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From page 1... ... 1Organization Project SHRP 2 C04, Improving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand, reviewed and advanced the state of the practice in modeling the effects of highway congestion and highway pricing on travelers' decisions, including choices of facility, route, mode, and time of day (TOD) Read the entire page →
From page 2... ... 2practice. Conclusions, main findings, and recommendations for future research are summarized in Chapter 6. Read the entire page →
From page 3... ... 3A typical formulation of the utility (U) of a highway alternative can be written as a linear function of trip time and cost (including all forms of pricing) Read the entire page →
From page 4... ... 4In practice, engineers often do not have accurate data on travelers' time constraints or trip frequency, particularly for forecasts. Trip purpose serves as a proxy, as regular trips to work or school tend to be the most frequent trips, and are often made by those with the busiest schedules. Read the entire page →
From page 5... ... 5can be translated into measures such as standard deviation or buffer time represented by the 80th or 90th percentile of travel time versus the median. Estimates of the distribution for an entire trip distance from origin to destination (O-D) Read the entire page →
From page 6... ... 6Stated Preference Data SP data are responses by survey respondents to questions about hypothetical travel situations. These data are collected in SP choice experiments that are customized around the context of an actual reported trip that a respondent has recently made. Read the entire page →
From page 7... ... 7Table ES.2 summarizes the aspects of choice represented in each of the primary data sets. The New York regional household travel survey RP data set supports the widest range of modeling. Read the entire page →
From page 8... ... 8Analysis Approach for Improved Demand Modeling The team tested the same types of variables and functional specifications of the generalized cost on multiple data sets and choice contexts and looked for consistencies that suggested the most reliable practices and productive paths for modeling the effects of highway congestion and pricing. This systematic approach involved the following steps: • The first models used the most basic specifications for each data set and each choice dimension (e.g., route or mode choice) Read the entire page →
From page 9... ... 9The suggested form for accounting for travelers' perceptions of travel costs corresponds to the monetary cost scaled by power functions of both income and vehicle occupancy: (ES.4) Read the entire page →
From page 10... ... 10 Through these primary choice dimensions, the impacts of congestion and pricing can be further propagated through the model system chain to affect destination choice, trip frequency, and other choice dimensions. Impacts of Congestion and Pricing on Travel Demand: Behavioral Insights and Implications for Policy and Modeling Key study findings are related to the estimated values of the parameters in Equation ES.2. Read the entire page →
From page 11... ... 11 Income and Willingness to Pay Key Finding Household and personal income has a strong relationship with VOT and willingness to pay, but the relationship appears to be less then linear. To account for the income effect, cost variables in travel models (including tolls) Read the entire page →
From page 12... ... 12 the metropolitan area, however, as large clusters of high-income jobs may be present in the central business district (CBD) Read the entire page →
From page 13... ... 13 were tested, but the measure that produced the most consistent results was the standard deviation of travel time divided by journey distance. Evaluating the estimation results to impute the reliability ratio (the value of reducing the standard deviation of travel time by 1 minute divided by the value of reducing the average travel time by 1 minute, or VOR/VOT for an average trip distance) Read the entire page →
From page 14... ... 14 Implications for Modeling Because VOT and VOR tend to vary with O-D trip distance, using a constant VOT and VOR for a wide range of trip lengths is an unreasonable simplification pertinent to most travel models. For the most accurate predictions, this distinction should be used in demand-forecasting models. Read the entire page →
From page 15... ... 15 Less likely responses to changes in congestion or pricing are changes in the choice of destination locations, the rescheduling of trips to very different times of day, and changes in the frequency of making trips from home. These types of changes are the least likely for activities that are most constrained, such as work and school trips or medical appointments. Read the entire page →
From page 16... ... 16 Implications for Modeling Segmentation is crucial for policy evaluation, and modeling systems should be segmented according to the main effects described above. Traditional four-step demand models and static traffic assignments, still the most common tools in practice, are of little use because limited segmentation is one of their major constraints. Read the entire page →
From page 17... ... 17 projects and policies, including pricing. The entire issue of improving travel time reliability can finally shift from qualitative analysis to quantitative analysis. Read the entire page →
From page 18... ... 18 demand side, and it can be readily extended to an activity-based integration of demand models and an activity-based dynamic traffic microassignment model. Dynamic mode share and toll road usage results of the proposed integrated model are demonstrated on the large-scale New York metropolitan network. Read the entire page →
From page 19... ... 19 highway utility function can be incorporated by including the suggested generalized cost components in the mode choice utilities for highway modes. The mode choice model has to differentiate highway modes by three to four occupancy categories and toll or nontoll route, which would result in six to eight highway modes. Read the entire page →
From page 20... ... 20 measure of reliability, but it can serve as a proxy for reliability because the perceived weight of each minute spent in congestion is a consequence of associated unreliability. • First Direct Measure: Time Variability (Distribution) Read the entire page →
From page 21... ... 21 Table ES.4. Recommended Coefficient Values Travel Purpose Model Coefficients Examples of Population and Travel Characteristics Derived Measures Toll Bias Time (min) Read the entire page →
From page 22... ... 22 From Work and Business -0.95 -0.0425 0.02024 -0.000266 -1.44 -0.545 0.6 0.8 30,000 1.0 5.0 -0.0465 20.4 -0.0030 9.4 22.1 2.34 30,000 2.0 5.0 -0.0465 20.4 -0.0017 16.4 38.4 2.34 30,000 3.0 5.0 -0.0465 20.4 -0.0012 22.7 53.1 2.34 30,000 1.0 10.0 -0.0500 19.0 -0.0030 10.1 11.0 1.09 30,000 2.0 10.0 -0.0500 19.0 -0.0017 17.6 19.2 1.09 30,000 3.0 10.0 -0.0500 19.0 -0.0012 24.3 26.6 1.09 30,000 1.0 20.0 -0.0552 17.2 -0.0030 11.2 5.5 0.49 30,000 2.0 20.0 -0.0552 17.2 -0.0017 19.4 9.6 0.49 30,000 3.0 20.0 -0.0552 17.2 -0.0012 26.9 13.3 0.49 60,000 1.0 5.0 -0.0465 20.4 -0.0020 14.3 33.4 2.34 60,000 2.0 5.0 -0.0465 20.4 -0.0011 24.8 58.2 2.34 60,000 3.0 5.0 -0.0465 20.4 -0.0008 34.4 80.5 2.34 60,000 1.0 10.0 -0.0500 19.0 -0.0020 15.3 16.7 1.09 60,000 2.0 10.0 -0.0500 19.0 -0.0011 26.7 29.1 1.09 60,000 3.0 10.0 -0.0500 19.0 -0.0008 36.9 40.3 1.09 60,000 1.0 20.0 -0.0552 17.2 -0.0020 16.9 8.4 0.49 60,000 2.0 20.0 -0.0552 17.2 -0.0011 29.5 14.6 0.49 60,000 3.0 20.0 -0.0552 17.2 -0.0008 40.8 20.1 0.49 100,000 1.0 5.0 -0.0465 20.4 -0.0014 19.4 45.4 2.34 100,000 2.0 5.0 -0.0465 20.4 -0.0008 33.7 79.1 2.34 100,000 3.0 5.0 -0.0465 20.4 -0.0006 46.7 109.4 2.34 100,000 1.0 10.0 -0.0500 19.0 -0.0014 20.8 22.7 1.09 100,000 2.0 10.0 -0.0500 19.0 -0.0008 36.3 39.5 1.09 100,000 3.0 10.0 -0.0500 19.0 -0.0006 50.1 54.7 1.09 100,000 1.0 20.0 -0.0552 17.2 -0.0014 23.0 11.4 0.49 100,000 2.0 20.0 -0.0552 17.2 -0.0008 40.0 19.8 0.49 100,000 3.0 20.0 -0.0552 17.2 -0.0006 55.4 27.3 0.49 Table ES.4. Recommended Coefficient Values (continued) Read the entire page →
From page 23... ... 23 Nonwork -1.2 -0.0335 0 0 -0.5228 -0.418 0.5 0.7 30,000 1.0 5.0 -0.0335 35.8 -0.0030 6.7 16.6 2.50 30,000 2.0 5.0 -0.0335 35.8 -0.0019 10.8 27.0 2.50 30,000 3.0 5.0 -0.0335 35.8 -0.0014 14.4 35.9 2.50 30,000 1.0 10.0 -0.0335 35.8 -0.0030 6.7 8.3 1.25 30,000 2.0 10.0 -0.0335 35.8 -0.0019 10.8 13.5 1.25 30,000 3.0 10.0 -0.0335 35.8 -0.0014 14.4 17.9 1.25 30,000 1.0 20.0 -0.0335 35.8 -0.0030 6.7 4.2 0.62 30,000 2.0 20.0 -0.0335 35.8 -0.0019 10.8 6.7 0.62 30,000 3.0 20.0 -0.0335 35.8 -0.0014 14.4 9.0 0.62 60,000 1.0 5.0 -0.0335 35.8 -0.0021 9.4 23.5 2.50 60,000 2.0 5.0 -0.0335 35.8 -0.0013 15.3 38.2 2.50 60,000 3.0 5.0 -0.0335 35.8 -0.0010 20.3 50.7 2.50 60,000 1.0 10.0 -0.0335 35.8 -0.0021 9.4 11.8 1.25 60,000 2.0 10.0 -0.0335 35.8 -0.0013 15.3 19.1 1.25 60,000 3.0 10.0 -0.0335 35.8 -0.0010 20.3 25.4 1.25 60,000 1.0 20.0 -0.0335 35.8 -0.0021 9.4 5.9 0.62 60,000 2.0 20.0 -0.0335 35.8 -0.0013 15.3 9.5 0.62 60,000 3.0 20.0 -0.0335 35.8 -0.0010 20.3 12.7 0.62 100,000 1.0 5.0 -0.0335 35.8 -0.0017 12.2 30.3 2.50 100,000 2.0 5.0 -0.0335 35.8 -0.0010 19.8 49.3 2.50 100,000 3.0 5.0 -0.0335 35.8 -0.0008 26.2 65.5 2.50 100,000 1.0 10.0 -0.0335 35.8 -0.0017 12.2 15.2 1.25 100,000 2.0 10.0 -0.0335 35.8 -0.0010 19.8 24.6 1.25 100,000 3.0 10.0 -0.0335 35.8 -0.0008 26.2 32.7 1.25 100,000 1.0 20.0 -0.0335 35.8 -0.0017 12.2 7.6 0.62 100,000 2.0 20.0 -0.0335 35.8 -0.0010 19.8 12.3 0.62 100,000 3.0 20.0 -0.0335 35.8 -0.0008 26.2 16.4 0.62 Note: SD = Standard deviation. Table ES.4. Read the entire page →

From page 1...

... 1Organization Project SHRP 2 C04, Improving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand, reviewed and advanced the state of the practice in modeling the effects of highway congestion and highway pricing on travelers' decisions, including choices of facility, route, mode, and time of day (TOD)