Incorporating Reliability Performance Measures into Operations and Planning Modeling Tools: Reference Material (2014) / Chapter Skim
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From page 191... ...
191 2. INCORPORATION OF TRAVEL TIME RELIABILITY 2.1 Approaches to Quantify Reliability and Its Impacts 2.1.1 Construction of User-Centric Network Reliability Measures In summary, the following two important aspects of the problem need to be taken into account when the user's perspective on reliability [and level of service (LOS)
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192 is partially a consequence of associated unreliability. This is the simplest measure that can be readily incorporated into both demand models and network simulation tools and equilibrated between them.
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193 a participation in the corresponding activity (or gain if travel time proved to be shorter) (Supernak 1992; Kitamura and Supernak 1997; Tseng and Verhoef 2008)
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194 Table 2.1. Methods to Quantify Reliability Impacts on Travel Choices Method Representation of Travel Time Impact on Travel Choices Through Generalized Cost Function Special Features Needed Perceived highway time by congestion levels Segmented by congestion levels Travel time weighted by congestion levels Mean-variance (travel time distribution measures)
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195 assume that PDT is optimized by the user based on the PAT and mean travel time only (e.g., by subtracting mean travel time from PAT)
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196 late arrival. More detailed explanation of the prospect theory in relation to travel time reliability can be found in Appendix C
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197 Table 2.3. Recommended Highway Time Weight by Congestion Levels Travel Time Conditions Weight LOS V/C Free Flow 1.00 A, B Under 0.5 Busy 1.05 C 0.5-0.7 Light Congestion 1.10 D 0.7-0.8 Heavy Congestion 1.20 E 0.8-1.0 Stop Start 1.40 F 1.0-1.2 Gridlock 1.80 F 1.2+ The weights applied have to be consistent between traffic assignment and mode choice.
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198 Table 2.4. Recommended Values of Parameters for Generalized Cost Function with Reliability Travel Purpose Examples of Population/Travel Model Coefficients and Derived Measures Household Income, $/vear Car Occupancy Distance, miles Time Coefficient Cost Coefficient Cost for SD min VOT, $/h VOR, $/h Reliability Ratio Work and business 30,000 1.0 5.0 -0.0425 -0.0026 -0.1042 9.9 24.3 2.45 30,000 2.0 5.0 -0.0425 -0.0015 -0.1042 17.2 42.3 2.45 30,000 3.0 5.0 -0.0425 -0.0011 -0.1042 23.9 58.5 2.45 30,000 1.0 10.0 -0.0425 -0.0026 -0.0521 9.9 12.1 1.23 30,000 2.0 10.0 -0.0425 -0.0015 -0.0521 17.2 21.1 1.23 30,000 3.0 10.0 -0.0425 -0.0011 -0.0521 23.9 29.2 1.23 30,000 1.0 20.0 -0.0425 -0.0026 -0.0260 9.9 6.1 0.61 30,000 2.0 20.0 -0.0425 -0.0015 -0.0260 17.2 10.6 0.61 30,000 3.0 20.0 -0.0425 -0.0011 -0.0260 23.9 14.6 0.61 60,000 1.0 5.0 -0.0425 -0.0017 -0.1042 15.0 36.8 2.45 60.000 2.0 5.0 -0.0425 -0.0010 -0.1042 26.1 64.1 2.45 60,000 3.0 5.0 -0.0425 -0.0007 -0.1042 36.2 88.6 2.45 60,000 1.0 10.0 -0.0425 -0.0017 -0.0521 15.0 18.4 1.23 60,000 2.0 10.0 -0.0425 -0.0010 -0.0521 26.1 32.0 1.23 60,000 3.0 10.0 -0.0425 -0.0007 -0.0521 36.2 44.3 1.23 60.000 1.0 20.0 -0.0425 -0.0017 -0.0260 15.0 9.2 0.61 60,000 2.0 20.0 -0.0425 -0.0010 -0.0260 26.1 16.0 0.61 60,000 3.0 20.0 -0.0425 -0.0007 -0.0260 36.2 22.2 0.61 100,000 1.0 5.0 -0.0425 -0.0013 -0.1042 20.4 50.0 2.45 100.000 2.0 5.0 -0.0425 -0.0007 -0.1042 35.5 87.1 2.45 100,000 3.0 5.0 -0.0425 -0.0005 -0.1042 49.1 120.4 2.45 100,000 1.0 10.0 -0.0425 -0.0013 -0.0521 20.4 25.0 1.23 100,000 2.0 10.0 -0.0425 -0.0007 -0.0521 35.5 43.5 1.23 100,000 3.0 10.0 -0.0425 -0.0005 -0.0521 49.1 60.2 1.23 100,000 1.0 20.0 -0.0425 -0.0013 -0.0260 20.4 12.5 0.61 100,000 2.0 20.0 -0.0425 -0.0007 -0.0260 35.5 21.8 0.61 100,000 3.0 20.0 -0.0425 -0.0005 -0.0260 49.1 30.1 0.61 Nonwork 30.000 1.0 5.0 -0.0335 -0.0030 -0.0697 6.7 13.8 2.08 30,000 2.0 5.0 -0.0335 -0.0019 -0.0697 10.8 22.5 2.08 30,000 3.0 5.0 -0.0335 -0.0014 -0.0697 14.4 29.9 2.08 30,000 1.0 10.0 -0.0335 -0.0030 -0.0348 6.7 6.9 1.04 30,000 2.0 10.0 -0.0335 -0.0019 -0.0348 10.8 11.2 1.04 30,000 3.0 10.0 -0.0335 -0.0014 -0.0348 14.4 14.9 1.04 30,000 1.0 20.0 -0.03351 -0.0030 -0.0174 6.7 3.5 0.52 30.000 2.0 20.0 -0.0335 -0.0019 -0.0174 10.8 5.6 0.52 30.000 3.0 20.0 -0.0335 -0.0014 -0.0174 14.4 7.5 0.52 60.000 1.0 5.0 -0.0335 -0.0021 -0.0697 9.4 19.6 2.08 60,000 2.0 5.0 -0.0335 -0.0013 -0.0697 15.3 31.8 2.08 60,000 3.0 5.0 -0.0335 -0.0010 -0.0697 20.3 42.3 2.08 60.000 1.0 10.0 -0.0335 -0.0021 -0.0348 9.4 9.8 1.04 60.000 2.0 10.0 -0.0335 -0.0013 -0.0348 15.3 15.9 1.04 60,000 3.0 10.0 -0.0335 -0.0010 -0.0348 20.3 21.1 1.04 60,000 1.0 20.0 -0.0335 -0.0021 -0.0174 9.4 4.9 0.52 50,000 2.0 20.0 -0.0335 -0.0013 -0.0174 15.3 8.0 0.52 60.000 3.0 20.0 -0.0335 -0.0010 -0.0174 20.3 10.6 0.52 100.000 1.0 5.0 -0.0335 -0.0017 -0.0697 12.2 25.3 2.08 100,000 2.0 5.0 -0.0335 -0.0010 -0.0697 19.8 41.1 2.08 100,000 3.0 5.0 -0.0335 -0.0008 -0.0697 26.2 54.6 2.08 100,000 1.0 10.0 -0.0335 -0.0017 -0.0348 12.2 12.6 1.04 100.000 2.0 10.0 -0.0335 -0.0010 -0.0348 19.8 20.5 1.04 100,000 3.0 10.0 -0.0335 -0.0008 -0.0348 26.2 27.3 1.04 100,000 1.0 20.0 -0.0335 -0.0017 -0.0174 12.2 6.3 0.52 100,000 2.0 20.0 -0.0335 -0.0010 -0.0174 19.8 10.3 0.52 100.000 3.0 20.0 -0.0335 -0.0008 -0.0174 26.2 13.6 0.52
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199 2.2.3 Schedule Delay Cost in Demand Model There are multiple estimated models with schedule delay cost as described in Appendix A The majority of them were estimated using different stated preference (SP)
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200 Demand Network TOD choice Mode choice Route choice PDT PAT Travel time distribution Schedule delay cost Schedule delay penalty functions TOD choice Mode choice Route choice & PDT optimization PDT PAT Schedule delay cost Schedule delay penalty functions Demand Network 1 s t a p p ro a c h : s c h e d u le d e la y c o s t c a lc u la ti o n i n d e m a n d m o d e l 2 n d a p p ro a c h : s c h e d u le d e la y c o s t c a lc u la ti o n i n n e tw o rk m o d e l Figure 2.1. Incorporation of schedule delay cost into demand model (mode choice)
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201 TOD choice model produces probabilities for each activity to be undertaken at certain time in a form of joint start (arrival) and end (departure)
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202 This means that the reliability measure of interest has to be incorporated in the route choice and generated at the O-D level to feed into the demand model. 2.3.1 Perceived Highway Time in Network Simulation This method is easy to implement without a significant restructuring of the network assignment model whether a user equilibrium (UE)
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203 2.3.3 Schedule Delay Cost in Network Simulation In the previous section, two possible approaches that differ in how and where the schedule delay cost component is calculated -- see Figure 2.1 -- were outlined. With the first approach, schedule delay cost is calculated in the demand model as part of the mode utility calculation for highway modes.
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204 supply side of activities and, specifically, the generation of reliability measures (some of them beyond the focused approaches outlined in the previous subsections)
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205 Table 2.5. Operational Measures of Reliability Measure Impact on Demand Supply Side Estimation/SP Estimation/RP Application Perceived Highway Time by Level of Congestion: Travel Time by LOS Differential time coefficients Stated travel conditions Network skims (single simulation)
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206 Measure Impact on Demand Supply Side Estimation/SP Estimation/RP Application Opening/closing hours for nonmandatory activity by type and location Constraint for time-of-day choice and variables for schedule delay cost Stated opening/closing hours Reported or surveyed opening/closing hours Surveyed, externally estimated, or synthesized opening/closing hours by activity type and location Situational fixed schedule for special activities (air flight, train trips, appointments, picking up/dropping off passengers) Constraint for time-of-day choice and variables for schedule delay cost Stated situational constraints Reported situational constraints Synthesized situational constraints 2.4.2 Challenges of Mean-Variance Approach in Network Models The crux of the modeling challenge is that reliability measures have to be generated at the trip (route)
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207 uniqueness of the solution can be proved, and practical methods for finding this solution can be developed) can be contrasted to a loose coupling with the demand model by means of iterative application with feedbacks [referred to as "shell" approach in Institute for Transportation Studies (2008]
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208 additive, in either a single-run framework or a multiple-run framework, are proposed below. Possible combinations of the four outlined aspects and perspectives to build an operational model are summarized in Table 2.6.
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209 The methods identified as realistic for real-size networks and operational models are in bold. The rest of the current report largely focuses on these methods.
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210 1. Implicitly in a single model run: in which travel time is implicitly treated as a random variable and its distribution, or some parameters of this distribution, such as mean and variance, are described analytically and used in the modeling process.
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