Incorporating Travel Time Reliability into the Highway Capacity Manual (2014) / Chapter Skim
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Pages 80-106
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From page 80... ...
80 C h a p t e r 6 This chapter discusses the enhancements that have been made to the FREEVAL and STREETVAL models during this project. It is divided into the following five sections: • Freeway facilities introduction; • Description of freeway facility enhancements; • FREEVAL-RL calibration; • Summary of freeway model enhancements; and • Urban streets enhancements.
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81 sometimes results in unrealistic speed estimates and inconsistent results, as segment speeds may actually increase when adding a CAF. In Project L08, the enhancements directly incorporate SAF and CAF into the respective procedure for each segment type and thus consistently account for the particular segment characteristics.
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82 reduction around 7%. This finding is often referred to as the two-capacity phenomenon.
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83 also increases queue formation shock wave speed and decreases the queue dissipation speed. In past research, even a 5% drop in capacity has been shown to result in an approximate 80% increase in queue length and 40% increase in travel time for a simulated test facility.
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84 Consideration of CAF and SAF for Other Segment Types The equation for CAF and SAF described in the previous section is ultimately intended for application to basic freeway segments. However, in the HCM2000 and HCM2010, it was also applied to the analysis of merge, diverge, and weaving segments with CAFs less than 1.0.
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85 SO = average speed of vehicles in outer lanes of the freeway, adjacent to the 1,500-ft ramp influence area, mph; S = average speed of all vehicles in all lanes within the 1,500-ft length covered by the ramp influence area, mph; FFS = free-flow speed of the freeway, mph; SAF = segment speed adjustment factor of the ramp segment; SFR = free-flow speed of the ramp, mph; LA = length of acceleration lane, ft; vR = demand flow rate on ramp, pcph; v12 = demand flow rate in lanes 1 and 2 of the freeway upstream of the ramp influence area; vR12 = total demand flow rate entering the on-ramp influence area, including v12 and vR, pcph; vOA = average per-lane demand flow in outer lanes adjacent to the ramp influence area (not including flow in lanes 1 and 2) , pcphpl; Ms = speed index for on-ramps (merge areas)
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86 the speed calculation procedure for weave segments is modified to consider weather and incident reductions in free-flow speed, through the use of SAFs. The method separately estimates the speed of weaving and nonweaving vehicles, which are eventually combined to estimate a space mean speed of all vehicles in the segment.
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87 New Defaults for CAF and SAF The research team performed an extensive literature review on the impacts of incidents and weather events on both segment free-flow speed and capacity. Summaries are presented in Table 6.4 and Table 6.5.
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88 doing so is expected to be used to model highly oversaturated conditions. To facilitate such cases, new performance measures have been developed.
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89 scenario. The saved output from each run can be categorized as follows: • Scenario description; • Analysis period detailed performance measures; • Speed contour in the time–space domain; and • Overall result summary sheet.
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90 on- and off-ramps for a 2-week period were used to supplement the data because the ramps have no permanent sensors. Sidefire sensor data were collected for all of 2010 at the 15-min level; daily per-lane volumes were calculated at each sensor to determine combinations of days and months that operated similarly.
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91 Table 6.6. Demand Factors: Ratio of ADT to AADT by Month and Day of Week Month Sunday Monday Tuesday Wednesday Thursday Friday Saturday January 0.617609 0.999005 1.030232 1.042881 1.055117 1.084198 0.662407 February 0.763747 0.941499 1.013144 1.041699 1.094640 1.142797 0.837179 March 0.794913 1.045799 1.071891 1.066066 1.113577 1.173921 0.940873 April 0.817347 1.076144 1.090055 1.100863 1.164751 1.217906 0.911421 May 0.815670 1.078904 1.108827 1.116618 1.160484 1.213328 0.933496 June 0.805796 1.080620 1.088449 1.070022 1.141443 1.183148 0.942226 July 0.764001 1.085168 1.073553 1.105148 1.150022 1.187813 0.933042 August 0.801063 1.048545 1.054661 1.062905 1.095856 1.167686 0.911527 September 0.768024 1.018452 1.026499 1.026072 1.077352 1.155702 0.893950 October 0.825240 1.051489 1.048223 1.069537 1.109691 1.163729 0.924886 November 0.756585 0.976373 1.002337 1.043700 1.084126 1.072912 0.829501 December 0.586780 0.977116 0.958762 0.989379 0.918297 1.010103 0.744283 Figure 6.7.
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92 case, the improbable and zero-probability detailed scenarios were removed from the reliability analysis. That translates to an inclusion threshold of near zero, meaning all scenarios with probability greater than zero were included in the analysis.
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93 Summary of Freeway Model enhancements The enhancements to the FREEVAL-RL computational engine include the following: • Incorporating the two-capacity phenomenon under queue discharge conditions; • Incorporating SAFs for certain nonrecurring congestion sources; • Improving modeling of CAFs and SAFs for merge, diverge, and weaving segments; • Adding new defaults for CAFs and SAFs for incidents and weather events on freeways; • Extending performance measures for congested conditions; and • Automating computation. The output of the enhanced computational engine is consistent with the HCM.
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94 Figure 6.9. Mid-segment work zone impacts.
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95 evaluation module is shown in Figure 6.11. The module comprises eight procedures.
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96 capacity. If the factor has been set to a value less than 1.0 in a previous iteration, then the factor continues to be adjusted with each subsequent iteration until convergence is achieved.
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97 than Lh. This observation will always be true when the segment length is sufficiently long that the stopping wave does not reach the upstream signal before the onset of the next green indication.
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98 delay of 2.0 s. The shorter response time of the second and subsequent queued drivers is likely due to their ability to anticipate the time to initiate motion by seeing the signal change and/or the movement of vehicles ahead.
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99 Chapter 30. The result will be a more reliable estimate of the time until spillback.
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100 Therefore, the HCM2010 methodology is again used to evaluate the facility. The analysis period is 0.15 h (= 0.25 - 0.10)
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101 one segment is considered a site. Each site is checked in this step.
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102 input variables remain unchanged. Then, the HCM2010 methodology is implemented to evaluate the facility.
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103 intersection. The movements of interest are those that enter the subject segment.
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104 This distance is computed using the equations described in Step 5. Any difference between the predicted and maximum queues is considered a prediction error.
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105 Table 6.9. Segment Description Segment Location Street Class Segment Length (ft)
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106 Table 6.10. Segment Traffic and Signalization Characteristics Segment Travel Direction Average Volumea (veh/h)
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