Analytical Procedures for Determining the Impacts of Reliability Mitigation Strategies (2012) / Chapter Skim
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From page 80... ...
80 C h a p t e r 5 Introduction The objective of this chapter is to describe in detail the factors that cause congestion, with the specific intent of helping agencies respond cost-effectively to reduce the formation of congestion. The results of a series of analyses that examined the causes of freeway congestion, first in Atlanta, then in greater detail in the Seattle metropolitan region, are discussed.
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From page 81... ...
81 a Closer Look at Congestion by Source: Seattle Background Analysis Overview To examine some of the issues raised in the preliminary Atlanta analysis, a detailed analysis was conducted using data from Seattle. This effort used measured roadway performance data (volumes and travel times taken every 5 minutes)
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From page 82... ...
82 Factors Affecting Congestion Given that congestion occurs when there is too much volume and too little roadway capacity, it can be said that all congestion is caused by having too much traffic volume. In some cases, too much volume is associated with routine temporal fluctuations in demand, such as peak period commute congestion in urban areas.
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83 WSDOT a record of when an incident was reported (used as an estimate of when that event occurred) , as well as when the incident respondent declared the site of the incident cleared.
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84 have occurred in the southernmost roadway sections before 5:00 p.m. and in the northern part of the city some time after 5:00 p.m.
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85 (e.g., baseball) do not have consistent durations, and their ending times are not easily determined.
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86 exits have less of an impact (the westbound on-ramp modestly affects the I-90 bridge section in both directions)
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87 and (f) rubbernecking, where rubbernecking was defined as a time during which a crash or incident was active on the roadway section being studied, but in the opposite direction of travel.
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88 the a.m. peak period that were later than noon because of various volume and bottleneck conditions that caused midday traffic to routinely travel below the speed limit.
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89 • Construction and rain have influenced queues that are present; • Construction, a crash, and an incident have influenced queues that are present; • Construction, rain, and an incident have influenced queues that are present; • Construction, rain, and a crash have influenced queues that are present; and • Construction, rain, a crash, and an incident have influenced queues that are present. Delay statistics were then aggregated by type of influence present.
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From page 90... ...
90 percentage of delay attributed to unknown causes tended to be those roadway sections with the least absolute vehicle delay. That is, nine of the 10 sections with the highest percentage of delay not caused, at least in part, by a known traffic disruption were among the 13 sections with the lowest total vehicle delay for the year.
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91Table 5.5. Hours of Delay Versus Percentage of Delay Without a Known Type of Disruption Corridor Vehicle Delay (h)
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92 was not expected at the outset of this analysis. It had been assumed that most of the delay without an observable cause was primarily due to too much traffic volume.
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93 from the race course and by airplanes flying low overhead. In addition, because the I-90 bridge is closed to traffic during the Blue Angel flights, considerable traffic diverts to the SR 520 bridge.
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94 Sensitivity tests were performed with various definitions of rain (e.g., requiring different fractions of an inch of rain falling within the previous hour for the pavement to be considered wet) and with different time periods within which rain had to have fallen (e.g., within the past hour or 2, 4, or 8 hours for the pavement to be considered wet)
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95 but congestion caused by that increase in accident rates is no more likely to occur than congestion from other sources. The greater probability of congestion early in the peak period and the longer queues that result from that early start to congestion also mean longer travel times on rainy days.
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96 the commute period is only marginally worse than normal, and by the end of the peak period, travel times are nearly the same as normal, regardless of whether rain has fallen. While the effects shown in Figure 5.5 were observed fairly universally for all roadway segments studied, further analysis of the 42 study segments revealed two significant differences in the effects of rain between less congested and more congested roadway segments.
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97 first section has extended back onto the second section. Thus, travelers do experience slower trip times, but the reported travel time on this section is not worse.
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98 not include the bridge itself. At more moderate wind speeds (e.g., 10 mph sustained winds)
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99 The effects of wind are similar to those of rain. High winds cause motorists to drive more cautiously.
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100 the slowing that individual vehicles exhibit under windy conditions. However, when volumes are high, the reduced functional roadway capacity resulting from motorists' voluntary slowing can create congestion that would not occur under average weather conditions.
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101 The primary statistical test used to compare influenced and noninfluenced travel times was an independent sample t-test. The majority of tests involved only data for Tuesdays, Wednesdays, and Thursdays to limit the effects that variations in dayof-week traffic volumes would have on the statistical results.
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102 many cases in which predisruption travel times were much faster than normal; when the disruption occurred, travel times slowed, but they never degraded to the point of normal conditions. Moreover, travel times returned to the faster-thannormal conditions that existed before the disruption.
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103 unless the queue is longer than the test section. This situation did happen on the test sections, but given the 2-mile minimum length of those test sections, it was unusual.
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104 end of congestion was redefined as being sustained speeds within either 10% or 20% of the speed limit, depending on the corridor. The intent of this new, corridor-specific definition was simply to allow better examination of how crashes and other disruptions affect when slow travel associated with peak period volumes ends.
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105 disruptions. That is, case studies of a number of specific days in 2006 showed that congestion on one roadway segment can frequently grow to the point that it affects the upstream road segment.
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106 the disruption imposed on the traffic stream. Therefore, crashes frequently have more significant effects during times of lower volume.
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107 percentile travel times were computed from the entire pool of travel times within each classification of trips, this approach did create a minor bias toward lower travel times in the noninfluenced category, as a disproportionate number of travel times for that category were taken from the early (least congested) portion of the peak periods.
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108 caused by noncrash traffic incidents is presented. This increase is then shown as a percentage change in study section travel time in comparison with the mean travel time with no disruption.
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109Table 5.8. Effects of Incidents and Crashes on P.M.
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110 A review of base data for a sample of these corridors suggested that two factors contributed to this variation. In some cases, the noninfluenced annual mean travel time was significantly affected by downstream congestion when that downstream congestion was caused both by routine conditions and by traffic disruptions on the downstream roadway segments.
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111 I-90 Bridge westbound 1.15 0.0 -1.0 56.2 162.0 I-5 Seattle CBD southbound 1.10 1.8 -1.3 13.0 28.6 SR 167 Auburn southbound 1.06 2.7 8.8 No crashes a.m. peak I-405 Eastgate southbound 1.05 0.3 -0.1 6.5 76.5 SR 167 Renton southbound 1.04 2.5 1.3 0.8 0.1 I-5 Tukwila southbound 1.02 0.0 -0.4 0.4 0.4 SR 520 Redmond westbound 1.02 0.6 12.7 29.3 76.6 I-405 North northbound 1.02 -0.2 1.1 1.2 6.2 I-5 Everett northbound 1.01 -0.1 -0.1 1.2 38.4 I-5 Lynnwood northbound 1.01 0.1 0.1 0.6 195.6 I-5 Seattle North northbound 1.01 2.3 2.9 5.9 5.3 I-90 Bellevue eastbound 1.01 0.0 0.9 0.0 0.7 I-5 South southbound 1.00 0.0 0.0 0.0 19.6 I-405 Kirkland northbound 1.00 0.2 2.4 0.2 0.2 SR 520 Redmond eastbound 1.00 -10.5 -14.9 -9.3 -16.5 I-90 Issaquah eastbound 1.00 0.0 0.0 0.0 0.0 I-5 North King northbound 1.00 0.0 0.0 0.0 0.0 Table 5.9.
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112 I-5 Seattle CBD southbound 1.72 2.4 -3.1 5.2 12.7 SR 167 Auburn southbound 1.96 0.6 22.5 29.4 11.2 SR 520 Redmond eastbound 1.87 -10.5 -14.9 -9.3 -16.5 I-5 South southbound 1.76 10.3 9.8 16.8 34.9 I-405 North northbound 1.61 8.4 30.5 27.4 59.5 I-5 Everett northbound 1.87 -6.8 -0.2 -3.4 2.5 I-5 Seattle North northbound 1.74 9.4 11.4 18.3 0.1 SR 167 Renton southbound 1.63 15.9 16.3 48.2 67.7 I-405 South southbound 1.52 5.1 6.9 7.8 22.6 I-90 Bridge westbound 1.73 48.8 29.6 47.5 13.4 SR 520 Seattle eastbound 1.49 24.4 21.3 32.1 42.9 I-5 Lynnwood northbound 1.38 12.8 2.4 43.1 60.9 I-405 Bellevue northbound 1.34 7.3 -8.0 54.5 68.1 I-90 Seattle westbound 1.13 0.6 7.7 1.7 9.5 SR 520 Redmond westbound 1.49 169.2 23.4 171.8 50.9 I-90 Seattle eastbound 1.43 25.9 -31.4 33.4 13.2 I-90 Bridge eastbound 1.40 51.3 15.4 83.5 21.5 I-5 North King southbound 1.33 9.9 86.8 151.5 114.6 I-90 Bellevue westbound 1.30 494.3 244.6 213.0 107.2 I-5 Tukwila southbound 1.19 8.4 7.9 48.6 18.0 SR 167 Renton northbound 1.17 6.3 23.1 26.3 44.4 I-405 Kennydale northbound 1.17 7.1 -3.9 40.4 98.1 I-90 Bellevue eastbound 1.11 3.2 -0.2 19.0 419.0 I-5 Everett southbound 1.10 5.9 8.6 57.5 149.3 I-5 Lynnwood southbound 1.10 -0.4 -7.2 20.6 41.3 I-405 North southbound 1.09 0.3 45.0 58.1 41.6 I-405 Kirkland southbound 1.09 6.6 19.3 20.9 29.1 I-5 Tukwila northbound 1.07 2.1 -6.7 113.7 146.3 SR 167 Auburn northbound 1.05 5.4 139.6 61.0 58.6 I-405 Eastgate northbound 1.04 -2.0 -6.8 26.7 142.6 I-90 Issaquah eastbound 1.01 1.2 1.0 0.1 -6.0 I-5 South northbound 1.01 -0.2 -0.1 4.7 77.8 I-90 Issaquah westbound 1.00 0.1 3.5 -0.4 -0.5 Table 5.10. Effects of Crashes and Noncrash Incidents on P.M.
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113 5.7 and 5.8, these two tables are sorted from most congested to least congested study corridor. Table 5.9 presents the changes to a.m.
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114 Table 5.11. Effects of Incidents and Crashes on Ending Time of P.M.
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115 Table 5.12. Effects of Incidents and Crashes on Ending Time of A.M.
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116 equal to the speed limit, the end of congestion time was extended when incidents occurred. Several significant differences were observed between the effects of incidents and crashes in the morning peak period described in Table 5.12 and those shown for the evening peak period in Table 5.11.
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117 the normal operations of a roadway. This combination of supply and demand effects are generally categorized into the seven sources of congestion.
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118 bad weather, and traffic volumes on travel times on I-5 northbound heading toward downtown Seattle. This graphic shows that congestion formed only as traffic volumes peaked.
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119 a crash occurring during the a.m. peak period adds an average of 2 hours and 17 minutes to the duration of the morning's peak period congestion.
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120 references 1.
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