual ramp. The low, medium, and high values of look-back dis- tance are 50, 500, and 1,000 ft (15.3, 153, and 305 m), respectively. The calibrated model look-back distance value is 300 ft (91.5 m). The resulting capacity of each is displayed in Figure 7. Look-back distance is defined as the distance upstream of a ramp or a section of roadway requiring some degree of maneuvering that a driver begins to position for the maneuver (/3), or more simply as the distance upstream that a vehicle will begin to attempt to change lanes. Look-back distance, in reality, is likely highly correlated with the amount of information provided the driver. Defining look-back
FIGURE 7 Simulated average capacity (with +SE) at four levels of look-back distance. However, this rule does not work in all cases. If the look-back dis- tance is too great, traffic will tend to bunch up on the right-hand side of the roadway far upstream of the exit ramp in an unrealistic man- ner. Gomes et al. (9) noted this aspect of look-back distance and suggested that look-back distance be calibrated carefully for each individual ramp. The low, medium, and high values of look-back dis- tance are 50, 500, and 1,000 ft (15.3, 153, and 305 m), respectively. The calibrated model look-back distance value is 300 ft (91.5 m). The resulting capacity of each is displayed in Figure 7. Look-back distance is defined as the distance upstream of a ramp or a section of roadway requiring some degree of maneuvering that a driver begins to position for the maneuver (/3), or more simply as the distance upstream that a vehicle will begin to attempt to change lanes. Look-back distance, in reality, is likely highly correlated with the amount of information provided the driver. Defining look-back
FIGURE 6 Simulated average capacity (with +SE) at four levels of CC8.
FIGURE 6 Simulated average capacity (with +SE) at four levels of CC8.
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