Improving Our Understanding of How Highway Congestion and Pricing Affect Travel Demand (2012) / Chapter Skim
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38 C h a p t e r 3 This chapter provides a detailed technical discussion of the main focus of the C04 research project, which was the specification and estimation of new advanced forms of travel demand models that aim to substantially improve how road pricing and congestion can be more fully and realistically modeled for transportation policy and planning. This chapter describes the results of model estimation research in terms of the somewhat more general findings presented in two key subsections: Overview of Section, Approach, and Main Findings; and Summary Comparison and Synthesis.
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39 comparable to a choice between managed and free lanes (or between toll and nontoll roads) for a particular trip.
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40 There have been some initial attempts to formulate and estimate choice models related to the acquisition of transponders (Yan and Small 2002) simultaneously with preroute, departure time, or car occupancy (or some combination of these factors)
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41 k = 3 represents parking search time; k = 4 represents walk access or egress time (e.g., from the parking lot to the trip destination) ; k = 5 represents extra time associated with carpooling (picking up and dropping off passengers)
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42 The segmentation of activities may best be addressed by the following: • Travel Purpose. Work trips, and, in particular, businessrelated trips, normally are associated with higher VOT than trips for nonwork purposes (Dehghani et al.
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43 and estimate explicitly, as well as to include in applied models (Spear 2005; Vovsha, Davidson et al.
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44 distribution (reliability skims) because of the dependence of travel times across adjacent links due to a mutual traffic flow.
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45 The choice framework presented in the stated preference (SP) survey context included only route choice.
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46 The third example is taken from the research work of Wardman et al.
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47 travel times and tolls was significantly enriched by combining RP data from actual choices with SP data from hypothetical situations that were aligned with the pricing experiment. Distribution of travel times was calculated based on the independently observed data.
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48 now have such data available for important highway segments. A problem yet to be resolved, however, is that when calculating the travel time reliability measure over the entire O-D path, the highway links cannot be considered independent.
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49 specifications. Interestingly, as reported by the authors, in the presence of explicit schedule delay cost, the travel time variability measure (standard deviation)
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50 profiles can be specified as two-dimensional functions U(t,d) , where d denotes the activity duration until moment t.
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51 The planned and actual utilities can be written as shown in Equations 3.12 and 3.13, respectively: ∫)
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52 An alternative approach is to allow further differences by expressing the parameters that represent preference weights (an and bn) as random parameters, as opposed to point estimates, such that the distribution for these preference weights can obtained and used in the derivation of value of travel time, which in turn will be distributed across the population.
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53 Discrete Choice Model Form and Estimation Issues The model form these models take is dictated partly by assumptions on the error terms, which in turn are dictated by the need to account for unobserved heterogeneity. In Equation 3.19, since µn is not observed, the term µnTTj becomes part of the unobserved component of the utility njε, so it can be expressed as (3.21)
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54 distribution. The normal distribution has been shown to cause some problems when applied to coefficients of undesirable attributes, such as travel time and cost, due to the possibility of positive coefficient values for these attributes (Hensher and Greene 2000; Cirillo and Axhausen 2006)
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55 1997 and 1998. In the survey, each auto trip has an attribute of toll value paid.
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56 • A direct measure of travel time reliability like standard deviation of travel time or standard deviation of travel time per unit distance proved to be statistically significant and performed better than more elaborate measures such as buffer time (the difference between the 90th and 50th percentiles)
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57 segmentation between arterial and local roads versus highways and freeways resulted in a statistically significant difference in coefficients (at least for the nonwork travel purpose)
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58 powered by 0.6. This formulation will be further tested in the extended choice frameworks of mode and TOD choice.
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59 reasonable VOT at this stage, but rather to continue testing of more elaborate forms for generalized cost. For nonwork travel, the VOT values are more reasonable, although there was a significant difference between New York ($6/hour)
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60 HBW, the maximum VOT is only about 20% higher than for very short trips. • It is important to account for the main land use and density effects in the mode choice framework to ensure a reasonable background for analysis of LOS impacts and to separate these effects from the pure effects of travel time, cost, and reliability.
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61 right (negative) sign on average travel time, cost, and travel time reliability measures.
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62 Impact of Joint Travel and Car Occupancy Several alternative specifications were tried with both the New York and Seattle data in order to capture the best cost-sharing coefficient statistically. They included cost scaling by the powered occupancy as well as an occupancy-specific cost coefficient.
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63 framework to ensure a reasonable background for the analysis of LOS impacts and to separate these effects from the pure effects of travel time, cost, and reliability. In the New York region, the primary effects were found by segmenting trips to and from Manhattan (strongly dominated by transit)
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64 A general form for the probabilistic model that returns the probability of activity duration is (3.27)
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65 generic across duration alternatives (bkt = bk) , while the variables are assumed to have the following form: (3.35)
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66 have to be estimated is in general fewer than the number of alternatives. This parsimonious structure, however, outperformed a model with a full set of (g × h)
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67 the same vein, the mode preference effects included in the mode choice models based on the Seattle RP data were once again included in the mode and TOD choice models, and the results were much the same. Income has several important impacts on joint choice of TOD and mode.
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68 Basic Specification, Segmentation, and Associated Value of Time The main conclusion that could be made at this stage was that for both the New York and Seattle models, the extension of the model to include a TOD choice dimension in addition to mode dimension did not violate the main impacts of LOS and other variables. In particular, all main LOS components previously substantiated for more limited frameworks of route-type choice and mode choice proved to be statistically significant, with the right sign, and mostly with a similar magnitude, in a more general choice context that included the TOD dimension.
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69 vein, for the Seattle models, the same tests were repeated that were done for the mode choice models that are reported above: segmenting the cost coefficient by income and vehicle occupancy as opposed to assuming the same power function that was adopted for the analyses on the New York data. Similar to the tests with the New York model, the results were virtually unchanged from what was found for the mode choice–only models.
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70 median) divided by distance, which has a significant negative coefficient.
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71 lanes. In the Seattle experiment, the free option could be on a different route, requiring a different travel distance; • The San Francisco experiment recruited people who made recent auto trips and parked downtown, and then presented hypothetical options with a cordon toll charged to enter the downtown area.
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72 VOT for work trips, the Seattle RP study found higher VOT for nonwork trips. Standard practice is to use much higher VOT for work trips than for nonwork trips, but such a result is rarely found in SP-based studies.
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73 unexpected delays will, on average, tend to be stronger for work trips than for leisure trips (although they may also be quite strong for specific types of nonwork trips)
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74 Shifting to Transit A number of different variables were used to represent the transit alternative, which is necessary to give respondents some clear idea of how attractive the transit alternatives would be for their particular trips. This includes travel time broken down into components, as well as transfers, frequency, and fare.
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75 RP analysis. Typically, SP samples only include the vehicle driver and not the other vehicle occupants, and it is not always clear to what extent respondents are answering only on their own behalf and to what extent they are answering for the entire traveling party, particularly with regard to sharing payment of tolls or other travel costs.
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76 moving later levels off after about 45 minutes (the bend in the curve is an artifact of the cubic function adopted)
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77 The estimation results, which are shown in Table 3.12, illustrate some important insights that can be gained by accounting for unobserved heterogeneity in travel time response. By capturing the distribution of individuals' VOT, the proportion of the population with a specific VOT can be determined.
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78 this larger variance is that more of the variance is captured by serial correlation. By not accounting for this serial correlation, VOT exhibits greater variance.
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79 captured. This is similar to the comparison between VOT for route-type choice only and the nested model, which captured both mode and route-type choice.
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80 direction has been less explored and represents a sound possible topic for future research. In the current report, the team further describes the approach based on accessibility measures derived from the lower-level tour and trip models that are immediately implementable with the highway utility (generalized cost)
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81 The composite travel impedance between zones, which can be referred to as an O-D accessibility measure, is calculated as a two-level logsum taken over the TOD periods and modes: ln exp (3.41)
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82 For escorting purpose (purpose = 4) , the size variable is set to the total population.
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83 is frequently chosen for work purpose, but it is practically not observed for the purposes of school trips or other trips. All nonwork purposes are aggregated for calculation of impedances, although they are separated with respect to size variables.
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84 Variable SOV HOV WT DT NM Work Travel Purpose SOV time (min) -0.03 na na na na HOV time (min)
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85 Table 3.15. Components and Coefficients of Mode Utilities (continued)
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86 Table 3.16. List of Mode and TOD Choice Logsums Impedance Accessibility from the Given (Residential)
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87 Measure Size Variable Impedance Measure Model in Which AppliedNo. Token No.
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88 Measure Size Variable Impedance Measure Model in Which AppliedNo. Token No.
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