Development of Tools for Assessing Wider Economic Benefits of Transportation (2014)

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21 Technical Guide Introduction and Purpose The purpose of the Reliability Module is to allow users to assess quickly the effects of highway investments in terms of both typical travel time and travel time reliability. In the past, economic assessments have been made strictly on the basis of typical travel time, but current research shows that travelers also value reliability of travel time. Accounting for this additional benefit means that transportation improvements have even more positive effects on users and the economy than heretofore thought. The Reliability Module is structured as a sketch planning tool that involves minimal data development and model calibration. It uses the results of other SHRP 2 projects in its methodology as well as methods from earlier studies. The procedure is based on making estimates of recurring and nonrecurring congestion, combining them, and using predictive equations to develop reliability metrics. Background on Travel Time Reliability A review of several SHRP 2 projects identified how they defined “reliability.” Project C04 (Improving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand) defined reliability as “. . . the level of (un)certainty with respect to the travel time and congestion levels.” It then used statistical measures, primarily the standard deviation of travel time, as the metrics used in subsequent analyses. Project C05 (Understanding the Contributions of Opera- tions, Technology, and Design to Meeting Highway Capacity Needs) defined it by saying “. . . the reliability of the perfor- mance is represented by the variability that occurs across multiple days.” Project L02 (Establishing Monitoring Programs for Travel Time Reliability) said the following: “It is important to start by observing that travel time reliability is not the same as (average) travel time. . . . Travel time reliability is about travel time probability density functions (TT-PDFs) that allow agencies to portray the variation in travel time that exists between two locations (point-to-point—P2P) or areas (area-to-area—A2A) at a given point in time or across some time interval. It is about estimating and reporting measures like the 10th, 50th, and 95th percentile travel times.” Functionally, Project L02 used the notion developed in Project L03 that reliability can be measured using the distribution of travel times for a facility or a trip. Project L04 (Incorporating Reliability Performance Mea- sures in Operations and Planning Modeling Tools) used this definition: “. . . models formulated in this research is based on the basic notion that transportation reliability is essentially a state of variation in expected (or repeated) travel times for a given facility or travel experience. The proposed approach is further grounded in a fundamental distinction between (1) sys- tematic variation in travel times resulting from predictable seasonal, day-specific, or hour-specific factors that affect either travel demand or network capacity, and (2) random variation that stems from various sources of largely unpredictable (to the user) unreliability.” Project L03 (Analytical Procedures for Determining the Impacts of Reliability Mitigation Strategies) used an expanded definition of reliability to include not only the idea of vari- ability but failure (or its opposite, being on time) as well. In terms of highway travel, the SHRP 2 Reliability Research Program defined reliability this way: “. . . from a practical standpoint, travel time reliability can be defined in terms of how travel times vary over time (e.g., hour to hour, day to day). This concept of variability can be extended to any other travel-time-based metrics such as average speeds and delay. For the purpose of this study, travel time variability and reli- ability are used interchangeably.” A slightly different view of reliability is based on the notion of a probability or the occurrence of failure often used to C h a p T e r 3 Reliability

22 characterize industrial processes. With this view, it is necessary to define what “failure” is, in terms of travel times. In other words, a threshold must be established. Then, one can count the number of times the threshold is not achieved or not exceeded. These types of measures are synonymous with “on- time performance” since performance is measured relative to a pre-established threshold. The only difference is that failure is defined in terms of how many times the travel time threshold is exceeded while on-time performance measures how many times the threshold is not exceeded. In recent years, some non-U.S. reliability research has focused on this other aspect of reliability, the probability of “failure,” where failure currently is defined in terms of traffic flow break- down. A corollary is the concept of “vulnerability,” which could be applied at the link or network level: This is a measure of how vulnerable the network is to breakdown conditions. Project L07 (Identification and Evaluation of the Cost- Effectiveness of Highway Design Features to Reduce Non- recurrent Congestion) used L03’s definition. Project L11 (Evaluating Alternative Operations Strategies to Improve Travel Time Reliability) defined reliability: “Travel- time reliability is related to the uncertainty in travel times. It is defined as the variation in travel time for the same trip from day to day (same trip implies the same purpose, from the same origin, to the same destination, at the same time of the day, using the same mode, and by the same route). If there is large variability, then the travel time is considered unreliable. If there is little or no variability, then the travel time is consid- ered reliable.” A wide range of viewpoints on the definition of travel time reliability clearly exists, but there is also a great degree of commonality. Travel time reliability relates to how travel times for a given trip and time period perform over time. For the purpose of measuring reliability, a “trip” can occur on a specific segment, facility (combination of multiple segments), any subset of the transportation network, or can be broad- ened to include a traveler’s initial origin and final destination. Measuring travel time reliability requires that a sufficient history be present in order to track travel time performance. There are two widely held ways that reliability can be defined. Each is valid and leads to a set of reliability performance mea- sures that capture the nature of travel time reliability: 1. The variability in travel times that occur on a facility or a trip over the course of time; and 2. The number of times (trips) that either “fail” or “succeed” in accordance with a pre-determined performance standard. In both cases, reliability (more appropriately, unreliability) is caused by the interaction of the factors that influence travel times: fluctuations in demand (which may be due to daily or seasonal variation or to special events), traffic control devices, traffic incidents, inclement weather, work zones, and physical capacity (based on prevailing geometrics and traffic patterns). These factors will produce travel times that differ from day to day for the same trip. From a measurement perspective, reliability is quantified from the distribution of travel times, for a given facility or trip and time period (e.g., weekday peak period), that occurs over a significant span of time (one year is generally long enough to capture nearly all of the variability caused by disruptions). A variety of different metrics can be computed once the travel time distribution has been established, including standard statistical measures (e.g., kurtosis, standard deviation), percentile-based measures (e.g., 95th percentile travel time or Buffer Index), on-time measures (e.g., percent of trips com- pleted within a travel time threshold), and failure measures (e.g., percent of trips that exceed a travel time threshold). The reliability of a facility or trip can be reported for different time slices (e.g., weekday peak hour, weekday peak period, and weekend). Figure 3.1 shows an actual travel time distribution derived from roadway detector data and the metrics that can be derived from it. Note that a number of metrics are expressed relative to the free flow travel time, which becomes the bench- mark for any reliability analysis. The degree of (un)reliability then becomes a relative comparison to the free flow travel time. The Value of Travel Time Reliability Valuing travel time has a long history in transportation model- ing and analysis. The value of travel time (VOT) refers to the monetary values travelers place on reducing their travel time. VOT has been long established from a basis in consumer theory where value is related to a wage rate or some portion of it. It is considered one of the largest cost components in benefit–cost analysis of transportation projects because one of the benefits for travelers in a transportation improvement is the reduction of travel time (Vovsha et al. 2011). In contrast, the value of reliability (VOR) is a relatively new concept. VOR refers to the monetary values travelers place on reducing the variability of their travel time. Reliability has most often been considered qualitatively and is associated with the statistical concept of variability (Carrion and Levinson 2010). However, it is clearly recognized by travelers of all types. Trav- elers account for the variability in their trips by building in “buffers” as insurance against late arrival. This action implies that the consequence of arriving late is “costly” and should be avoided (OECD 2010). Efficiency and productivity lost in these buffers or safety margins represent an additional cost that travelers absorb. Reliability is of such sufficient value to transportation system users that they are willing to pay for reduced travel time, as

23 has been demonstrated by empirical studies. Variability in the costs which are acceptable to different travelers for different trips suggests that this value is not uniform for all types of trips (Waters 1992). The difference in value between users and the type of use must be quantified to be understood and applied appropriately. For the business traveler and freight shippers, time is money. The just-in-time delivery aspect of the present economy implies a high cost associated with an unreliable transportation system and a corresponding value for travel time reliability. Freight providers are a unique category of transportation users in many aspects; however, the value placed on reliability is consistent with or greater than with other travelers. Past research studies have used the Reliability Ratio (VOR/VOT) as the most convenient way to measure reliabil- ity in an empirical study. Table 3.1 summarizes the values of reliability for passenger travel that were included in the reviewed research. Of these studies, the Carrion and Levinson 2010 work is the most comprehensive. They were selective in their choice of studies because they were using them for a meta-analysis. It is interesting that there is less variation among more recent studies, and if the means of each individual study is used, the reliability ratios are grouped in the 0.5 to 1.5 range. Figure 3.2 is taken directly from Carrion and Levinson. Previously, SHRP 2 Project C04 also noted the same range. The SHRP 2 L05 effort more narrowly focused the Reliability Ratio range to 0.9 to 1.25 based on including only the research with the most rigor- ous methods. A Florida DOT study recommended a Reliability Ratio range of 0.8 to 1.0, based on their assessment of the most rigorous studies (Elefteriadou and Cui 2007). The authors also mentioned that the value could be “as much as three times higher” if strict schedule adherence is required for the trip. For the reliability spreadsheet tool, the 80th–50th percentile is used as the measure of the reliability space. This produces a conservative estimate of reliability. Specification of Inputs Inputs are provided for base condition as well as for one or more improvement scenarios. Basic Analysis Unit Highway segments are the basic unit of analysis, and input data pertains to them. Segments can be of any length but it is recommended that they not be so long that the characteristics change dramatically along the segment, or too short that input is burdensome. Reasonable segment lengths would be as follows: • Freeways: between interchanges; • Signalized highways: between signals; and • Rural highways (nonfreeways): 2 to 5 miles. For the purpose of output, segments are aggregated into highway sections in order to be compatible with the reliability prediction equations. Source: Cambridge Systematics et al. 2013. Figure 3.1. The travel time distribution is the basis for defining reliability metrics.

24 Table 3.1. Past Research on the Value of Reliability: Passenger Travel Authors Study Type Reliability Ratio (personal auto use) Reliability Metric/Definition Brownstone and Small (2005) RP/SP 1.18 90th–50th Percentile Ghosh (2001) RP 1.17 90th–50th Percentile Li et al. (2010) SP 0.70 Scheduling approach; standard deviation Börjesson and Eliasson (2008) SP 1.27 Ratio of sensitivity to standard deviation to sensitivity of the mean Small et al. (1995) SP 2.30 Standard deviation Small and Yan (2001) SP 2.51 Standard deviation Small et al. (2005) RP 0.91 80th–50th Percentiles Tilahun and Levinson (2010) SP 0.89 90th–50th Percentile Carrion and Levinson (2010) RP 0.91 90th–50th Percentile De Jong et al. (2009) SP 1.35 Standard deviation Fosgerau et al. (2008) RP 1.00 Standard deviation Yan (2002) RP/SP 0.97 90th–50th Percentile Asensio and Matas (2008) SP 0.98 Scheduling approach; standard deviation Bhat and Sardesai (2006) RP/SP 0.26 Scheduling approach; standard deviation Senna (1994) SP 0.76 Standard deviation Black and Towriss (1993) SP 0.55–0.70 Standard deviation Tilahun and Levinson (2009) SP 1.0 Scheduling approach; difference between actual late arrival and usual travel time Ubbels et al. (2005) SP 0.5 Scheduling approach; difference between early/late arrival time and preferred arrival time Koskenoja (1996) SP 0.75 Average schedule delay (late and early) Parsons Brinckerhoff et al. (2013) RP 0.7–1.5 Standard deviation per unit distance Stogios et al. (forthcoming) RP 0.57–2.69 Standard deviation per unit distance Note: RP = Revealed Preference (based on observed behavior); SP = Stated Preference (based on survey responses). Inventory Data • Route. • Beginning mile point. • Beginning landmark. • Ending mile point. • Ending landmark. • Highway type. 44 Freeway (access controlled); 44 Multilane (nonsignalized, nonaccess controlled); 44 Signalized; and 44 Rural two-lane. • Number of lanes. • Free flow speed. 44 Alternately, the posted speed limit. Traffic Data • Average Annual Daily Traffic (AADT), current. • Annual traffic growth rate (%). Truck Data • Percentage of trucks in the traffic stream (combinations plus single units). Capacity Data • Peak capacity as determined with Highway Capacity Manual procedures. 44 Alternately, the G/C ratio (effective green time divided by cycle length) for signalized highways; and 44 Terrain (flat, rolling, or mountainous) for freeways and rural two-lane highways. Time Horizon • Number of years into the future for which the analysis applies. Analysis Period • Specify the hours of the day for which the analysis will be run.

25 Economic Analysis Data • Unit cost of travel time, personal ($/hour): default = $19.86 (HERS-ST Highway Economic Requirements System—State Version: Technical Report, 2005). • Unit cost of travel time, commercial ($/hour): default = $36.055. • Reliability Ratio, personal: default = 0.8 (based on Stogios et al., forthcoming). • Reliability Ratio, commercial: default = 1.16. Output and Calculations Output Outputs are produced for the entire project length in table form. Outputs are displayed for the base condition and all improve- ment scenarios. A variety of reliability metrics are produced to allow users wide flexibility in interpreting the results. They also permit users to make independent estimates of the value of reliability if they want to use alternative measures of the reliability space, such as the following: • Year of analysis (the future year); • Recurring delay (hours); • Incident delay (hours); • Total delay (hours); • Overall travel time index; • 95th percentile travel time index; • 80th percentile travel time index; • Percent of trips < 45 mph; • Percent of trips < 30 mph; • Cost of recurring delay; • Cost of unreliability; and • Total congestion cost. Calculations Calculations are done for each hour and direction on the study segments. The results are summed over all segments and reported for the current year and forecast year. Equations 3.1 through 3.17 follow. Calculate Future Year AADT: p )(= +FutureAADT AADT 1 TrafficGrowthRate (3.1) NumberOf Years Figure 3.2. Reliability ratios from previous studies. Source: Carrion and Levinson 2010.

26 Calculate HCM p Capacity (if not directly input) for Freeways and Multilane Highways Without Signals: p p=Capacity IdealCap (3.2)HVN F where Capacity = One-way capacity; IdealCap = 2,400 passenger cars per hour per lane (pcphpl) if free flow speed ≥70 mph, or 2,300 pcphpl otherwise; N = number of through lanes in one direction; FHV = heavy vehicle adjustment factor: 1.0/(1.0 + 0.5 HV) for level terrain, 1.0/(1.0 + 2.0 HV) for rolling terrain, 1.0/(1.0 + 5.0 HV) for moun- tainous terrain (rare in urban areas); and HV = daily proportion of trucks in traffic stream. Signalized Highways: p p p=Capacity IdealSat (3.3)HVN F g C where Capacity = One-way capacity; IdealSat = Ideal saturation flow rate (1,900 pcphpl); N = number of through lanes in one direction; FHV = heavy vehicle adjustment factor: 1.0/(1.0 + 0.5 HV) for level terrain, 1.0/(1.0 + 2.0 HV) for rolling terrain, 1.0/(1.0 + 5.0 HV) for moun- tainous terrain (rare in urban areas); and g/C = effective green time divided by cycle length (0.45 for arterials, 0.35 for other highway classes). Rural Two-Lane Highways: p p=Capacity IdealCap (3.4)HVF FG where Capacity = Two-way capacity; IdealCap = 3,200 passenger cars per hour (pcph); FHV = heavy vehicle adjustment factor (1 or 1 + PT[ET - 1]); PT = percent of trucks; ET = Passenger car equivalents from Table 3.2; and FG = grade adjustment factor from Table 3.3. Calculate AADT/Capacity (C): =AADT C FutureAADT TwoWayCapacity (3.5) Note: For all multilane and signalized highways, TwoWay Capacity is the one-way capacity times two. Calculate Hourly Volumes for Hours to Be Used in the Analysis: Multiply AADT and FutureAADT by the appropriate hourly factor from Table 3.4. For multilane highways, the analysis is done by each direction individually. For rural two- lane highways, the analysis is done for both lanes combined, that is, the hourly volume is the sum of the a.m. and p.m. directions. Calculate Free Flow Speed (if speed limit is input in lieu of the actual free flow speed): This calculation is from Dowling et al. 1997. ( )= +FreeFlowSpeed 0.88 SpeedLimit 14, for freeways and rural two-lane highways (3.6) p ( )= +FreeFlowSpeed 0.79 SpeedLimit 12, for signalized highways (3.7) p Calculate Travel Time per Unit Distance (Travel Rate) for the Current and Forecast Years: p ){ })( ( )(= + ≤1 0.1225 FreeFlowSpeed, for 1.40 (3.8) 8t v c v c where t = travel rate (hours per mile); v = hourly volume; and c = capacity (for an hour, previously defined). Note: v/c should be capped at 1.40 (Cambridge Systematics et al. 1998). Table 3.2. Passenger Car Equivalents for Trucks (ET) Two-Way Flow Rates (pcph) Type of Terrain Level Rolling Mountainous 0–600 1.7 2.5 7.2 >600–1,200 1.2 1.9 7.2 >1,200 1.1 1.5 7.2 Note: Flow rates are determined by using the “AADT/C<7” condition from Table 3.3 and combining the a.m. and p.m. percentage for the peak hour, which is assumed to be the hour ending at 18:00 (Hour 18 in Table 3.4). Table 3.3. Grade Adjustment Factors (fG) for Highway Performance Measuring System Two-Way Flow Rates (pcph) Level Rolling Mountainous 0–600 1.00 0.71 0.57 >600–1,200 1.00 0.93 0.85 >1,200 1.00 0.99 0.99

27 Table 3.4. Hourly Traffic Distributions Hour Ending Freeway, Weekday Other, Weekday AADT/C AADT/C LE 7.0 7.1–11.0 GT 11.0 LE 7.0 7.1–11.0 GT 11.0 Peak Direction Peak Direction Peak Direction Peak Direction Peak Direction Peak Direction A.M. P.M. A.M. P.M. A.M. P.M. A.M. P.M. A.M. P.M. A.M. P.M. Percent of Daily Volume Percent of Daily Volume Percent of Daily Volume Percent of Daily Volume Percent of Daily Volume Percent of Daily Volume Percent of Daily Volume Percent of Daily Volume Percent of Daily Volume Percent of Daily Volume Percent of Daily Volume Percent of Daily Volume 1 0.42 0.58 0.44 0.57 0.47 0.54 0.34 0.47 0.37 0.47 0.41 0.49 2 0.27 0.33 0.27 0.34 0.27 0.32 0.21 0.28 0.23 0.27 0.24 0.28 3 0.23 0.25 0.22 0.26 0.20 0.24 0.15 0.18 0.17 0.18 0.18 0.20 4 0.23 0.22 0.21 0.21 0.18 0.18 0.14 0.14 0.16 0.15 0.17 0.18 5 0.38 0.29 0.35 0.28 0.31 0.25 0.24 0.18 0.28 0.20 0.33 0.27 6 1.17 0.68 1.12 0.69 1.06 0.72 0.74 0.42 0.81 0.48 1.03 0.67 7 3.26 1.75 3.16 1.90 2.86 2.18 2.23 1.19 2.35 1.27 2.55 1.72 8 4.83 2.90 4.59 3.05 3.90 3.27 4.11 2.28 3.85 2.39 3.57 2.79 9 3.56 2.57 3.80 2.76 3.66 3.04 3.45 2.33 3.42 2.39 3.09 2.78 10 2.58 2.24 2.75 2.30 2.94 2.53 2.64 2.29 2.69 2.31 2.68 2.47 11 2.46 2.33 2.50 2.34 2.68 2.49 2.64 2.56 2.65 2.54 2.62 2.57 12 2.56 2.56 2.61 2.61 2.73 2.69 2.90 3.02 2.90 2.98 2.83 2.89 13 2.65 2.71 2.68 2.75 2.75 2.78 3.20 3.35 3.17 3.30 3.04 3.13 14 2.70 2.77 2.75 2.81 2.82 2.86 3.14 3.24 3.14 3.22 3.06 3.13 15 2.93 3.12 2.93 3.15 2.97 3.15 3.18 3.44 3.12 3.37 3.21 3.34 16 3.26 4.01 3.21 3.87 3.21 3.60 3.40 4.13 3.35 3.93 3.41 3.78 17 3.47 4.81 3.38 4.43 3.28 3.82 3.46 4.78 3.49 4.49 3.47 3.92 18 3.42 4.85 3.32 4.39 3.29 3.77 3.31 4.83 3.45 4.55 3.39 3.86 19 2.66 3.23 2.66 3.20 2.82 3.22 2.68 3.23 2.75 3.31 2.82 3.12 20 1.95 2.23 1.97 2.25 2.12 2.36 2.14 2.41 2.18 2.53 2.28 2.53 21 1.54 1.78 1.54 1.79 1.62 1.86 1.73 1.97 1.75 2.07 1.83 2.09 22 1.40 1.63 1.44 1.69 1.54 1.74 1.49 1.71 1.50 1.77 1.55 1.80 23 1.14 1.30 1.19 1.39 1.27 1.46 1.10 1.26 1.11 1.25 1.22 1.29 24 0.79 0.98 0.83 1.05 0.89 1.07 0.74 0.94 0.75 0.90 0.83 0.97 Total 49.87 50.13 49.92 50.08 49.84 50.16 49.36 50.64 49.67 50.33 49.71 50.29 Note: AADT/C = Annual average daily traffic/capacity. Source: Science Applications International Corporation and Cambridge Systematics 1994.

28 Compute the Recurring Delay in Hours per Mile: )(= −RecurringDelayRate 1 FreeFlowSpeed (3.9)t Compute the Delay Due to Incidents (IncidentDelayRate) in Hours per Mile: The lookup tables from the IDAS User’s Manual are used to calculate incident delay. This requires the v/c ratio, number of lanes, and length and type of the period being studied, which is set at one hour (Cambridge Systematics 2003). (For rural two-lane highways, use number of lanes = 2.) This is the base incident delay. If incident management programs have been added as a strategy or if a strategy lowers the incident rate (frequency of occurrence), then the “after” delay is calculated as follows: D D R Ra u f dp p1 1 (3.10) 2( ) ( )− − − where Da = Adjusted delay (hours of delay per mile); Du = Unadjusted (base) delay (hours of delay per mile, from the incident rate tables); Rf = Reduction in incident frequency expressed as a fraction (with Rf = 0 meaning no reduction, and Rf = .30 mean- ing a 30% reduction in incident frequency); and Rd = Reduction in incident duration expressed as a fraction (with Rd = 0 meaning no reduction, and Rd = .30 mean- ing a 30% reduction in incident duration). Changes in incident frequency are most commonly affected by strategies that decrease crash rates. However, crashes are only about 20% of total incidents. So, a 30% reduction in crash rates alone would reduce overall incident rates by 6% (.30 * .20 = .06). Compute the Overall Mean Travel Time Index (TTIm): This includes the effects of recurring and incident delay. TTI 1 FFS RecurringDelayRate IncidentDelayRate (3.11) m ( )= + +p where FFS is the free flow speed. Because the data on which the reliability metric predictive functions do not include extremely high values of TTIm, it is recommended that TTIm be capped at a value of 6.0, which roughly corresponds to an average speed of 10 mph. Even though the data included highway sections that were con- sidered to be severely congested, an overall annual average speed of 10 mph for a peak period was never observed. At TTIm = 6.0, the reliability prediction equations are still inter- nally consistent. Compute Reliability Metrics Using the SHRP 2 L03 “Data Poor” Equations: The equations for the 80th and 50th percentile TTIs were developed specifically for this module using the original SHRP 2 Project L03 data. e e e e m m m m m p p p p p TTI 1 3.6700 ln TTI TTI 5.3746 1 ; TTI 1.0 TTI 4.01224 1 ; TTI 1.0 PercentTripsOccurringLT45mph 1 PercentTripsOccurringLT30mph 1 0.333 0.672 1 (3.12) 95 80 1.5782 0.85867 TTI 1 0.04953 80 50 1.7417 0.93677 TTI 1 0.82741 50 1.5115 TTI 1 5.0366 TTI 1.8256[ ]{ } ( ) { } { } ( ) ( ) ( ) = + = + ≥ = + ≥ = − = − + + ( ) ( ) ( ) ( ) ( )( ) − − − − − ( )( ) − where TTI95 is the 95th percentile TTI; TTI80 is the 80th percentile TTI; TTI50 is the 50th percentile TTI; PercentTripsOccurringLT45mph is the percent of trips that occur at speeds less than 45 mph; and PercentTripsOccurringLT30mph is the percent of trips that occur at speeds less than 30 mph. Calculate Travel Time Equivalents Separately for Passenger Cars and Trucks: ae pTTI TTI TTI TTI (3.13)VT 50 80 50( )= + −( ) where TTIe(VT) = the TTI equivalent on the segment, computed separately for passenger cars (personal travel) and trucks (commercial travel); and a = the Reliability Ratio (VOR/VOT), which is 0.8 for passenger cars and 1.1 for trucks. The use of the median to capture the “typical” or “average” condition is to avoid double counting: The mean value from the full distribution has some of the variability built in, the median less so. Compute Total Equivalent Delay Based on the TTIe, Separately for Passenger Vehicles and Trucks: e p TotalEquivalentAnnualWeekdayDelay TTI FreeFlowSpeed 1 FreeFlowSpeed AVMT (3.14) VT VT VT( )( ) = −( ) where TotalEquivalentAnnualWeekdayDelayVT is in vehicle-hours, separately for vehicle types (passenger and truck for now);

29 AVMTVT = HourlyVolume p SectionLength p Pct p 260; and Pct = percent of trucks in traffic stream (for commer- cial traffic) or 1 - percent of trucks in traffic stream (for passenger travel). Compute Congestion and Reliability Costs: p =TotalDelayCost TotalEquivalentAnnualWeekdayDelay UnitCost (3.15) VT VT VT p )(= )(RecurringDelayCost TotalDelayCost TTI TTI (3.16) VT VT 50 VTe = −ReliabilityCost TotalDelayCost RecurringDelayCost (3.17) VT VT VT Costs should be computed separately for each vehicle type (passenger versus commercial) and summed. Assessing the Impacts of Highway Improvements Highway improvements of various types need to be trans- lated into changes in the input parameters. Specifically, improvements can affect capacity, volume, and incident characteristics. If a capacity analysis is not done offline, then the Module will compute a new capacity for the improvement if there are changes in • Number of lanes or truck percentage (freeways); • Number of lanes, truck percentage, or green-to-cycle ratio (signalized highways); • Truck percentage of grade (two-lane highways); or • Free flow speed (all highways). Additional geometric improvements may be considered if the user performs an offline capacity analysis. Examples include lane and shoulder widening, median separation, and turn lane additions at signalized intersections. Offline capacity analysis will also identify the increase in capacity due to signal progression and converting stop sign-controlled intersections to signal control. If an improvement changes the volume (AADT or traffic growth rate), the user needs to indicate the change. This can only be done offline; the module does not deal with estimating demand changes. For Incident Characteristics, the tool uses both incident fre- quency and incident duration to estimate nonrecurring delay. Incident frequency is primarily affected by reductions in crashes (a subset of total incidents) due to safety improvements. Crash reduction factors for a wide variety of geometric and operating improvements can be found in the Highway Safety Manual. Chapter 2 of the manual discusses how these are incorporated into the procedure. Note that a safety improvement can also increase capacity; the user should check whether this is the case and perform an offline capacity analysis if warranted. Incident duration is affected by incident management strategies. The information in Table 3.5 can be used to deter- mine the reduction in incident duration. reliability Module User’s Guide and Instructions Introduction and Purpose The Reliability Module presented here is a sketch planning corridor spreadsheet tool based on SHRP 2 Reliability Proj- ect L03 research that estimates the benefits of improving travel time reliability for use in benefit–cost analysis. Local travel time reliability data are not required because reliability mea- sures are embedded in the L03 work. Agencies will typically have the required inputs (e.g., traffic volume, roadway capacity, AADT, percent of trucks, number of lanes, and growth rate). Before You Start 1. At the website http://www.tpics.us/tools, under Tools, click the link for Reliability Tool. Download “SHRP 2 C11 Reli- ability Module.xlsm” and open it using Microsoft Excel. (A version number is usually added to the end of this file name such as “SHRP 2 C11 Reliability Module v9.2.xlsm”.) 2. If prompted to Enable or Disable Macros when the file opens, be sure to choose Enable. Optionally, to permanently enable macros in Microsoft Excel 2007 and 2010, follow these steps: a. For Excel 2007 and 2010, first click the Office button (upper-left corner). b. Click Excel Options. c. Select the Trust Center options and click Trust Center Settings (Figure 3.3). d. In Macro Settings, click the radio button Enable All Macros (Figure 3.4). e. For earlier versions of Excel, navigate to Tools>Options. Select the Security Tab and click Macro Security. Select Low. The Reliability Module requires you to understand two key concepts: 1. Scenario: A scenario represents a set of highway and traffic conditions. It is input and named by the user, which is saved and reported on by the tool. These scenarios are kept even after the program is closed. 2. Session: A session consists of a set of scenarios. If there are scenarios saved when the Reliability Module is opened, this is considered to be a previous session. When a new session

30 Table 3.5. Incident Management Impacts Improvement Impact Incident Management (IM): Improving from no formal IM program to a program that includes detection, verification, and service patrols Atlanta—Average time between first report and incident verification was reduced by 74%. Average time between verification and response initiation reduced by 50%. Average time between incident verification and clearance of traffic lanes reduced by 38%. The maximum time between incident verification and clearance of traffic lanes was reduced by 60% (Booz Allen Hamilton 1997). Houston—Average 30-minute incident duration reduction (RITA 1997). IDAS Model recommends a default reduction in incident duration of 9% for incident detection, 39% for incident response systems, and 51% for combination incident detection and response systems (Cambridge Systematics 2003). Georgia (Navigator)—Reduced incident clearance time by an average of 23 minutes and reduced incident response time by 30% (Institute of Transportation Engineers 1997). Maryland (CHART)—Reduced the blockage duration from incidents by 36%. This translates to a reduction in highway user delay time of about 42,000 hours per incident (Chang and Rochon 2007). 15% to 38% reduction in all secondary crashes; 4% to 30% reduction in rear-end crashes; and 21% to 43% reduction in severe secondary crashes (Institute of Transportation Engineers 1997). RECOMMENDATION Improved equipment for incident detection and verification (CCTV) Based on CHART, reduce incident lane-hours lost by 36%. Brooklyn—Average time required to clear incident from roadway reduced by 66% (Stough 2001). San Antonio (TransGuide)—20% improvement in response time (21% reduction for major incidents and 19% for minor incidents) (Turner et al. 1998). RECOMMENDATION Improved interagency communications for incident detection and verification Based on TransGuide and assuming that incident response time is 20% of incident duration time, reduce incident duration by 4%. Minneapolis/St. Paul (Highway Helper)—Automatic tow truck dispatch program is credited with a 20-minute reduction in incident response and removal times (85% improvement) (ATA Foundation and Cambridge Systematics 1997). RECOMMENDATION Improved equipment and service for incident response Assuming that response time is 20% of incident duration time, reduce incident duration by 17%. Hayward, California—38% reduction in incident duration and 57% reduction in breakdown duration (Skabardonis et al. 1995). Northern Virginia—Reduction in duration for all incidents is 2 to 5 minutes for cell phone in response vehicles, 2 to 5 minutes for CAD screens in response vehicles, and 4 to 7 minutes for GPS location for response vehicles (Maas et al. 2001). Oregon—The duration of delay-causing incidents decreased by approximately 30% on Highway 18 and 15% on Interstate 5 (service patrol addition) (Bertini et al. 2001). Pittsburgh—Service patrol reduced response time to incidents from 17 to 8.7 minutes (FHWA 2000). Washington State—Average freeway incident clearance time for large trucks reduced to 1.5 hours from 5 to 7 hours without the incident response team (Hall 2000). RECOMMENDATION Service Patrols For the implementation of service patrols, reduce incident duration by 38%. Note: CHART = Coordinated Highways Action Response Team; CCTV = closed-circuit television; CAD = computer-assisted drafting. Source: Cambridge Systematics et al. 2013. is created, all scenarios created in the previous session are deleted. Users can save the Reliability Module under a dif- ferent file name to retain the previous session. Quick Start Guide To create a new scenario, erasing any scenarios currently in file, follow these steps: 1. At the website http://www.tpics.us/tools, under Tools, click the link for Reliability Tool and open it using Microsoft Excel file (choose to enable macros if prompted). 2. Select the tab named 2–INPUTS. 3. Click Begin a New Session. 4. Click Yes. 5. Click Yes (Reminder: all Scenario data will be deleted). 6. In the Scenario Inputs window, click New Scenario. 7. In the New Scenario window, enter a Scenario name. 8. If using default input values, select the Using Default Values checkbox. 9. In the Scenario Inputs window, enter all required input data. 10. To save this Scenario, click Save Scenario. To create a new Scenario, in addition to Scenarios currently in file, follow these steps: 1. Open the file (choose to enable macros if prompted). 2. Choose the tab named 2–INPUTS. 3. Click Resume a Previous Session.

31 Figure 3.3. Screenshot of Trust Center settings. Figure 3.4. Screenshot of how to enable all macros.

32 4. In the Scenario Inputs window, click New Scenario. 5. In the New Scenario window, enter a scenario name. 6. If using default input values, select the Using Default Values checkbox. 7. In the Scenario Inputs window, enter all required input data. 8. Click Save Scenario to save this Scenario. To view results, click Results while in the Scenario Inputs window. Before attempting to view Results, be sure to have entered (and saved) at least one Scenario. Entering Inputs Figure 3.5 shows the screen that displays when you open the Reliability Module for the first time. Here you can see when your version of the Reliability Module was last updated as well as a brief set of instructions for the tool. To begin entering data: 1. Click the tab labeled 2–INPUTS. The 2–INPUTS tab dis- plays (Figure 3.6). 2. Read the instructions listed by each button. 3. Do one of the following: a. If you want to Resume a Previous Session, click that button, and continue reading instructions at the heading Using the Scenario Inputs Window. b. If you want to Begin a New Session, click that button. Note that doing so will first delete all currently entered data. Optionally, you can hide or unhide (password- protected) sheets used in the background of the tool. Continue reading instructions at the heading Entering a New Scenario. Using the Scenario Inputs Window 1. On the 2–INPUTS tab, after you click Resume a Previous Session, the Scenario Inputs window displays (Figure 3.7). Descriptions of the navigation buttons are in Table 3.6, and inputs and their descriptions are shown in Table 3.7. 2. Enter all pertinent information about a specific scenario. 3. When you are finished, click the button for either Save Sce- nario, Delete Current Scenario, Results, or New Scenario. Entering a New Scenario To enter a new scenario: 1. In the Scenario Inputs window, click the New Scenario button. The dialog box shown in Figure 3.8 displays. 2. Enter a Scenario name. 3. If you want the system to automatically fill in the default values for several input parameters, select the Use Default Values checkbox. The system will fill in the default values for the data fields, as listed in Table 3.8. Figure 3.5. Screenshot of opening tab of Reliability Module (1—START). Figure 3.6. Screenshot of first screen on the 2—Inputs Tab.

33 4. Click OK. The Scenario Inputs window displays (Fig- ure 3.7). 5. Enter all pertinent information about a specific scenario. 6. When you are finished, click a button either for Save Sce- nario, Delete Current Scenario, Results, or New Scenario. In the Scenario Inputs window (Figure 3.7), if you want to save the scenario under a different name, enter the name in the Scenario Name field. If there are no Scenarios currently saved, enter and save a Scenario before attempting to explore the functionality of the Reliability Module. Not doing so may impact the proper running of the program. If there are no Scenarios entered and the program stops working, close the program without saving it, reopen the file, and enter a Scenario using the steps outlined above. Obtaining Results In the tab labeled 3–RESULTS, results are displayed for each scenario entered through the 2–INPUTS tab. By default, the results are given in a summary form, with scenarios orga- nized in columns. A detailed, hourly view is available for indi- vidual scenarios as well. For more detailed information about the results, including their calculation, refer to the Reliability Technical Guide in Chapter 2. Organizing Results On the 3–RESULTS tab, the heading Current Year contains the following: • Congestion Metrics—Key measures of congestion, such as overall travel time mean index, 95th and 80th percentile travel time index, and percent of trips that occur at speeds less than 45 mph and 30 mph. • Total Annual Weekday Delay—Total annual weekday delay in vehicle-hours, categorized by congestion types (recurring and incident delay) and vehicle types (passenger and truck). Table 3.6. Buttons in the Inputs Window—Tab 2 Buttons in the Inputs Window New Scenario Displays a dialogue box for creating a new Scenario. Save Scenario Saves the currently entered data to the Scenario selected from Scenario Name. Delete Current Scenario Permanently deletes the Current Scenario from the Scenario Name drop-down list. Results Navigates to the Results tab. Figure 3.7. Screenshot of the Scenario Inputs page—Tab 2.

34 Table 3.7. Input Fields and Their Meanings—Tab 2 Field Name Req? Description Scenario Name Yes A unique name used to describe a Scenario Description No An optional description of the Scenario Time Horizon Yes Number of years into the future for which the analysis applies Analysis Period Yes Specify the hours of the day for which the analysis will be run Highway Type Yes Freeway, Signalized, or Two-lane Rural Beg. Milepoint Yes Beginning milepoint is used with end milepoint to determine length of highway to analyze End Milepoint Yes Ending milepoint is used with begin milepoint to determine length of highway to analyze No. of Lanes (One-way) Yes The number of lanes in one traffic direction (does not apply to Two-lane Rural) Free Flow Speed Yes Free Flow Speed (FFS) is the average speed that a motorist would travel if there is no congestion or other adverse condition Using Speed Limit Yes This is a checkbox. When checked, the FFS field can be used to enter the posted speed limit, which is then used to calculate the FFS Current AADT Yes Current AADT volume Est Annual Traffic Growth Rate Yes Estimated future annual average daily traffic volume Pct. Trucks in Traffic Yes Percent trucks in the traffic stream (combinations + single units) Peak Capacity Yes Peak capacity as determined using Highway Capacity Manual procedures Terrain Yes Flat, Rolling, or Mountainous. Can be used to calculate Peak Capacity Personal Travel Time Unit Cost Yes Unit cost of travel time, personal ($/hour): Default = $19.86 Commercial Travel Time Unit Cost Yes Unit cost of travel time, commercial ($/hour): Default = $36.05 Reduction in Incident Frequency Yes Reduction in incident frequency, expressed as a percentage, due to the addition of an incident management program/strategy. Default = 0% Reduction in Incident Duration Yes Reduction in incident duration, expressed as a percentage, due to the addition of an incident management program/strategy. Default = 0% Personal Reliability Ratio Yes The ratio of value of travel time reliability over value of travel time for the general motorists. Default = 0.8 Commercial Reliability Ratio Yes The ratio of value of travel time reliability over value of travel time for commercial vehicles. Default = 1.1 Route No Route name Beg. Landmark No Name of beginning landmark End Landmark No Name of ending landmark Figure 3.8. Screenshot of New Scenario input screen—Tab 2. Table 3.8. Default Values for the Reliability Module Data Field Default Value Personal Travel Time Unit Cost $19.86 Commercial Travel Time Unit Cost $36.05 Reduction in Incident Frequency 0% Reduction in Incident Duration 0% Personal Reliability Ratio 0.8 Commercial Reliability Ratio 1.1

35 • Total Annual Weekday Congestion Costs—Total annual weekday delay cost incurred by congestion, categorized by congestion types (recurring and unreliability costs) and vehicle types (passenger and truck). On the 3–RESULTS tab, the heading Future Year, which is determined by Time Horizon, contains the following: • Congestion Metrics—Key measures of congestion, such as overall travel time mean index, 95th and 80th percentile travel time index, and percent of trips that occur at speeds less than 45 mph and 30 mph. • Total Annual Weekday Delay—Total annual weekday delay in vehicle-hours, categorized by congestion types (recurring and incident delay) and vehicle types (passenger and truck). • Total Annual Weekday Congestion Costs—Total annual weekday delay cost incurred by congestion, categorized by congestion types (recurring and unreliability costs) and vehicle types (passenger and truck). Summary View In the Summary view (Figure 3.9), aggregated results are shown for all scenarios. To view the hourly results for a particular Figure 3.9. Screenshot of the RESULTS summary page—Tab 3.

36 scenario, click in the column containing the desired scenario and then click Details. In the case shown in Figure 3.9, clicking Details would show hourly results for scenario “Freeway,” because a cell in the column with the results for “Freeway” has been selected. Details View In the Details view (Figure 3.10), hourly results are shown for one specific scenario. Several input parameters are displayed in italics underneath the scenario name and pertain only to the scenario being currently viewed. Calculation Debugger Pressing Ctrl+Shift+D while in the 3–RESULTS tab displays the Calculation Debugger window (Table 3.9). Here, many variables that are used in calculating the results are displayed and organized by scenario, year, and hour. In Figure 3.11, one can see that by scrolling down through the Calculation Debugger window, one can view the data from all scenarios currently saved in the Module. Note oN SaviNg aNd CloSiNg the File Inside the Scenario Inputs window, any changes to scenario data must be saved manually using the Save Scenario button (see Figure 3.12). If there are any unsaved changes to a sce- nario when you attempt to either create a new scenario or view Results, a dialog box prompts you to save these changes. The fields where changes have been made are marked in red, and you can choose to continue and save the changes by click- ing Yes in the dialog box, continue and discard the change(s) by clicking No, or click Cancel and remain in the Scenario Inputs window. However, on closing the file, there will never be a prompt to save changes, even if the 3–RESULTS tab has been changed from Summary to Details view (or vice versa). This is because the Reliability Module has been set up to automatically save at points such as this, for the user’s convenience.

37 Figure 3.10. Screenshot of the Details view of RESULTS—Tab 3. Table 3.9. Buttons in the Results Tab—Tab 3 Buttons in the Results Tab Scenario Inputs Displays up the Scenario Inputs window. Details (if in Summary view) Displays the hourly results for a particular scenario. Summary (if in Details view) The default view, showing aggregated results for all scenarios. Ctrl+Shift+D Pressing Ctrl+Shift+D displays the Calculation Debugger. The Calculation Debugger is a dialog box that shows many more variables, calculated in the background, which are not needed for normal use.

38 Figure 3.11. Screenshots of Calculation Debugger. Figure 3.12. Screenshot of Scenario Inputs, including dialog box to Save Before Continuing.