Improving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand (2012) / Chapter Skim
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Pages 132-157
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132 C h a p t e r 6 Impacts of Congestion and pricing on travel Demand: Behavioral Insights and policy Implications Variations in Value of Time Across Highway Users Key Finding Value of time (VOT) varies widely across the traveling population, from $5/hour through $50/hour across income groups, vehicle occupancies, and travel purposes.
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133 States will be willing to pay $20 or more per hour of travel time saved. Furthermore, it has long been understood that there is a wide variation in VOT from individual to individual that cannot be readily related to data on person and household characteristics or trip context.
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134 be purchased by those who can most afford them, and equity considerations cannot be discounted. Of course, lower-income individuals can also derive benefits in the form of increased options, as well as improvements in traffic conditions if capacity in the entire system can be increased through priced facilities.
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135 time savings in particular, there is not much of an income impact between, say, a household with a $200,000 income versus a household with a $400,000 income that is expressed in a lower income exponent e that makes the curve flatter. This variable is useful for examining expected behaviors when future conditions change.
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136 different reasons. Income effects are due primarily to monetary budget constraints, but occupancy effects are due primarily to the possibility of cost sharing among occupants.
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137 This means that time-of-day (TOD) pricing and other peakspreading policies will tend to be less successful in influencing the behavior of carpoolers.
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138 as perceived highway time by congestion levels, as explained below) can also be applied with the existing model structures and network simulation procedures.
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139 The team believes that the lower VOT for long-distance commuters is a manifestation of restructuring the daily activity–travel pattern. In particular, long-distance commuters tend to simplify their patterns and not have many additional out-of-home activities on the day of their regular commute because the work activity and commuting consume most of the daily schedule.
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140 of service in terms of travel time savings and improvements in reliability. In this sense, tolling existing facilities in order to collect revenue, but without a substantial LOS improvement, would generally be perceived very negatively by highway users.
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141 highest to the lowest propensity to change behavior, these responses are as follows: • Primary. Change lane or route type or make minor shifts in departure time (up to 1 hour earlier or later)
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142 and cost of traveling by car can have a marked influence on such decisions, even if they are not the primary decision factors. In Chapter 3, the team outlines an approach to modeling a wide range of possible longer-term responses to congestion and pricing by means of accessibility measures that are derived from the estimated primary choice of route, mode, and TOD.
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143 Table 6.1. Highway User Segmentation Dimension for User Segmentation Previous Research Current Study Future Research Socioeconomic Segments of Population by Household income Positively correlated with VOT (frequently linearly)
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144 Trip length or tour distance VOT grows with distance (although weaker than linearly) because of marginal cost damping and time valuing For commuting to work, VOT grows with distance but drops for distances over 40 miles following an inverse U shape; for nonwork trips no significant effect; distance-based biases are significant for rail modes in mode choice Explicitly account for time and budget constraints Toll payment method Electronic payment is favored by users beyond direct time and cost consideration because of a different perception (not out-of-pocket)
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145 have a fixed schedule; more than three-quarters have at least some flexibility. Schedule flexibility logically proved to be strongly correlated with worker's income.
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146 Data Limitations and Global Positioning System–Based Data Collection Methods Key Finding The availability of data sets adequate to support the analyses undertaken in this study was extremely limited, especially for the aspect of travel time reliability. The culture and methodology for collecting needed travel time variability measures with O-D travel time trajectory data (not just link-level data)
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147 conditions, nor are the trajectories from buses and taxis particularly useful for analysis purposes. With the proliferation and wide-area penetration of wireless (cellular)
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148 been proposed and demonstrated on large actual networks in this research effort. • Traveler Heterogeneity.
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149 The current research has shown that at the first two levels of transferability, the model approaches and structures can be effectively generalized. Most of the functional forms for highway utility proved to be statistically significant in such different regions as New York City and Seattle.
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150 more than 20 vehicle classes and long run times for large regional networks. • DTA with Microsimulation of Individual Vehicles.
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151 and calibration of all temporal utility profiles for all possible activities and all person types is significant. This complexity made it unrealistic to adopt this approach as the main concept for the current project.
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152 153 (continued on next page) Table 6.3.
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154 155 Travel Purpose Model Coefficients Examples of Population and Travel Characteristics Derived Measures Toll Bias Time (min) Distance (mi)
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156 and pricing on travel demand in a comprehensive framework of various travel dimensions including auto route choice, mode choice, and TOD choice. However, like any research, it had to be limited to a finite number of model components and bound to available data sets.
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157 and estimate a microeconomic model of travel behavior analogous to the daily activity pattern choice model, and compare the results. • It is important to ensure that the results of the current and subsequent research be applicable in the framework of an operational travel model.
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158 Incorporating Reliability Performance Measures in Operations and Planning Modeling Tools. These data include • A travel choices data set from Seattle that could be analyzed more extensively for supply-side variability, especially experienced variability; • Simulated variability (objective and experienced)
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159 on the findings from Project L04 in the C04 framework to leverage analytic relations that may be used when primary data are unavailable or only partially available. Actions needed to implement the third implementation opportunity include the following: • Development of an application "primer" for metropolitan planning organizations and state and local agencies to explain what to do and how to do it given available modeling tools (e.g., static, dynamic, stand alone, or integrated)
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