Value of Travel Time Reliability in Transportation Decision Making: Proof of Concept—Maryland (2014) / Chapter Skim
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6Background and application of the Method P a r t 1
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7C h a P t e r 1 Introduction The topic of travel time reliability has been a significant focus in the transportation systems management and operations (TSM&O) community during recent years.
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8of travel time reliability by using speed and volume data as input. The options-theoretic approach introduced by the SHRP 2 L11 uses an analogy where premiums are set for an insurance policy on guaranteed speed levels.
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9sources (i.e., RP versus SP) , these values show significant variation depending on the reliability measures used and modeling approach (e.g., heterogeneity, travel time unit, and choice dimensions considered)
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10 in the Netherlands, which led to 5,760 respondents. In the second survey, 1,430 respondents were recruited in the same manner as for the previous research study; namely, at petrol stations along the motorways, parking garages, train stations, tram and bus stops, airports (Schiphol and Eindhoven)
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11 obtain district totals. The recurrent and nonrecurrent delays for each sample facility are computed for three prototypical days (weekday, weekend, holiday)
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12 e, f = coefficients reflecting the impact of income and occupancy on the perception of cost respectively; STD = day-to-day standard deviation of the travel time; and c = coefficients reflecting the impact of travel time (un) reliability.
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13 be generated by static assignment methods, while advanced methods such as Dynamic Traffic Assignment (DTA) can be more beneficial, or rather necessary, as stated in Chapter 3 of the SHRP 2 C04 draft report: It is important to note that making this approach operational within the framework of regional travel models requires explicitly deriving these measures from simulation of travel time distributions, as well as adopting assumptions regarding the ways in which travelers acquire information about the uncertain situation they are about to experience.
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14 with relative ease has yet to be developed. Therefore, it is recommended that a range of values be used in the absence of empirical data and sources to estimate them.
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15 C h a p t e r 2 Through the adoption of various measurement and reporting methodologies and tools, Maryland State Highway Administration (SHA) has been able to quantify current mobility and reliability conditions and trends on its highways.
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16 truck travel (outlined with blue dots)
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17 methodology to the ten directional corridor cases in Maryland and their various results. Incorporate Value of travel time reliability into project evaluation process The local VTTR calculated using the travel-time data-driven methodology for estimating RR/VTTR was used to replace the current value in the baseline approach.
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18 C h a p t e r 3 Overview of process Used to apply Value of travel time reliability in Maryland The high-level steps used to incorporate value of travel time reliability (VTTR) into the Maryland State Highway Administration (SHA)
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19 the Maryland Department of Transportation (MDOT) , local jurisdictions, and Metropolitan Planning Organizations (MPOs)
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20 on the extent of reliability by reporting, for example, the percent of peak hour vehicle miles traveled (VMT) experiencing unreliable (PTI > 1.5)
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21 of lanes that service the volume, percent trucks, and speeds based on vehicle probe data. The VISSIM models are calibrated using the following criteria for each roadway segment along I-695 between interchanges as follows: • Traffic volumes must be within 10% of the input volume.
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22 44 Bridge widening that is necessary (outside of restriping alternatives) is by separate preceding contract unless otherwise noted.
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23 2012 (after) than before (2010)
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24 RR value. One of the objectives of this research was to develop a methodology to defend this number or provide a basis for changing it based on local data.
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25 Travelers are assumed to incur penalties associated with arriving earlier or later than their planned arrival times at their destination. In the proposed method, these penalties are defined as a fixed portion of the amount of time by which the traveler is early or late relative to their planned arrival time.
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26 average about 6 minutes smaller. Note that due to bias in selfreporting, Census Bureau estimates tend to be an overestimate.
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27 northbound MD 41 (Perring Parkway) to MD 43 eastbound.
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28 Table 3.5. Proposed Improvement Projects in the Northeast Quadrant of the Baltimore Beltway Project Code Location Improvement Description NE1 I-695 inner loop: MD 139 (Charles Street)
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29 Table 3.6. Improvement Projects Benefit–Cost Analysis Under Current Value of Reliability (RR 0.75)
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30 6. The assumed pavement section consists of 2-in.
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31 Table 3.7. Sensitivity Analysis on Improvement Project Rankings with Various Reliability Ratios Project Code 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95 1 1.05 1.1 1.15 1.2 SW1 11 11 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10 SW2 12 12 12 12 12 12 12 12 11 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 12 SW3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 SW4 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 NW1 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 4 4 4 4 5 5 5 5 NW2 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 15 NW3 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 4 NW4 10 10 11 11 11 11 11 11 13 13 13 13 13 13 14 14 14 14 14 14 14 14 14 14 14 NE1 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 NE2 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 16 NE3 13 13 13 13 13 13 13 13 12 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 11 NE4 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 NE5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 NE6 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 NE7 14 14 14 14 14 14 14 14 14 14 14 14 14 14 13 13 13 13 13 13 13 13 13 13 13 NE8 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
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32 Figure 3.8. Improvement project rankings under various reliability ratios.
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33 Figure 3.9. Impacts of different reliability ratio and budget levels on selected improvement projects.
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34 Future research directions should include integration of a calibrated reliability ratio model into travel behavior models. One of the integral findings of SHRP 2 L35B is the datadriven empirical model to compute reliability ratio (RR)
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35 captured. System benefits were estimated based on the resulting improved travel time reliability at the O-D level.
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36 Do rc hes ter Qu ee n An ne 's Fre der ick Pri nc e Ge or ge' s 0 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 Travel Time Savings ($) Travel Time Reliability Savings ($)
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37 Figure 3.15. Travel time saving per trip comparing future year -- build with future year -- no build.
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38 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4 NB base year NB future year SB base year SB future year Travel time savings(min) /Traveler Travel time reliability savings(min)
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39 Figure 3.18. Explanation of the underlying analogy for the travel-time data-driven methodology.
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40 value of reliability uses large quantities of historical travel time data based on probe data, along with a value of typical/usual travel time (VOTT) , and produces a RR along with a value of reliability (VTTR)
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41 The overall response from SHA, including management, was positive. Interestingly, and perhaps not surprisingly, SHA management did want to learn more about the technical details regarding the travel-time data-driven methodology developed.
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42 Conclusions and Suggested Research Overall Findings An overall conclusion from this research suggests that agencies who do not account for VTTR in their BCA processes might be undervaluing project benefits resulting from improvements to trip reliability. Valuation tools and techniques, both existing and newly developed as a result of this research, along with a significant body of literature, provide a basis for incorporating VTTR in an agency's BCA process.
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43 The other assumptions regarding the payoff function used in the proposed method needs further validation based on local data. Survey-based measurements of penalties (or rewards)
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44 References: Part 1 Asensio, J., and Matas, A
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45 International Transport Forum.
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46 Overview of Maryland Department of Transportation Planning The Maryland Department of Transportation (MDOT) is one of the state's largest agencies, with nearly 9,000 employ ees committed to delivering a balanced and sustainable multi modal transportation system for all Maryland's residents and businesses.
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47 Figure A.1. Maryland Department of Transportation with its modal administrations.
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48 Overview of SHA investment decision-Making Process The Maryland State Highway Administration receives high way transportation funds from MDOT, and works with Metro politan Planning Organizations (MPOs) and local jurisdictions to allocate funds to meet highway preservation and capital programming needs.
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49 phases and cost estimation procedures. The HNI lists only major capital improvement projects (i.e., no system preserva tion projects)
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50 Maryland State Highway Administration Budget Allocation -- example from FY2011 SHA's annual expenditure can be divided into two distinct areas with each area further breaking down into three main categories: • Capital ($738.3M) 44 Construction ($634.3M)
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51 funding is programmed to carry out the project. Once plan ning and programming efforts have been conducted, the project then (typically)
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52 projections for each project in yearly increments. CHART updates its projects and budgets every year for submittal to the MDOT CTP, showing the latest CHART capital investment sixyear projection.
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53 planning and programming process into project design and deployment. Other example of a SHA Programming Process: Crash Prevention, Safety and Spot improvement, and intersection Capacity improvement Maryland has a number of additional internal project identi fication and programming processes, and following are three specific programs (Crash Prevention, Safety and Spot Improve ment, and Intersection Capacity Improvement)
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54 • Benefit/Cost/Difficulty (from "Expensive/Difficult" to "Cheap/Easy") Safety and Spot improvement Program Candidate projects are given a rating based on the categories of Congestion/Operations (80%)
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55 MATLAB Code A p p e n d i x B GBM Calibration and Hypothesis Testing Function: function [tt_mean,alpha,sigma,h] =gbm_calibrate(time,tt,period,corridor_name,segment_na me,L,fig_handle,axis_handle)
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56 % Black-Scholes formula % Input: % alpha: long-term trend (%) % sigma: instantaneous variation (%)
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57 p_prime=1-p; % forward binary tree development tree=nan(n+1,n+1) ; tree(1,1)
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58 Presentation to Maryland State Highway Administration A p p e n d i x C
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59 L35B Local Methods for Modeling, economic evaluation, Justification, and Use of the Value of Travel Time Reliability in Transportation decision Making
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